NASA Astrophysics Data System (ADS)
Antoine, Jean-Pierre; Vandergheynst, Pierre; Bouyoucef, Karim; Murenzi, Romain
1995-06-01
Both in 1D (signal analysis) and 2D (image processing), the wavelet transform (WT) has become by now a standard tool. Although the discrete version, based on multiresolution analysis, is probably better known, the continous WT (CWT) plays a crucial role for the detection and analysis of particular features in a signal, and we will focus here on the latter. In 2D however, one faces a practical problem. Indeed, the full parameter space of the wavelet transform of an image is 4D. It yields a representation of the image in position parameters (range and perception angle), as well as scale and anisotropy angle. The real challenge is to compute and visualize the full continuous wavelet transform in all four variables--obviously a demanding task. Thus, in order to obtain a manageable tool, some of the variables must be frozen. In other words, one must limit oneself to sections of the parameter space, usually 2D or 3D. For 2D sections, two variables are fixed and the transform is viewed as a function of the two remaing ones, and similarly for 3D sections. Among the six possible 2D sections, two play a privileged role. They yield respectively the position representation, which is the standard one, and the scale-angle representation, which has been proposed and studied systematically by two of us in a number of works. In this paper we will review these results and investigate the four remaining 2D representations. We will also make some comments on possible applications of 3D sections. The most spectacular property of the CWT is its ability at detecting discontinuities in a signal. In an image, this means in particular the sharp boundary between two regions of different luminosity, that is, a contour or an edge. Even more prominent in the transform are the corners of a given contour, for instance the contour of a letter. In a second part, we will exploit this property of the CWT and describe how one may design an algorithm for automatic character recognition (here we
A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform
Tang, Hui; Tong, Dan; Dong Bao, Xu; Dillenseger, Jean-Louis
2015-04-15
Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.
A parallel splitting wavelet method for 2D conservation laws
NASA Astrophysics Data System (ADS)
Schmidt, Alex A.; Kozakevicius, Alice J.; Jakobsson, Stefan
2016-06-01
The current work presents a parallel formulation using the MPI protocol for an adaptive high order finite difference scheme to solve 2D conservation laws. Adaptivity is achieved at each time iteration by the application of an interpolating wavelet transform in each space dimension. High order approximations for the numerical fluxes are computed by ENO and WENO schemes. Since time evolution is made by a TVD Runge-Kutta space splitting scheme, the problem is naturally suitable for parallelization. Numerical simulations and speedup results are presented for Euler equations in gas dynamics problems.
Applications of a fast, continuous wavelet transform
Dress, W.B.
1997-02-01
A fast, continuous, wavelet transform, based on Shannon`s sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon`s sampling theorem lets us view the Fourier transform of the data set as a continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time- domain sampling of the signal under analysis. Computational cost and nonorthogonality aside, the inherent flexibility and shift invariance of the frequency-space wavelets has advantages. The method has been applied to forensic audio reconstruction speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants` heart beats. Audio reconstruction is aided by selection of desired regions in the 2-D representation of the magnitude of the transformed signal. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass-spring system (e.g., a vehicle) by an occupants beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, features such as the glottal closing rate and word and phrase segmentation may be extracted from voice data.
Compression of echocardiographic scan line data using wavelet packet transform
NASA Technical Reports Server (NTRS)
Hang, X.; Greenberg, N. L.; Qin, J.; Thomas, J. D.
2001-01-01
An efficient compression strategy is indispensable for digital echocardiography. Previous work has suggested improved results utilizing wavelet transforms in the compression of 2D echocardiographic images. Set partitioning in hierarchical trees (SPIHT) was modified to compress echocardiographic scanline data based on the wavelet packet transform. A compression ratio of at least 94:1 resulted in preserved image quality.
Applications of a fast continuous wavelet transform
NASA Astrophysics Data System (ADS)
Dress, William B.
1997-04-01
A fast, continuous, wavelet transform, justified by appealing to Shannon's sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and from the standard treatment of the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon's sampling theorem lets us view the Fourier transform of the data set as representing the continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time-domain sampling of the signal under analysis. Although more computationally costly and not represented by an orthogonal basis, the inherent flexibility and shift invariance of the frequency-space wavelets are advantageous for certain applications. The method has been applied to forensic audio reconstruction, speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants' heart beats. Audio reconstruction is aided by selection of desired regions in the 2D representation of the magnitude of the transformed signals. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass- spring system by an occupant's beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, different features may be extracted from voice
A Mellin transform approach to wavelet analysis
NASA Astrophysics Data System (ADS)
Alotta, Gioacchino; Di Paola, Mario; Failla, Giuseppe
2015-11-01
The paper proposes a fractional calculus approach to continuous wavelet analysis. Upon introducing a Mellin transform expression of the mother wavelet, it is shown that the wavelet transform of an arbitrary function f(t) can be given a fractional representation involving a suitable number of Riesz integrals of f(t), and corresponding fractional moments of the mother wavelet. This result serves as a basis for an original approach to wavelet analysis of linear systems under arbitrary excitations. In particular, using the proposed fractional representation for the wavelet transform of the excitation, it is found that the wavelet transform of the response can readily be computed by a Mellin transform expression, with fractional moments obtained from a set of algebraic equations whose coefficient matrix applies for any scale a of the wavelet transform. Robustness and computationally efficiency of the proposed approach are shown in the paper.
Staring 2-D hadamard transform spectral imager
Gentry, Stephen M.; Wehlburg, Christine M.; Wehlburg, Joseph C.; Smith, Mark W.; Smith, Jody L.
2006-02-07
A staring imaging system inputs a 2D spatial image containing multi-frequency spectral information. This image is encoded in one dimension of the image with a cyclic Hadamarid S-matrix. The resulting image is detecting with a spatial 2D detector; and a computer applies a Hadamard transform to recover the encoded image.
Optical wavelet transform for fingerprint identification
NASA Astrophysics Data System (ADS)
MacDonald, Robert P.; Rogers, Steven K.; Burns, Thomas J.; Fielding, Kenneth H.; Warhola, Gregory T.; Ruck, Dennis W.
1994-03-01
The Federal Bureau of Investigation (FBI) has recently sanctioned a wavelet fingerprint image compression algorithm developed for reducing storage requirements of digitized fingerprints. This research implements an optical wavelet transform of a fingerprint image, as the first step in an optical fingerprint identification process. Wavelet filters are created from computer- generated holograms of biorthogonal wavelets, the same wavelets implemented in the FBI algorithm. Using a detour phase holographic technique, a complex binary filter mask is created with both symmetry and linear phase. The wavelet transform is implemented with continuous shift using an optical correlation between binarized fingerprints written on a Magneto-Optic Spatial Light Modulator and the biorthogonal wavelet filters. A telescopic lens combination scales the transformed fingerprint onto the filters, providing a means of adjusting the biorthogonal wavelet filter dilation continuously. The wavelet transformed fingerprint is then applied to an optical fingerprint identification process. Comparison between normal fingerprints and wavelet transformed fingerprints shows improvement in the optical identification process, in terms of rotational invariance.
Entangled Husimi Distribution and Complex Wavelet Transformation
NASA Astrophysics Data System (ADS)
Hu, Li-Yun; Fan, Hong-Yi
2010-05-01
Similar in spirit to the preceding work (Int. J. Theor. Phys. 48:1539, 2009) where the relationship between wavelet transformation and Husimi distribution function is revealed, we study this kind of relationship to the entangled case. We find that the optical complex wavelet transformation can be used to study the entangled Husimi distribution function in phase space theory of quantum optics. We prove that, up to a Gaussian function, the entangled Husimi distribution function of a two-mode quantum state | ψ> is just the modulus square of the complex wavelet transform of e^{-\\vert η \\vert 2/2} with ψ( η) being the mother wavelet.
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.
The Wavelet Element Method. Part 2; Realization and Additional Features in 2D and 3D
NASA Technical Reports Server (NTRS)
Canuto, Claudio; Tabacco, Anita; Urban, Karsten
1998-01-01
The Wavelet Element Method (WEM) provides a construction of multiresolution systems and biorthogonal wavelets on fairly general domains. These are split into subdomains that are mapped to a single reference hypercube. Tensor products of scaling functions and wavelets defined on the unit interval are used on the reference domain. By introducing appropriate matching conditions across the interelement boundaries, a globally continuous biorthogonal wavelet basis on the general domain is obtained. This construction does not uniquely define the basis functions but rather leaves some freedom for fulfilling additional features. In this paper we detail the general construction principle of the WEM to the 1D, 2D and 3D cases. We address additional features such as symmetry, vanishing moments and minimal support of the wavelet functions in each particular dimension. The construction is illustrated by using biorthogonal spline wavelets on the interval.
The X-Ray Transform Projection of 3D Mother Wavelet Function
Yang, Xiangyu; Guo, Jiqiang; Lu, Li; Zeng, Li
2013-01-01
As we all know, any practical computed tomography (CT) projection data more or less contains noises. Hence, it will be inconvenient for the postprocessing of a reconstructed 3D image even when the noise in the projection data is white. The reason is that the noise in the reconstructed image may be nonwhite. X-ray transform can be applied to the three dimensional (3D) CT, depicting the relationship between material density and ray projection. In this paper, nontensor product relationship between the two dimensional (2D) mother wavelet and 3D mother wavelet is obtained by taking X-ray transform projection of 3D mother wavelet. We proved that the projection of the 3D mother wavelet is a 2D mother wavelet if the 3D mother wavelet satisfies certain conditions. So, the 3D wavelet transform of a 3D image can be implemented by the 2D wavelet transform of its X-ray transform projection and it will contribute to the reduction complexity and computation time during image processing. What is more, it can also avoid noise transfer and amplification during the processing of CT image reconstruction. PMID:24376470
Dual tree fractional quaternion wavelet transform for disparity estimation.
Kumar, Sanoj; Kumar, Sanjeev; Sukavanam, Nagarajan; Raman, Balasubramanian
2014-03-01
This paper proposes a novel phase based approach for computing disparity as the optical flow from the given pair of consecutive images. A new dual tree fractional quaternion wavelet transform (FrQWT) is proposed by defining the 2D Fourier spectrum upto a single quadrant. In the proposed FrQWT, each quaternion wavelet consists of a real part (a real DWT wavelet) and three imaginary parts that are organized according to the quaternion algebra. First two FrQWT phases encode the shifts of image features in the absolute horizontal and vertical coordinate system, while the third phase has the texture information. The FrQWT allowed a multi-scale framework for calculating and adjusting local disparities and executing phase unwrapping from coarse to fine scales with linear computational efficiency. PMID:24388356
Review of wavelet transforms for pattern recognitions
NASA Astrophysics Data System (ADS)
Szu, Harold H.
1996-03-01
After relating the adaptive wavelet transform to the human visual and hearing systems, we exploit the synergism between such a smart sensor processing with brain-style neural network computing. The freedom of choosing an appropriate kernel of a linear transform, which is given to us by the recent mathematical foundation of the wavelet transform, is exploited fully and is generally called the adaptive wavelet transform (WT). However, there are several levels of adaptivity: (1) optimum coefficients: adjustable transform coefficients chosen with respect to a fixed mother kernel for better invariant signal representation, (2) super-mother: grouping different scales of daughter wavelets of same or different mother wavelets at different shift location into a new family called a superposition mother kernel for better speech signal classification, (3) variational calculus to determine ab initio a constraint optimization mother for a specific task. The tradeoff between the mathematical rigor of the complete orthonormality and the speed of order (N) with the adaptive flexibility is finally up to the user's needs. Then, to illustrate (1), a new invariant optoelectronic architecture of a wedge- shape filter in the WT domain is given for scale-invariant signal classification by neural networks.
Medical image compression by using three-dimensional wavelet transformation.
Wang, J; Huang, K
1996-01-01
This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance. PMID:18215935
Information retrieval system utilizing wavelet transform
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.
Cosmic Ray elimination using the Wavelet Transform
NASA Astrophysics Data System (ADS)
Orozco-Aguilera, M. T.; Cruz, J.; Altamirano, L.; Serrano, A.
2009-11-01
In this work, we present a method for the automatic cosmic ray elimination in a single CCD exposure using the Wavelet Transform. The proposed method can eliminate cosmic rays of any shape or size. With this method we can eliminate over 95% of cosmic rays in a spectral image.
Wavelet transforms as solutions of partial differential equations
Zweig, G.
1997-10-01
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 continuous 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.
Multispectral multisensor image fusion using wavelet transforms
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.
Adaptive directional wavelet transform based on directional prefiltering.
Tanaka, Yuichi; Hasegawa, Madoka; Kato, Shigeo; Ikehara, Masaaki; Nguyen, Truong Q
2010-04-01
This paper proposes an efficient approach for adaptive directional wavelet transform (WT) based on directional prefiltering. Although the adaptive directional WT is able to transform an image along diagonal orientations as well as traditional horizontal and vertical directions, it sacrifices computation speed for good image coding performance. We present two efficient methods to find the best transform directions by prefiltering using 2-D filter bank or 1-D directional WT along two fixed directions. The proposed direction calculation methods achieve comparable image coding performance comparing to the conventional one with less complexity. Furthermore, transform direction data of the proposed method can be used for content-based image retrieval to increase retrieval ratio. PMID:20028625
Modelling Elastic Media With Arbitrary Shapes Using the Wavelet Transform
NASA Astrophysics Data System (ADS)
Rosa, J. W.; Cardoso, F. A.; Rosa, J. W.; Aki, K.
2004-12-01
We extend the new method proposed by Rosa et al. (2001) for the study of elastic bodies with complete arbitrary shapes. The method was originally developed for modelling 2-D elastic media with the application of the wavelet transform, and was extended to cases where discontinuities simulated geologic faults between two different elastic media. In addition to extending the method for the study of bodies with complete arbitrary shapes, we also test new transforms with the objective of making the related matrices more compact, which are also applied to the most general case of the method. The basic method consists of the discretization of the polynomial expansion for the boundary conditions of the 2-D problem involving the stress and strain relations for the media. This parameterization leads to a system of linear equations that should be solved for the determination of the expansion coefficients, which are the model parameters, and their determination leads to the solution of the problem. Despite the fact that the media we studied originally were 2-D bodies, the result of the application of this new method can be viewed as an approximate solution to some specific 3-D problems. Among the motivations for developing this method are possible geological applications (that is, the study of tectonic plates and geologic faults) and simulations of the elastic behaviour of materials in several other fields of science. The wavelet transform is applied with two main objectives, namely to decrease the error related to the truncation of the polynomial expansion and to make the system of linear equations more compact for computation. Having validated this method for the original 2-D elastic media, we plan that this extension to elastic bodies with complete arbitrary shapes will enable it to be even more attractive for modelling real media. Reference Rosa, J. W. C., F. A. C. M. Cardoso, K. Aki, H. S. Malvar, F. A. V. Artola, and J. W. C. Rosa, Modelling elastic media with the
2D hexagonal quaternion Fourier transform in color image processing
NASA Astrophysics Data System (ADS)
Grigoryan, Artyom M.; Agaian, Sos S.
2016-05-01
In this paper, we present a novel concept of the quaternion discrete Fourier transform on the two-dimensional hexagonal lattice, which we call the two-dimensional hexagonal quaternion discrete Fourier transform (2-D HQDFT). The concept of the right-side 2D HQDFT is described and the left-side 2-D HQDFT is similarly considered. To calculate the transform, the image on the hexagonal lattice is described in the tensor representation when the image is presented by a set of 1-D signals, or splitting-signals which can be separately processed in the frequency domain. The 2-D HQDFT can be calculated by a set of 1-D quaternion discrete Fourier transforms (QDFT) of the splitting-signals.
Spike detection using the continuous wavelet transform.
Nenadic, Zoran; Burdick, Joel W
2005-01-01
This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution. PMID:15651566
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.
Continuous wavelet transform in quantum field theory
NASA Astrophysics Data System (ADS)
Altaisky, M. V.; Kaputkina, N. E.
2013-07-01
We describe the application of the continuous wavelet transform to calculation of the Green functions in quantum field theory: scalar ϕ4 theory, quantum electrodynamics, and quantum chromodynamics. The method of continuous wavelet transform in quantum field theory, presented by Altaisky [Phys. Rev. D 81, 125003 (2010)] for the scalar ϕ4 theory, consists in substitution of the local fields ϕ(x) by those dependent on both the position x and the resolution a. The substitution of the action S[ϕ(x)] by the action S[ϕa(x)] makes the local theory into a nonlocal one and implies the causality conditions related to the scale a, the region causality [J. D. Christensen and L. Crane, J. Math. Phys. (N.Y.) 46, 122502 (2005)]. These conditions make the Green functions G(x1,a1,…,xn,an)=⟨ϕa1(x1)…ϕan(xn)⟩ finite for any given set of regions by means of an effective cutoff scale A=min(a1,…,an).
Efficient VLSI architecture for multi-dimensional discrete wavelet transform
NASA Astrophysics Data System (ADS)
Xiong, Cheng-Yi; Tian, Jin-Wen; Liu, Jian
2005-10-01
Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2m subbands transforms in lifting-based m-D DWT is explored, which increases efficiently the throughput rate of separable m-D DWT. The proposed architecture is composed of m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m input samples per clock cycle, and generate 2m subbands coefficients synchronously. The total time of computing one level of decomposition for a 2-D image (3-D image sequence) of size N2 (MN2) is approximately N2/4 (MN2/8) intra- clock cycles (ccs). An efficient line-based architecture framework for both 2D+t and t+2D 3-D DWT is first proposed. Compared with the similar works reported in previous literature, the proposed architecture has good performance in terms of production of computation time and hardware cost. The proposed architecture is simple, regular, scalable and well suited for VLSI implementation.
Practical Algorithm For Computing The 2-D Arithmetic Fourier Transform
NASA Astrophysics Data System (ADS)
Reed, Irving S.; Choi, Y. Y.; Yu, Xiaoli
1989-05-01
Recently, Tufts and Sadasiv [10] exposed a method for computing the coefficients of a Fourier series of a periodic function using the Mobius inversion of series. They called this method of analysis the Arithmetic Fourier Transform(AFT). The advantage of the AFT over the FN 1' is that this method of Fourier analysis needs only addition operations except for multiplications by scale factors at one stage of the computation. The disadvantage of the AFT as they expressed it originally is that it could be used effectively only to compute finite Fourier coefficients of a real even function. To remedy this the AFT developed in [10] is extended in [11] to compute the Fourier coefficients of both the even and odd components of a periodic function. In this paper, the improved AFT [11] is extended to a two-dimensional(2-D) Arithmetic Fourier Transform for calculating the Fourier Transform of two-dimensional discrete signals. This new algorithm is based on both the number-theoretic method of Mobius inversion of double series and the complex conjugate property of Fourier coefficients. The advantage of this algorithm over the conventional 2-D FFT is that the corner-turning problem needed in a conventional 2-D Discrete Fourier Transform(DFT) can be avoided. Therefore, this new 2-D algorithm is readily suitable for VLSI implementation as a parallel architecture. Comparing the operations of 2-D AFT of a MxM 2-D data array with the conventional 2-D FFT, the number of multiplications is significantly reduced from (2log2M)M2 to (9/4)M2. Hence, this new algorithm is faster than the FFT algorithm. Finally, two simulation results of this new 2-D AFT algorithm for 2-D artificial and real images are given in this paper.
Improved Compression of Wavelet-Transformed Images
NASA Technical Reports Server (NTRS)
Kiely, Aaron; Klimesh, Matthew
2005-01-01
A recently developed data-compression method is an adaptive technique for coding quantized wavelet-transformed data, nominally as part of a complete image-data compressor. Unlike some other approaches, this method admits a simple implementation and does not rely on the use of large code tables. A common data compression approach, particularly for images, is to perform a wavelet transform on the input data, and then losslessly compress a quantized version of the wavelet-transformed data. Under this compression approach, it is common for the quantized data to include long sequences, or runs, of zeros. The new coding method uses prefixfree codes for the nonnegative integers as part of an adaptive algorithm for compressing the quantized wavelet-transformed data by run-length coding. In the form of run-length coding used here, the data sequence to be encoded is parsed into strings consisting of some number (possibly 0) of zeros, followed by a nonzero value. The nonzero value and the length of the run of zeros are encoded. For a data stream that contains a sufficiently high frequency of zeros, this method is known to be more effective than using a single variable length code to encode each symbol. The specific prefix-free codes used are from two classes of variable-length codes: a class known as Golomb codes, and a class known as exponential-Golomb codes. The codes within each class are indexed by a single integer parameter. The present method uses exponential-Golomb codes for the lengths of the runs of zeros, and Golomb codes for the nonzero values. The code parameters within each code class are determined adaptively on the fly as compression proceeds, on the basis of statistics from previously encoded values. In particular, a simple adaptive method has been devised to select the parameter identifying the particular exponential-Golomb code to use. The method tracks the average number of bits used to encode recent runlengths, and takes the difference between this average
Wavelet transforms for electroencephalographic spike and seizure detection
NASA Astrophysics Data System (ADS)
Schiff, Steven J.; Milton, John G.
1993-11-01
The application of wavelet transforms (WT) to experimental data from the nervous system has been hindered by the lack of a straightforward method to handle noise. A noise reduction technique, developed recently for use in wavelet cluster analysis in cosmology and astronomy, is here adapted for electroencephalographic (EEG) time-series data. Noise is filtered using control surrogate data sets generated from randomized aspects of the original time-series. In this study, WT were applied to EEG data from human patients undergoing brain mapping with implanted subdural electrodes for the localization of epileptic seizure foci. EEG data in 1D were analyzed from individual electrodes, and 2D data from electrode grids. These techniques are a powerful means to identify epileptic spikes in such data, and offer a method to identity the onset and spatial extent of epileptic seizure foci. The method is readily applied to the detection of structure in stationary and non-stationary time-series from a variety of physical systems.
Medical image fusion by wavelet transform modulus maxima
NASA Astrophysics Data System (ADS)
Guihong, Qu; Dali, Zhang; Pingfan, Yan
2001-08-01
Medical image fusion has been used to derive useful information from multimodality medical image data. In this research, we propose a novel method for multimodality medical image fusion. Using wavelet transform, we achieved a fusion scheme. Afusion rule is proposed and used for calculating the wavelet transformation modulus maxima of input images at different bandwidths and levels. To evaluate the fusion result, a metric based on mutual information (MI) is presented for measuring fusion effect. The performances of other two methods of image fusion based on wavelet transform are briefly described for comparison. The experiment results demonstrate the effectiveness of the fusion scheme.
Coresident sensor fusion and compression using the wavelet transform
Yocky, D.A.
1996-03-11
Imagery from coresident sensor platforms, such as unmanned aerial vehicles, can be combined using, multiresolution decomposition of the sensor images by means of the two-dimensional wavelet transform. The wavelet approach uses the combination of spatial/spectral information at multiple scales to create a fused image. This can be done in both an ad hoc or model-based approach. We compare results from commercial ``fusion`` software and the ad hoc, wavelet approach. Results show the wavelet approach outperforms the commercial algorithms and also supports efficient compression of the fused image.
Complex Wavelet Transform of the Two-mode Quantum States
NASA Astrophysics Data System (ADS)
Song, Jun; Zhou, Jun; Yuan, Hao; He, Rui; Fan, Hong-Yi
2016-08-01
By employing the bipartite entangled state representation and the technique of integration within an ordered product of operators, the classical complex wavelet transform of a complex signal function can be recast to a matrix element of the squeezing-displacing operator U 2( μ, σ) between the mother wavelet vector < ψ| and the two-mode quantum state vector | f> to be transformed. < ψ| U 2( μ, σ)| f> can be considered as the spectrum for analyzing the two-mode quantum state | f>. In this way, for some typical two-mode quantum states, such as two-mode coherent state and two-mode Fock state, we derive the complex wavelet transform spectrum and carry out the numerical calculation. This kind of wavelet-transform spectrum can be used to recognize quantum states.
Wavelet characterization of 2D turbulence and intermittency in magnetized electron plasmas
NASA Astrophysics Data System (ADS)
Romé, M.; Chen, S.; Maero, G.
2016-06-01
A study of the free relaxation of turbulence in a two-dimensional (2D) flow is presented, with a focus on the role of the initial vorticity conditions. Exploiting a well-known analogy with 2D inviscid incompressible fluids, the system investigated here is a magnetized pure electron plasma. The dynamics of this system are simulated by means of a 2D particle-in-cell code, starting from different spiral density (vorticity) distributions. A wavelet multiresolution analysis is adopted, which allows the coherent and incoherent parts of the flow to be separated. Comparison of the turbulent evolution in the different cases is based on the investigation of the time evolution of statistical properties, including the probability distribution functions and structure functions of the vorticity increments. It is also based on an analysis of the enstrophy evolution and its spectrum for the two components. In particular, while the statistical features assess the degree of flow intermittency, spectral analysis allows us not only to estimate the time required to reach a state of fully developed turbulence, but also estimate its dependence on the thickness of the initial spiral density distribution, accurately tracking the dynamics of both the coherent structures and the turbulent background. The results are compared with those relevant to annular initial vorticity distributions (Chen et al 2015 J. Plasma Phys. 81 495810511).
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
Analysis and removing noise from speech using wavelet transform
NASA Astrophysics Data System (ADS)
Tomala, Karel; Voznak, Miroslav; Partila, Pavol; Rezac, Filip; Safarik, Jakub
2013-05-01
The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.
An Adaptive Digital Image Watermarking Algorithm Based on Morphological Haar Wavelet Transform
NASA Astrophysics Data System (ADS)
Huang, Xiaosheng; Zhao, Sujuan
At present, much more of the wavelet-based digital watermarking algorithms are based on linear wavelet transform and fewer on non-linear wavelet transform. In this paper, we propose an adaptive digital image watermarking algorithm based on non-linear wavelet transform--Morphological Haar Wavelet Transform. In the algorithm, the original image and the watermark image are decomposed with multi-scale morphological wavelet transform respectively. Then the watermark information is adaptively embedded into the original image in different resolutions, combining the features of Human Visual System (HVS). The experimental results show that our method is more robust and effective than the ordinary wavelet transform algorithms.
Topology-Preserving Rigid Transformation of 2D Digital Images.
Ngo, Phuc; Passat, Nicolas; Kenmochi, Yukiko; Talbot, Hugues
2014-02-01
We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.
Topology-Preserving Rigid Transformation of 2D Digital Images.
Ngo, Phuc; Passat, Nicolas; Kenmochi, Yukiko; Talbot, Hugues
2014-02-01
We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping. PMID:26270925
Early detection of rogue waves by the wavelet transforms
NASA Astrophysics Data System (ADS)
Bayındır, Cihan
2016-01-01
We discuss the possible advantages of using the wavelet transform over the Fourier transform for the early detection of rogue waves. We show that the triangular wavelet spectra of the rogue waves can be detected at early stages of the development of rogue waves in a chaotic wave field. Compared to the Fourier spectra, the wavelet spectra are capable of detecting not only the emergence of a rogue wave but also its possible spatial (or temporal) location. Due to this fact, wavelet transform is also capable of predicting the characteristic distances between successive rogue waves. Therefore multiple simultaneous breaking of the successive rogue waves on ships or on the offshore structures can be predicted and avoided by smart designs and operations.
Tree-structured wavelet transform signature for classification of melanoma
NASA Astrophysics Data System (ADS)
Patwardhan, Sachin V.; Dhawan, Atam P.; Relue, Patricia A.
2002-05-01
The purpose of this work is to evaluate the use of a wavelet transform based tree structure in classifying skin lesion images in to melanoma and dysplastic nevus based on the spatial/frequency information. The classification is done using the wavelet transform tree structure analysis. Development of the tree structure in the proposed method uses energy ratio thresholds obtained from a statistical analysis of the coefficients in the wavelet domain. The method is used to obtain a tree structure signature of melanoma and dysplastic nevus, which is then used to classify the data set in to the two classes. Images are classified by using a semantic comparison of the wavelet transform tree structure signatures. Results show that the proposed method is effective and simple for classification based on spatial/frequency information, which also includes the textural information.
Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
NASA Astrophysics Data System (ADS)
Andreão, Rodrigo Varejão; Boudy, Jérôme
2006-12-01
This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification.
Soares, João V B; Leandro, Jorge J G; Cesar Júnior, Roberto M; Jelinek, Herbert F; Cree, Michael J
2006-09-01
We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.
ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform
NASA Astrophysics Data System (ADS)
Kaur, Inderbir; Rajni, Rajni; Marwaha, Anupma
2016-06-01
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.
Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform
Zhou, Jian; Senhadji, Lotfi; Coatrieux, Jean-Louis; Luo, Limin
2009-01-01
The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior. PMID:21869846
Doppler radar fall activity detection using the wavelet transform.
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.
Doppler radar fall activity detection using the wavelet transform.
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. PMID:25376033
Remote sensing image fusion via wavelet transform and sparse representation
NASA Astrophysics Data System (ADS)
Cheng, Jian; Liu, Haijun; Liu, Ting; Wang, Feng; Li, Hongsheng
2015-06-01
In this paper, we propose a remote sensing image fusion method which combines the wavelet transform and sparse representation to obtain fusion images with high spectral resolution and high spatial resolution. Firstly, intensity-hue-saturation (IHS) transform is applied to Multi-Spectral (MS) images. Then, wavelet transform is used to the intensity component of MS images and the Panchromatic (Pan) image to construct the multi-scale representation respectively. With the multi-scale representation, different fusion strategies are taken on the low-frequency and the high-frequency sub-images. Sparse representation with training dictionary is introduced into the low-frequency sub-image fusion. The fusion rule for the sparse representation coefficients of the low-frequency sub-images is defined by the spatial frequency maximum. For high-frequency sub-images with prolific detail information, the fusion rule is established by the images information fusion measurement indicator. Finally, the fused results are obtained through inverse wavelet transform and inverse IHS transform. The wavelet transform has the ability to extract the spectral information and the global spatial details from the original pairwise images, while sparse representation can extract the local structures of images effectively. Therefore, our proposed fusion method can well preserve the spectral information and the spatial detail information of the original images. The experimental results on the remote sensing images have demonstrated that our proposed method could well maintain the spectral characteristics of fusion images with a high spatial resolution.
Time-frequency analysis with the continuous wavelet transform
NASA Astrophysics Data System (ADS)
Lang, W. Christopher; Forinash, Kyle
1998-09-01
The continuous wavelet transform can be used to produce spectrograms which show the frequency content of sounds (or other signals) as a function of time in a manner analogous to sheet music. While this technique is commonly used in the engineering community for signal analysis, the physics community has, in our opinion, remained relatively unaware of this development. Indeed, some find the very notion of frequency as a function of time troublesome. Here spectrograms will be displayed for familiar sounds whose pitches change with time, demonstrating the usefulness of the continuous wavelet transform.
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.
Detection of Orthoimage Mosaicking Seamlines by Means of Wavelet Transform
NASA Astrophysics Data System (ADS)
Pyka, K.
2016-06-01
The detection of orthoimage mosaicking seamlines by means of wavelet transform was examined. Radiometric alignment was omitted, giving priority to the issue of seamlines which bypass locations where there is a parallax between orthoimages. The importance of this issue is particularly relevant for images with very high resolution. In order to create a barrier image between orthoimages, the redundant wavelet transform variant known as MODWT-MRA was used. While more computationally complex than the frequently used DWT, it enables very good multiresolution edge detection. An IT prototype was developed on the basis of the described concept, and several cases of seamline detection were tested on the basis of data with a resolution of 10 cm to 1 m. The correct seamline location was obtained for each test case. This result opens the door to future expansion of the radiometric alignment method, which is also based on wavelets.
Stationary wavelet transform for under-sampled MRI reconstruction.
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.
NASA Astrophysics Data System (ADS)
Wang, Chun-Hsiung; Hsu, Kuan-Yu; Lee, Chih-Kung
2016-03-01
A real-time three-dimensional surface profile metrology system was implemented by integrating Fourier Transform (FT) based algorithms to convert interference intensity fringes to wrapped frequency phase maps and then to unwrapped phase maps. The revival of this field can find its roots in recognizing the development of high-resolution high-speed CCD/CMOS over the years. Two-dimensional Continuous Wavelet Transform (2D-CWT), which possesses the ability to construct daughter wavelets of good time and frequency localization according to different fringes conditions from a characteristic mother wavelet, was implemented with an attempt to reduce redundant fitting process of ordinary Short Time Fourier Transform (STFT), also known as Windowed Fourier Transform (WFT), and therefore to accelerate the FT-related algorithms needed. Implemented with the efficient wavelet construction process by using 2D-CWT, Electronic Speckle Pattern Interferometer (ESPI) was adopted to take advantage of this new process. Different from using several phase shifting steps before to solve the direction ambiguity, which takes time to capture multiple intensity maps during measurement, the phase maps needed were retrieved from a single frame interference fringes. It is to be noted that this one-image interference fringe was captured by having a pre-introduced spatial carrier frequency embedded within the experimental setup so as to remove the directional ambiguity. 2D-CWT dealing with different signal-to-noise ratios was also designed by selecting wavelet parameters properly, which is expected to achieve higher accuracy and faster processing speed. For phase unwrapping, Poisson's equation with Neumann boundary condition was solved by using FFT. The benefit of using 2D-CWTs with different wavelets as compared to WFT was demonstrated experimentally.
Sparse imaging of cortical electrical current densities via wavelet transforms
NASA Astrophysics Data System (ADS)
Liao, Ke; Zhu, Min; Ding, Lei; Valette, Sébastien; Zhang, Wenbo; Dickens, Deanna
2012-11-01
While the cerebral cortex in the human brain is of functional importance, functions defined on this structure are difficult to analyze spatially due to its highly convoluted irregular geometry. This study developed a novel L1-norm regularization method using a newly proposed multi-resolution face-based wavelet method to estimate cortical electrical activities in electroencephalography (EEG) and magnetoencephalography (MEG) inverse problems. The proposed wavelets were developed based on multi-resolution models built from irregular cortical surface meshes, which were realized in this study too. The multi-resolution wavelet analysis was used to seek sparse representation of cortical current densities in transformed domains, which was expected due to the compressibility of wavelets, and evaluated using Monte Carlo simulations. The EEG/MEG inverse problems were solved with the use of the novel L1-norm regularization method exploring the sparseness in the wavelet domain. The inverse solutions obtained from the new method using MEG data were evaluated by Monte Carlo simulations too. The present results indicated that cortical current densities could be efficiently compressed using the proposed face-based wavelet method, which exhibited better performance than the vertex-based wavelet method. In both simulations and auditory experimental data analysis, the proposed L1-norm regularization method showed better source detection accuracy and less estimation errors than other two classic methods, i.e. weighted minimum norm (wMNE) and cortical low-resolution electromagnetic tomography (cLORETA). This study suggests that the L1-norm regularization method with the use of face-based wavelets is a promising tool for studying functional activations of the human brain.
NASA Astrophysics Data System (ADS)
Sato, Haruo; Fehler, Michael C.
2016-10-01
The envelope broadening and the peak delay of the S-wavelet of a small earthquake with increasing travel distance are results of scattering by random velocity inhomogeneities in the earth medium. As a simple mathematical model, Sato proposed a new stochastic synthesis of the scalar wavelet envelope in 3-D von Kármán type random media when the centre wavenumber of the wavelet is in the power-law spectral range of the random velocity fluctuation. The essential idea is to split the random medium spectrum into two components using the centre wavenumber as a reference: the long-scale (low-wavenumber spectral) component produces the peak delay and the envelope broadening by multiple scattering around the forward direction; the short-scale (high-wavenumber spectral) component attenuates wave amplitude by wide angle scattering. The former is calculated by the Markov approximation based on the parabolic approximation and the latter is calculated by the Born approximation. Here, we extend the theory for the envelope synthesis of a wavelet in 2-D random media, which makes it easy to compare with finite difference (FD) simulation results. The synthetic wavelet envelope is analytically written by using the random medium parameters in the angular frequency domain. For the case that the power spectral density function of the random velocity fluctuation has a steep roll-off at large wavenumbers, the envelope broadening is small and frequency independent, and scattering attenuation is weak. For the case of a small roll-off, however, the envelope broadening is large and increases with frequency, and the scattering attenuation is strong and increases with frequency. As a preliminary study, we compare synthetic wavelet envelopes with the average of FD simulation wavelet envelopes in 50 synthesized random media, which are characterized by the RMS fractional velocity fluctuation ε = 0.05, correlation scale a = 5 km and the background wave velocity V0 = 4 km s-1. We use the radiation
NASA Astrophysics Data System (ADS)
Sato, Haruo; Fehler, Michael C.
2016-07-01
The envelope broadening and the peak delay of the S-wavelet of a small earthquake with increasing travel distance are results of scattering by random velocity inhomogeneities in the earth medium. As a simple mathematical model, Sato (2016) proposed a new stochastic synthesis of the scalar wavelet envelope in 3-D von Kármán type random media when the center wavenumber of the wavelet is in the power-law spectral range of the random velocity fluctuation. The essential idea is to split the random medium spectrum into two components using the center wavenumber as a reference: the long-scale (low-wavenumber spectral) component produces the peak delay and the envelope broadening by multiple scattering around the forward direction; the short-scale (high-wavenumber spectral) component attenuates wave amplitude by wide angle scattering. The former is calculated by the Markov approximation based on the parabolic approximation and the latter is calculated by the Born approximation. Here, we extend the theory for the envelope synthesis of a wavelet in 2-D random media, which makes it easy to compare with finite difference (FD) simulation results. The synthetic wavelet envelope is analytically written by using the random medium parameters in the angular frequency domain. For the case that the power spectral density function of the random velocity fluctuation has a steep roll-off at large wavenumbers, the envelope broadening is small and frequency independent, and scattering attenuation is weak. For the case of a small roll-off, however, the envelope broadening is large and increases with frequency, and the scattering attenuation is strong and increases with frequency. As a preliminary study, we compare synthetic wavelet envelopes with the average of FD simulation wavelet envelopes in 50 synthesized random media, which are characterized by the RMS fractional velocity fluctuation ε=0.05, correlation scale a =5 km and the background wave velocity V0=4 km/s. We use the radiation
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.
Wavelet steerability and the higher-order Riesz transform.
Unser, Michael; Van De Ville, Dimitri
2010-03-01
Our main goal in this paper is to set the foundations of a general continuous-domain framework for designing steerable, reversible signal transformations (a.k.a. frames) in multiple dimensions ( d >or= 2). To that end, we introduce a self-reversible, Nth-order extension of the Riesz transform. We prove that this generalized transform has the following remarkable properties: shift-invariance, scale-invariance, inner-product preservation, and steerability. The pleasing consequence is that the transform maps any primary wavelet frame (or basis) of [Formula: see text] into another "steerable" wavelet frame, while preserving the frame bounds. The concept provides a functional counterpart to Simoncelli's steerable pyramid whose construction was primarily based on filterbank design. The proposed mechanism allows for the specification of wavelets with any order of steerability in any number of dimensions; it also yields a perfect reconstruction filterbank algorithm. We illustrate the method with the design of a novel family of multidimensional Riesz-Laplace wavelets that essentially behave like the N th-order partial derivatives of an isotropic Gaussian kernel. PMID:20031498
A Secret Image Sharing Method Using Integer Wavelet Transform
NASA Astrophysics Data System (ADS)
Huang, Chin-Pan; Li, Ching-Chung
2007-12-01
A new image sharing method, based on the reversible integer-to-integer (ITI) wavelet transform and Shamir's [InlineEquation not available: see fulltext.] threshold scheme is presented, that provides highly compact shadows for real-time progressive transmission. This method, working in the wavelet domain, processes the transform coefficients in each subband, divides each of the resulting combination coefficients into [InlineEquation not available: see fulltext.] shadows, and allows recovery of the complete secret image by using any [InlineEquation not available: see fulltext.] or more shadows [InlineEquation not available: see fulltext.]. We take advantages of properties of the wavelet transform multiresolution representation, such as coefficient magnitude decay and excellent energy compaction, to design combination procedures for the transform coefficients and processing sequences in wavelet subbands such that small shadows for real-time progressive transmission are obtained. Experimental results demonstrate that the proposed method yields small shadow images and has the capabilities of real-time progressive transmission and perfect reconstruction of secret images.
Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram
2015-01-01
It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data. PMID:26381141
Facial Feature Extraction Based on Wavelet Transform
NASA Astrophysics Data System (ADS)
Hung, Nguyen Viet
Facial feature extraction is one of the most important processes in face recognition, expression recognition and face detection. The aims of facial feature extraction are eye location, shape of eyes, eye brow, mouth, head boundary, face boundary, chin and so on. The purpose of this paper is to develop an automatic facial feature extraction system, which is able to identify the eye location, the detailed shape of eyes and mouth, chin and inner boundary from facial images. This system not only extracts the location information of the eyes, but also estimates four important points in each eye, which helps us to rebuild the eye shape. To model mouth shape, mouth extraction gives us both mouth location and two corners of mouth, top and bottom lips. From inner boundary we obtain and chin, we have face boundary. Based on wavelet features, we can reduce the noise from the input image and detect edge information. In order to extract eyes, mouth, inner boundary, we combine wavelet features and facial character to design these algorithms for finding midpoint, eye's coordinates, four important eye's points, mouth's coordinates, four important mouth's points, chin coordinate and then inner boundary. The developed system is tested on Yale Faces and Pedagogy student's faces.
Electroencephalography data analysis by using discrete wavelet packet transform
NASA Astrophysics Data System (ADS)
Karim, Samsul Ariffin Abdul; Ismail, Mohd Tahir; Hasan, Mohammad Khatim; Sulaiman, Jumat; Muthuvalu, Mohana Sundaram; Janier Josefina, B.
2015-05-01
Electroencephalography (EEG) is the electrical activity generated by the movement of neurons in the brain. It is categorized into delta waves, theta, alpha, beta and gamma. These waves exist in a different frequency band. This paper is a continuation of our previous research. EEG data will be decomposed using Discrete Wavelet Packet Transform (DWPT). Daubechies wavelets 10 (D10) will be used as the basic functions for research purposes. From the main results, it is clear that the DWPT able to characterize the EEG signal corresponding to each wave at a specific frequency. Furthermore, the numerical results obtained better than the results using DWT. Statistical analysis support our main findings.
Ganesan, Karthikeyan; Acharya, U. Rajendra; Chua, Chua Kuang; Min, Lim Choo; Abraham, Thomas K.
2014-01-01
Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis. In specific, wavelets are a popular choice for texture analysis of these images. Though discrete wavelets have been used extensively for this purpose, spherical wavelets have rarely been used for Computer-Aided Diagnosis (CAD) of breast cancer using mammograms. In this work, a comparison of the performance between the features of Discrete Wavelet Transform (DWT) and Spherical Wavelet Transform (SWT) based on the classification results of normal, benign and malignant stage was studied. Classification was performed using Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Nearest Mean Classifier (NMC), Support Vector Machines (SVM) and Parzen Classifier (ParzenC). We have obtained a maximum classification accuracy of 81.73% for DWT and 88.80% for SWT features using SVM classifier. PMID:24000991
Ganesan, Karthikeyan; Acharya, U Rajendra; Chua, Chua Kuang; Min, Lim Choo; Abraham, Thomas K
2014-12-01
Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis. In specific, wavelets are a popular choice for texture analysis of these images. Though discrete wavelets have been used extensively for this purpose, spherical wavelets have rarely been used for Computer-Aided Diagnosis (CAD) of breast cancer using mammograms. In this work, a comparison of the performance between the features of Discrete Wavelet Transform (DWT) and Spherical Wavelet Transform (SWT) based on the classification results of normal, benign and malignant stage was studied. Classification was performed using Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Nearest Mean Classifier (NMC), Support Vector Machines (SVM) and Parzen Classifier (ParzenC). We have obtained a maximum classification accuracy of 81.73% for DWT and 88.80% for SWT features using SVM classifier. PMID:24000991
NASA Astrophysics Data System (ADS)
Plesniak, Daniel H.; Bulusu, Kartik V.; Plesniak, Michael W.
2012-11-01
Interpretation of complex flow patterns observed in this study of a model curved artery required characterization of multiple, low-circulation secondary flow structures that were observed during the late systolic deceleration and diastolic phases under physiological inflow conditions. Phase-locked, planar vorticity PIV data were acquired at various cross-sectional locations of the 180-degree bent tube model. High circulation, deformed Dean- and Lyne-type vortices were observed during early stages of deceleration, while several smaller scale, highly deformed, low-circulation vortical patterns appeared in the core and near-wall regions during late systolic deceleration and diastolic phases. Due to the multiplicity of vortical scales and shapes, anisotropic 2D Ricker wavelets were used for coherent structure detection in a continuous wavelet transform algorithm (PIVlet 1.2). Our bio-inspired study is geared towards understanding whether optimizing the shape of the wavelet kernel will enable better resolution of several low-circulation, multi-scale secondary flow morphologies and whether new insights into the dynamics of arterial secondary flow structures can accordingly be gained. Supported by the National Science Foundation, Grant No. CBET-0828903 and GW Center for Biomimetics and Bioinspired Engineering (COBRE).
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.
NASA Astrophysics Data System (ADS)
Abuturab, Muhammad Rafiq
2015-11-01
A novel gyrator wavelet transform based non-linear multiple single channel information fusion and authentication is introduced. In this technique, each user channel is normalized, phase encoded, and modulated by random phase function, and then multiplexed into a single channel user ciphertext. Now, the secret channel of corresponding user is phase encoded, modulated by random phase function, and gyrator transformed, and then multiplexed into a single channel secret ciphertext. The user ciphertext and secret ciphertext are multiplied to get a single channel multiplex image and then inverse gyrator transformed. The resultant spectrum is phase- and amplitude-truncated to obtain the encrypted image and the asymmetric key, respectively. The encrypted image is a single-level 2-D discrete wavelet transformed. The information is decomposed into LL, HL, LH, and HH sub-bands. This process is repeated to obtain three sets of four sub-bands of three different images. Next, the individual sub-band of each encrypted image is fused to get four fused sub-bands. Finally, the four fused sub-bands are inverse single-level 2-D discrete wavelet transformed to obtain final encrypted image. This is the main advantage for the proposed system: using multiple individual decryption keys (authentication key, asymmetric key, secret keys, and sub-band keys) for each user not only expands the key spaces but also supplies non-linear keys to control the system security. Moreover, the orders of gyrator transform provide extra degrees of freedom. The theoretical analysis and numerical simulation results support the proposed method.
Digital audio signal filtration based on the dual-tree wavelet transform
NASA Astrophysics Data System (ADS)
Yaseen, A. S.; Pavlov, A. N.
2015-07-01
A new method of digital audio signal filtration based on the dual-tree wavelet transform is described. An adaptive approach is proposed that allows the automatic adjustment of parameters of the wavelet filter to be optimized. A significant improvement of the quality of signal filtration is demonstrated in comparison to the traditionally used filters based on the discrete wavelet transform.
Evaluating Interpersonal Synchrony: Wavelet Transform Toward an Unstructured Conversation.
Fujiwara, Ken; Daibo, Ikuo
2016-01-01
This study examined whether interpersonal synchrony could be extracted using spectrum analysis (i.e., wavelet transform) in an unstructured conversation. Sixty-two female undergraduates were randomly paired and they engaged in a 6-min unstructured conversation. Interpersonal synchrony was evaluated by calculating the cross-wavelet coherence of the time-series movement data, extracted using a video-image analysis software. The existence of synchrony was tested using a pseudo-synchrony paradigm. In addition, the frequency at which the synchrony occurred and the distribution of the relative phase was explored. The results showed that the value of cross-wavelet coherence was higher in the experimental participant pairs than in the pseudo pairs. Further, the coherence value was higher in the frequency band under 0.5 Hz. These results support the validity of evaluating interpersonal synchron Behavioral mimicry and interpersonal syyby using wavelet transform even in an unstructured conversation. However, the role of relative phase was not clear; there was no significant difference between each relative-phase region. The theoretical contribution of these findings to the area of interpersonal coordination is discussed.
Evaluating Interpersonal Synchrony: Wavelet Transform Toward an Unstructured Conversation
Fujiwara, Ken; Daibo, Ikuo
2016-01-01
This study examined whether interpersonal synchrony could be extracted using spectrum analysis (i.e., wavelet transform) in an unstructured conversation. Sixty-two female undergraduates were randomly paired and they engaged in a 6-min unstructured conversation. Interpersonal synchrony was evaluated by calculating the cross-wavelet coherence of the time-series movement data, extracted using a video-image analysis software. The existence of synchrony was tested using a pseudo-synchrony paradigm. In addition, the frequency at which the synchrony occurred and the distribution of the relative phase was explored. The results showed that the value of cross-wavelet coherence was higher in the experimental participant pairs than in the pseudo pairs. Further, the coherence value was higher in the frequency band under 0.5 Hz. These results support the validity of evaluating interpersonal synchron Behavioral mimicry and interpersonal syyby using wavelet transform even in an unstructured conversation. However, the role of relative phase was not clear; there was no significant difference between each relative-phase region. The theoretical contribution of these findings to the area of interpersonal coordination is discussed. PMID:27148125
Evaluating Interpersonal Synchrony: Wavelet Transform Toward an Unstructured Conversation.
Fujiwara, Ken; Daibo, Ikuo
2016-01-01
This study examined whether interpersonal synchrony could be extracted using spectrum analysis (i.e., wavelet transform) in an unstructured conversation. Sixty-two female undergraduates were randomly paired and they engaged in a 6-min unstructured conversation. Interpersonal synchrony was evaluated by calculating the cross-wavelet coherence of the time-series movement data, extracted using a video-image analysis software. The existence of synchrony was tested using a pseudo-synchrony paradigm. In addition, the frequency at which the synchrony occurred and the distribution of the relative phase was explored. The results showed that the value of cross-wavelet coherence was higher in the experimental participant pairs than in the pseudo pairs. Further, the coherence value was higher in the frequency band under 0.5 Hz. These results support the validity of evaluating interpersonal synchron Behavioral mimicry and interpersonal syyby using wavelet transform even in an unstructured conversation. However, the role of relative phase was not clear; there was no significant difference between each relative-phase region. The theoretical contribution of these findings to the area of interpersonal coordination is discussed. PMID:27148125
Abuturab, Muhammad Rafiq
2015-10-01
A novel method of group multiple-image encoding and watermarking using coupled logistic maps and gyrator wavelet transform is presented. The proposed method employs three different groups of multiple images. The color images of each group are individually segregated into R, G, and B channels. Each channel is first permutated by using a sequence of chaotic pairs generated with a system of two symmetrically coupled identical logistic maps and then gyrator transformed. The gyrator spectrum of each channel is multiplied together and then modulated by a random phase function to obtain a corresponding multiplex channel. The encoded multiplex image is restituted through a concatenation of R, G, and B multiplex channels. The phase and amplitude functions of the first, second, and third groups of encoded multiplex images are generated. The host image is a single-level 2D discrete wavelet transformed to decompose into LL, HL, LH, and HH subbands. HL, LH, and HH subbands are then replaced with phase functions of the first, second, and third groups, respectively. Finally, the resultant image is an inverse single-level 2D discrete wavelet transformed to construct a watermarked image. The three groups of multiple images are protected not only by the encryption algorithm but also visually by the host image. Thus, a high level of security can be achieved. Each group includes group decryption keys, and each image of the group comprises individual decryption keys beside parameters of coupled logistic maps and gyrator transform. As a result, the key space is very large. The decryption system can be realized by using an optoelectronic device. The numerical simulation results confirm the validity and security of the proposed scheme.
Abuturab, Muhammad Rafiq
2015-10-01
A novel method of group multiple-image encoding and watermarking using coupled logistic maps and gyrator wavelet transform is presented. The proposed method employs three different groups of multiple images. The color images of each group are individually segregated into R, G, and B channels. Each channel is first permutated by using a sequence of chaotic pairs generated with a system of two symmetrically coupled identical logistic maps and then gyrator transformed. The gyrator spectrum of each channel is multiplied together and then modulated by a random phase function to obtain a corresponding multiplex channel. The encoded multiplex image is restituted through a concatenation of R, G, and B multiplex channels. The phase and amplitude functions of the first, second, and third groups of encoded multiplex images are generated. The host image is a single-level 2D discrete wavelet transformed to decompose into LL, HL, LH, and HH subbands. HL, LH, and HH subbands are then replaced with phase functions of the first, second, and third groups, respectively. Finally, the resultant image is an inverse single-level 2D discrete wavelet transformed to construct a watermarked image. The three groups of multiple images are protected not only by the encryption algorithm but also visually by the host image. Thus, a high level of security can be achieved. Each group includes group decryption keys, and each image of the group comprises individual decryption keys beside parameters of coupled logistic maps and gyrator transform. As a result, the key space is very large. The decryption system can be realized by using an optoelectronic device. The numerical simulation results confirm the validity and security of the proposed scheme. PMID:26479935
NASA Astrophysics Data System (ADS)
Huda, Feblil; Kajiwara, Itsuro; Hosoya, Naoki
2014-08-01
In this paper, a vibration testing and health monitoring system based on an impulse response excited by laser is proposed to detect damage in membrane structures. A high power Nd: YAG pulse laser is used to supply an ideal impulse to a membrane structure by generating shock waves via laser-induced breakdown in air. A health monitoring apparatus is developed with this vibration testing system and a damage detecting algorithm which only requires the vibration mode shape of the damaged membrane. Artificial damage is induced in membrane structure by cutting and tearing the membrane. The vibration mode shapes of the membrane structure extracted from vibration testing by using the laser-induced breakdown and laser Doppler vibrometer are then analyzed by 2-D continuous wavelet transformation. The location of damage is determined by the dominant peak of the wavelet coefficient which can be seen clearly by applying a boundary treatment and the concept of an iso-surface to the 2-D wavelet coefficient. The applicability of the present approach is verified by finite element analysis and experimental results, demonstrating the ability of the method to detect and identify the positions of damage induced on the membrane structure.
Remote Sensing Image Fusion Using Ica and Optimized Wavelet Transform
NASA Astrophysics Data System (ADS)
Hnatushenko, V. V.; Vasyliev, V. V.
2016-06-01
In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.
Classification of Transient Phenomena in Distribution System using wavelet Transform
NASA Astrophysics Data System (ADS)
Sedighi, Alireza
2014-05-01
An efficient procedure for classification of transient phenomena in distribution systems is proposed in this paper. The proposed method has been applied to classify some transient phenomena such as inrush current, load switching, capacitor switching and single phase to ground fault. The new scheme is based on wavelet transform algorithm. All of the events for feature extraction and test are simulated using Electro Magnetic Transient Program (EMTP). Results show high accuracy of proposed method.
Low-memory-usage image coding with line-based wavelet transform
NASA Astrophysics Data System (ADS)
Ye, Linning; Guo, Jiangling; Nutter, Brian; Mitra, Sunanda
2011-02-01
When compared to the traditional row-column wavelet transform, the line-based wavelet transform can achieve significant memory savings. However, the design of an image codec using the line-based wavelet transform is an intricate task because of the irregular order in which the wavelet coefficients are generated. The independent block coding feature of JPEG2000 makes it work effectively with the line-based wavelet transform. However, with wavelet tree-based image codecs, such as set partitioning in hierarchical trees, the memory usage of the codecs does not realize significant advantage with the line-based wavelet transform because many wavelet coefficients must be buffered before the coding starts. In this paper, the line-based wavelet transform was utilized to facilitate backward coding of wavelet trees (BCWT). Although the BCWT algorithm is a wavelet tree-based algorithm, its coding order differs from that of the traditional wavelet tree-based algorithms, which allows the proposed line-based image codec to become more memory efficient than other line-based image codecs, including line-based JPEG2000, while still offering comparable rate distortion performance and much lower system complexity.
Image compression using wavelet transform and multiresolution decomposition.
Averbuch, A; Lazar, D; Israeli, M
1996-01-01
Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512x512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480x480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined.
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.
Automation of the lifting factorisation of wavelet transforms
NASA Astrophysics Data System (ADS)
Maslen, M.; Abbott, P.
2000-05-01
Wavelets are sets of basis functions used in the analysis of signals and images. In contrast to Fourier analysis, wavelets have both spatial and frequency localization, making them useful for the analysis of sharply-varying or non-periodic signals. The lifting scheme for finding the discrete wavelet transform was demonstrated by Daubechies and Sweldens (1996). In particular, they showed that this method depends on the factorization of polyphase matrices, whose entries are Laurent polynomials, using the Euclidean algorithm extended to Laurent polynomials. Such factorization is not unique and hence there are multiple factorizations of the polyphase matrix. In this paper we outline a Mathematica program that finds all factorizations of such matrices by automating the Euclidean algorithm for Laurent polynomials. Polynomial reduction using Gröbner bases was also incorporated into the program so as to reduce the number of wavelet filter coefficients appearing in a given expression through use of the relations they satisfy, thus permitting exact symbolic factorizations for any polyphase matrix.
[Adaptive de-noising of ECG signal based on stationary wavelet transform].
Dong, Hong-sheng; Zhang, Ai-hua; Hao, Xiao-hong
2009-03-01
According to the limitations of wavelet threshold in de-noising method, we approached a combining algorithm of the stationary wavelet transform with adaptive filter. The stationary wavelet transformation can suppress Gibbs phenomena in traditional DWT effectively, and adaptive filter is introduced at the high scale wavelet coefficient of the stationary wavelet transformation. It would remove baseline wander and keep the shape of low frequency and low amplitude P wave, T wave and ST segment wave of ECG signal well. That is important for analyzing ECG signal of other feature information.
Continuous wavelet transform for non-stationary vibration detection with phase-OTDR.
Qin, Zengguang; Chen, Liang; Bao, Xiaoyi
2012-08-27
We propose the continuous wavelet transform for non-stationary vibration measurement by distributed vibration sensor based on phase optical time-domain reflectometry (OTDR). The continuous wavelet transform approach can give simultaneously the frequency and time information of the vibration event. Frequency evolution is obtained by the wavelet ridge detection method from the scalogram of the continuous wavelet transform. In addition, a novel signal processing algorithm based on the global wavelet spectrum is used to determine the location of vibration. Distributed vibration measurements of 500 Hz and 500 Hz to 1 kHz sweep events over 20 cm fiber length are demonstrated using a single mode fiber.
Li, Jingsong; Yu, Benli; Fischer, Horst
2015-04-01
This paper presents a novel methodology-based discrete wavelet transform (DWT) and the choice of the optimal wavelet pairs to adaptively process tunable diode laser absorption spectroscopy (TDLAS) spectra for quantitative analysis, such as molecular spectroscopy and trace gas detection. The proposed methodology aims to construct an optimal calibration model for a TDLAS spectrum, regardless of its background structural characteristics, thus facilitating the application of TDLAS as a powerful tool for analytical chemistry. The performance of the proposed method is verified using analysis of both synthetic and observed signals, characterized with different noise levels and baseline drift. In terms of fitting precision and signal-to-noise ratio, both have been improved significantly using the proposed method.
NASA Astrophysics Data System (ADS)
Harikumar, Rajaguru; Vijayakumar, Thangavel
2014-12-01
The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.
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.
Speech signal filtration using double-density dual-tree complex wavelet transform
NASA Astrophysics Data System (ADS)
Yasin, A. S.; Pavlova, O. N.; Pavlov, A. N.
2016-08-01
We consider the task of increasing the quality of speech signal cleaning from additive noise by means of double-density dual-tree complex wavelet transform (DDCWT) as compared to the standard method of wavelet filtration based on a multiscale analysis using discrete wavelet transform (DWT) with real basis set functions such as Daubechies wavelets. It is shown that the use of DDCWT instead of DWT provides a significant increase in the mean opinion score (MOS) rating at a high additive noise and makes it possible to reduce the number of expansion levels for the subsequent correction of wavelet coefficients.
Givehchi, Alireza; Bender, Andreas; Glen, Robert C
2006-01-01
The effect of multitarget dependent descriptor transformation on classification performance is explored in this work. To this end decision trees as well as neural net QSAR in combination with PLS were applied to predict the activity class of 5HT3 ligands, angiotensin converting enzyme inhibitors, 3-hydroxyl-3-methyl glutaryl coenzyme A reductase inhibitors, platelet activating factor antagonists, and thromboxane A2 antagonists. Physicochemical descriptors calculated by MOE and fragment-based descriptors (MOLPRINT 2D) were employed to generate descriptor vectors. In a subsequent step the physicochemical descriptor vectors were transformed to a lower dimensional space using multitarget dependent descriptor transformation. Cross-validation of the original physicochemical descriptors in combination with decision trees and neural net QSAR as well as cross-validation of PLS multitarget transformed descriptors with neural net QSAR were performed. For comparison this was repeated using fragment-based descriptors in combination with decision trees.
Efficient architectures for two-dimensional discrete wavelet transform using lifting scheme.
Xiong, Chengyi; Tian, Jinwen; Liu, Jian
2007-03-01
Novel architectures for 1-D and 2-D discrete wavelet transform (DWT) by using lifting schemes are presented in this paper. An embedded decimation technique is exploited to optimize the architecture for 1-D DWT, which is designed to receive an input and generate an output with the low- and high-frequency components of original data being available alternately. Based on this 1-D DWT architecture, an efficient line-based architecture for 2-D DWT is further proposed by employing parallel and pipeline techniques, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. This 2-D architecture is called fast architecture (FA) that can perform J levels of decomposition for N * N image in approximately 2N2(1 - 4(-J))/3 internal clock cycles. Moreover, another efficient generic line-based 2-D architecture is proposed by exploiting the parallelism among four subband transforms in lifting-based 2-D DWT, which can perform J levels of decomposition for N * N image in approximately N2(1 - 4(-J))/3 internal clock cycles; hence, it is called high-speed architecture. The throughput rate of the latter is increased by two times when comparing with the former 2-D architecture, but only less additional hardware cost is added. Compared with the works reported in previous literature, the proposed architectures for 2-D DWT are efficient alternatives in tradeoff among hardware cost, throughput rate, output latency and control complexity, etc. PMID:17357722
Quantum computation of multifractal exponents through the quantum wavelet transform
Garcia-Mata, Ignacio; Giraud, Olivier; Georgeot, Bertrand
2009-05-15
We study the use of the quantum wavelet transform to extract efficiently information about the multifractal exponents for multifractal quantum states. We show that, combined with quantum simulation algorithms, it enables to build quantum algorithms for multifractal exponents with a polynomial gain compared to classical simulations. Numerical results indicate that a rough estimate of fractality could be obtained exponentially fast. Our findings are relevant, e.g., for quantum simulations of multifractal quantum maps and of the Anderson model at the metal-insulator transition.
Wavelet transform modulus maxima based fractal correlation analysis
NASA Astrophysics Data System (ADS)
Lin, D. C.; Sharif, A.
2007-12-01
The wavelet transform modulus maxima (WTMM) used in the singularity analysis of one fractal function is extended to study the fractal correlation of two multifractal functions. The technique is developed in the framework of joint partition function analysis (JPFA) proposed by Meneveau et al. [C. Meneveau, K.R. Sreenivasan, Phys. Rev. A 41, 894 (1990)] and is shown to be equally effective. In addition, we show that another leading approach developed for the same purpose, namely, relative multifractal analysis, can be considered as a special case of JPFA at a particular parameter setting.
Adaptive segmentation of wavelet transform coefficients for video compression
NASA Astrophysics Data System (ADS)
Wasilewski, Piotr
2000-04-01
This paper presents video compression algorithm suitable for inexpensive real-time hardware implementation. This algorithm utilizes Discrete Wavelet Transform (DWT) with the new Adaptive Spatial Segmentation Algorithm (ASSA). The algorithm was designed to obtain better or similar decompressed video quality in compare to H.263 recommendation and MPEG standard using lower computational effort, especially at high compression rates. The algorithm was optimized for hardware implementation in low-cost Field Programmable Gate Array (FPGA) devices. The luminance and chrominance components of every frame are encoded with 3-level Wavelet Transform with biorthogonal filters bank. The low frequency subimage is encoded with an ADPCM algorithm. For the high frequency subimages the new Adaptive Spatial Segmentation Algorithm is applied. It divides images into rectangular blocks that may overlap each other. The width and height of the blocks are set independently. There are two kinds of blocks: Low Variance Blocks (LVB) and High Variance Blocks (HVB). The positions of the blocks and the values of the WT coefficients belonging to the HVB are encoded with the modified zero-tree algorithms. LVB are encoded with the mean value. Obtained results show that presented algorithm gives similar or better quality of decompressed images in compare to H.263, even up to 5 dB in PSNR measure.
Identification of formation interfaces by using wavelet and Fourier transforms
NASA Astrophysics Data System (ADS)
Mukherjee, Bappa; Srivardhan, V.; Roy, P. N. S.
2016-05-01
The identification of formation interfaces is of prime importance from well log data. The interfaces are not clearly discernible due to the presence of high and low frequency noise in the log response. Accurate bed boundary information is very crucial in hydrocarbon exploration and the problem has received considerable attention and many techniques have been proposed. Frequency spectrum based filtering techniques aids us in interpretation, but usually leads to inaccurate amplification of unwanted components of the log response. Wavelet transform is very effective in denoising the log response and can be carried out to filter low and high frequency components of signal. The use of Fourier and Wavelet transform in denoising the log data for obtaining formation interfaces is demonstrated in this work. The feasibility of the proposed technique is tested so that it can be used in the industry to decipher formation interfaces. The work flow is demonstrated by testing on wells belonging to the Upper Assam Basin, which are self-potential, gamma ray, and resistivity log responses.
Microarray image enhancement by denoising using stationary wavelet transform.
Wang, X H; Istepanian, Robert S H; Song, Yong Hua
2003-12-01
Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.
Remotely sensed image compression based on wavelet transform
NASA Technical Reports Server (NTRS)
Kim, Seong W.; Lee, Heung K.; Kim, Kyung S.; Choi, Soon D.
1995-01-01
In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.
The use of wavelet transforms in the solution of two-phase flow problems
Moridis, G.J.; Nikolaou, M.; You, Yong
1994-10-01
In this paper we present the use of wavelets to solve the nonlinear Partial Differential.Equation (PDE) of two-phase flow in one dimension. The wavelet transforms allow a drastically different approach in the discretization of space. In contrast to the traditional trigonometric basis functions, wavelets approximate a function not by cancellation but by placement of wavelets at appropriate locations. When an abrupt chance, such as a shock wave or a spike, occurs in a function, only local coefficients in a wavelet approximation will be affected. The unique feature of wavelets is their Multi-Resolution Analysis (MRA) property, which allows seamless investigational any spatial resolution. The use of wavelets is tested in the solution of the one-dimensional Buckley-Leverett problem against analytical solutions and solutions obtained from standard numerical models. Two classes of wavelet bases (Daubechies and Chui-Wang) and two methods (Galerkin and collocation) are investigated. We determine that the Chui-Wang, wavelets and a collocation method provide the optimum wavelet solution for this type of problem. Increasing the resolution level improves the accuracy of the solution, but the order of the basis function seems to be far less important. Our results indicate that wavelet transforms are an effective and accurate method which does not suffer from oscillations or numerical smearing in the presence of steep fronts.
The application study of wavelet packet transformation in the de-noising of dynamic EEG data.
Li, Yifeng; Zhang, Lihui; Li, Baohui; Wei, Xiaoyang; Yan, Guiding; Geng, Xichen; Jin, Zhao; Xu, Yan; Wang, Haixia; Liu, Xiaoyan; Lin, Rong; Wang, Quan
2015-01-01
This paper briefly describes the basic principle of wavelet packet analysis, and on this basis introduces the general principle of wavelet packet transformation for signal den-noising. The dynamic EEG data under +Gz acceleration is made a de-noising treatment by using wavelet packet transformation, and the de-noising effects with different thresholds are made a comparison. The study verifies the validity and application value of wavelet packet threshold method for the de-noising of dynamic EEG data under +Gz acceleration. PMID:26405863
Short Exon Detection via Wavelet Transform Modulus Maxima
Zhang, Xiaolei; Shen, Zhiwei; Zhang, Guishan; Shen, Yuanyu; Chen, Miaomiao; Zhao, Jiaxiang; Wu, Renhua
2016-01-01
The detection of short exons is a challenging open problem in the field of bioinformatics. Due to the fact that the weakness of existing model-independent methods lies in their inability to reliably detect small exons, a model-independent method based on the singularity detection with wavelet transform modulus maxima has been developed for detecting short coding sequences (exons) in eukaryotic DNA sequences. In the analysis of our method, the local maxima can capture and characterize singularities of short exons, which helps to yield significant patterns that are rarely observed with the traditional methods. In order to get some information about singularities on the differences between the exon signal and the background noise, the noise level is estimated by filtering the genomic sequence through a notch filter. Meanwhile, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the wavelet coefficients for improving the computational efficiency. In addition, the output measure of a paired-numerical representation calculated in both forward and reverse directions is used to incorporate a useful DNA structural property. The performances of our approach and other techniques are evaluated on two benchmark data sets. Experimental results demonstrate that the proposed method outperforms all assessed model-independent methods for detecting short exons in terms of evaluation metrics. PMID:27635656
ECG signals denoising using wavelet transform and independent component analysis
NASA Astrophysics Data System (ADS)
Liu, Manjin; Hui, Mei; Liu, Ming; Dong, Liquan; Zhao, Zhu; Zhao, Yuejin
2015-08-01
A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.
Short Exon Detection via Wavelet Transform Modulus Maxima.
Zhang, Xiaolei; Shen, Zhiwei; Zhang, Guishan; Shen, Yuanyu; Chen, Miaomiao; Zhao, Jiaxiang; Wu, Renhua
2016-01-01
The detection of short exons is a challenging open problem in the field of bioinformatics. Due to the fact that the weakness of existing model-independent methods lies in their inability to reliably detect small exons, a model-independent method based on the singularity detection with wavelet transform modulus maxima has been developed for detecting short coding sequences (exons) in eukaryotic DNA sequences. In the analysis of our method, the local maxima can capture and characterize singularities of short exons, which helps to yield significant patterns that are rarely observed with the traditional methods. In order to get some information about singularities on the differences between the exon signal and the background noise, the noise level is estimated by filtering the genomic sequence through a notch filter. Meanwhile, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the wavelet coefficients for improving the computational efficiency. In addition, the output measure of a paired-numerical representation calculated in both forward and reverse directions is used to incorporate a useful DNA structural property. The performances of our approach and other techniques are evaluated on two benchmark data sets. Experimental results demonstrate that the proposed method outperforms all assessed model-independent methods for detecting short exons in terms of evaluation metrics. PMID:27635656
Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform
Han, Guang; Wang, Jinkuan; Cai, Xi
2016-01-01
Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques. PMID:27043570
Application of Wavelet Transform for PDZ Domain Classification
Daqrouq, Khaled; Alhmouz, Rami; Balamesh, Ahmed; Memic, Adnan
2015-01-01
PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III), that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT) and Shannon entropy denoted by wavelet entropy (WE) feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%), which outperformed our occurrence rearrangements based method. The recognition rate was (81.41%) with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification. PMID:25860375
Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform.
Han, Guang; Wang, Jinkuan; Cai, Xi
2016-01-01
Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques. PMID:27043570
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.
Implementing wavelet inverse-transform processor with surface acoustic wave device.
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.
Multi-frequency fringe projection profilometry based on wavelet transform.
Jiang, Chao; Jia, Shuhai; Dong, Jun; Lian, Qin; Li, Dichen
2016-05-30
Based on wavelet transforms (WTs), an alternative multi-frequency fringe projection profilometry is described. Fringe patterns with multiple frequencies are projected onto an object and the reflected patterns are recorded digitally. Phase information for every pattern is calculated by identifying the ridge that appears in WT results. Distinct from the phase unwrapping process, a peak searching algorithm is applied to obtain object height from the phases of the different frequency for a single point on the object. Thus, objects with large discontinuities can be profiled. In comparing methods, the height profiles obtained from the WTs have lower noise and higher measurement accuracy. Although measuring times are similar, the proposed method offers greater reliability. PMID:27410063
The wavelet transform function to analyze interplanetary scintillation observations
NASA Astrophysics Data System (ADS)
Aguilar-Rodriguez, E.; Rodriguez-Martinez, M.; Romero-Hernandez, E.; Mejia-Ambriz, J. C.; Gonzalez-Esparza, J. A.; Tokumaru, M.
2014-05-01
Interplanetary scintillation (IPS) observations are useful to remotely sense the inner heliosphere. We present a new technique to analyze IPS observations using a wavelet transform (WT) function. This technique allows us to derive, in a straightforward way, a simple method to obtain the scintillation index (m). We tested this WT technique to analyze IPS observations obtained by the Solar-Terrestrial Environment Laboratory (STEL) radio telescope. The analysis of the m index of the radio source 3C48 detected by STEL over the year 2012 shows the expected decrease with solar elongation reported in previous studies. The WT technique has a great potential for future solar wind studies using IPS observations from contemporary radio telescopes.
Atrial fibrillation detection using stationary wavelet transform analysis.
Weng, Binwei; Wang, John J; Michaud, Francis; Blanco-Velasco, Manuel
2008-01-01
Atrial fibrillation (AF) is a common cardiac arrythmia that is usually developed for elder people with aging. AF may result in complications such as chest pain or even heart failure in later stage. Based on the characteristics of surface ECG, AF can be detected by several methods. A particular investigation on the fibrillatory waveform reveals the inherent structure of AF signals. As opposed to traditional frequency domain methods, we utilize the stationary wavelet transform to extract the information from ECG signal which differentiates AF and non-AF cases based on some feature extraction and selection processes. A linear classifier is then designed for computational efficiency. The proposed method eliminates the need for QRST cancellation step which is required for frequency domain methods and provides a more systematic approach for AF detection. Extensive experiments are tested on signals from the MIT-BIH Atrial Fibrillation Database to show the superior performance of the proposed algorithm.
An overview of the quantum wavelet transform, focused on earth science applications
NASA Astrophysics Data System (ADS)
Shehab, O.; LeMoigne, J.; Lomonaco, S.; Halem, M.
2015-12-01
Registering the images from the MODIS system and the OCO-2 satellite is currently being done by classical image registration techniques. One such technique is wavelet transformation. Besides image registration, wavelet transformation is also used in other areas of earth science, for example, processinga and compressing signal variation, etc. In this talk, we investigate the applicability of few quantum wavelet transformation algorithms to perform image registration on the MODIS and OCO-2 data. Most of the known quantum wavelet transformation algorithms are data agnostic. We investigate their applicability in transforming Flexible Representation for Quantum Images. Similarly, we also investigate the applicability of the algorithms in signal variation analysis. We also investigate the transformation of the models into pseudo-boolean functions to implement them on commercially available quantum annealing computers, such as the D-Wave computer located at NASA Ames.
Two-dimensional integer wavelet transform with reduced influence of rounding operations
NASA Astrophysics Data System (ADS)
Strutz, Tilo; Rennert, Ines
2012-12-01
If a system for lossless compression of images applies a decorrelation step, this step must map integer input values to integer output values. This can be achieved, for example, using the integer wavelet transform (IWT). The non-linearity, introduced by the obligatory rounding steps, is the main drawback of the IWT, since it deteriorates the desired filter characteristic. This paper discusses different methods for reducing the influence of rounding in 5/3 and 9/7 filter banks. A novel combination of two-dimensional implementations of the JPEG2000 9/7 filter bank with new filter coefficients is proposed and the effects of the methods on lossless image compression are investigated. In addition, these filter banks are compared to the 9/7 Deslauriers-Dubuc filter bank (97DD). The analysed two-dimensional implementations generally perform better than their one-dimensional counterparts in terms of compression ratio for natural images. On average, the 2D 97DD filter bank performs best. In addition, it has been found that the compression results cannot be improved by simply reducing the number of lifting steps via 2D implementations of the JPEG2000 9/7 filter bank. Only the 2D implementation with a minimum number of lifting steps, in combination with modified lifting coefficients, leads to fewer bits per pixel than the separable implementation on average for a selected set of images.
Gao, Zhan; Deng, Yan; Duan, Yiting; Zhang, Zhifeng; Wei, Cheng; Chen, Shiqian; Cui, Jianying; Feng, Qibo
2012-01-01
A heterodyne temporal speckle pattern interferometer that measures the in-plane displacement dynamically has been built. The object is displaced in its plane continuously and the frequency-modulated output signals with a carrier frequency are recorded by a CCD camera. The displacement information is extracted with wavelet transform technique. Preliminary experiments have been performed with such interferometer. The respective measurement results recovered from wavelet transform and Fourier transform are compared.
Biomedical image and signal de-noising using dual tree complex wavelet transform
NASA Astrophysics Data System (ADS)
Rizi, F. Yousefi; Noubari, H. Ahmadi; Setarehdan, S. K.
2011-10-01
Dual tree complex wavelet transform(DTCWT) is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The purposes of de-noising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal or image. This paper proposes a method for removing white Gaussian noise from ECG signals and biomedical images. The discrete wavelet transform (DWT) is very valuable in a large scope of de-noising problems. However, it has limitations such as oscillations of the coefficients at a singularity, lack of directional selectivity in higher dimensions, aliasing and consequent shift variance. The complex wavelet transform CWT strategy that we focus on in this paper is Kingsbury's and Selesnick's dual tree CWT (DTCWT) which outperforms the critically decimated DWT in a range of applications, such as de-noising. Each complex wavelet is oriented along one of six possible directions, and the magnitude of each complex wavelet has a smooth bell-shape. In the final part of this paper, we present biomedical image and signal de-noising by the means of thresholding magnitude of the wavelet coefficients.
NASA Astrophysics Data System (ADS)
Nenna, V.; Pidlisecky, A.
2012-12-01
As mapping of groundwater resources with airborne electromagnetics expands into more urban areas, it is increasingly important to identify sources of cultural noise in acquired data sets. A number of methods have been proposed to reduce the impact of cultural coupling on acquired data. While intense local calibration to increase the signal to noise ratio has been used, most often in practice, the transients associated with these noise sources are manually identified and filtered out during data processing. This can be a challenging task, particularly as datasets grow large (e.g. up to terabytes of data). In response to this, we propose a method for identifying noise in airborne electromagnetic data based on a spatial application of the continuous wavelet transform (CWT). We apply a continuous wavelet transform to three airborne electromagnetic surveys collected in the Edmonton-Calgary Corridor as part of a groundwater inventory sponsored by the Alberta Geological Survey and Environment Alberta. The three surveys consist of 210 flightlines covering approximately 18 000 linear kilometers with roughly 13 m sounding spacing. B-field and dB/dt data from a three-component 20-channel GeoTEM multicoil system, were recorded at 5 on-time and 15 off-time channels with a total measurement time of 16.664 ms per sounding. The nominal height of vertical axis transmitter was 120 m; the current pulse was 670 A, and the pulse-width was 4.045 ms. Wavelet transforms are localized in time and frequency, similar to a windowed Fourier transform, and are used to identify dominant frequencies within a signal as a function of time or space. While there are a number of options for wavelet functions, we convolve a Morlet wavelet with the data signal at 120 distance scales on a logarithmic scale from 0.1 to 30 km. We calculate the CWT along each flightline for all off-time channels. We then calculate the wavelet power normalized by the data variance, and bin results into 4 bins of spatial
Fast Fourier and Wavelet Transforms for Wavefront Reconstruction in Adaptive Optics
Dowla, F U; Brase, J M; Olivier, S S
2000-07-28
Wavefront reconstruction techniques using the least-squares estimators are computationally quite expensive. We compare wavelet and Fourier transforms techniques in addressing the computation issues of wavefront reconstruction in adaptive optics. It is shown that because the Fourier approach is not simply a numerical approximation technique unlike the wavelet method, the Fourier approach might have advantages in terms of numerical accuracy. However, strictly from a numerical computations viewpoint, the wavelet approximation method might have advantage in terms of speed. To optimize the wavelet method, a statistical study might be necessary to use the best basis functions or ''approximation tree.''
Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates
Dovlo, Edem; Baddour, Natalie
2015-01-01
The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: • The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms. • The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform. • The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time. PMID:26150988
Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates.
Dovlo, Edem; Baddour, Natalie
2015-01-01
The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: •The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms.•The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform.•The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time.
R-peaks detection based on stationary wavelet transform.
Merah, M; Abdelmalik, T A; Larbi, B H
2015-10-01
Automatic detection of the QRS complexes/R-peaks in an electrocardiogram (ECG) signal is the most important step preceding any kind of ECG processing and analysis. The performance of these systems heavily relies on the accuracy of the QRS detector. The objective of present work is to drive a new robust method based on stationary wavelet transform (SWT) for R-peaks detection. The decimation of the coefficients at each level of the transformation algorithm is omitted, more samples in the coefficient sequences are available and hence a better outlier detection can be performed. Using the information of local maxima, minima and zero crossings of the fourth SWT coefficient detail, the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes, as well as detection and identification of the QRS individual waves peaks of the pre-processed ECG signal. Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, achieving excellent performance on different databases, on the MIT-BIH database (Se=99.84%, P=99.88%), on the QT Database (Se=99.94%, P=99.89%) and on MIT-BIH Noise Stress Test Database, (Se=95.30%, P=93.98%). Reliability and accuracy are close to the highest among the ones obtained in other studies. Experiments results being satisfactory, the SWT may represent a novel QRS detection tool, for a robust ECG signal analysis. PMID:26105724
R-peaks detection based on stationary wavelet transform.
Merah, M; Abdelmalik, T A; Larbi, B H
2015-10-01
Automatic detection of the QRS complexes/R-peaks in an electrocardiogram (ECG) signal is the most important step preceding any kind of ECG processing and analysis. The performance of these systems heavily relies on the accuracy of the QRS detector. The objective of present work is to drive a new robust method based on stationary wavelet transform (SWT) for R-peaks detection. The decimation of the coefficients at each level of the transformation algorithm is omitted, more samples in the coefficient sequences are available and hence a better outlier detection can be performed. Using the information of local maxima, minima and zero crossings of the fourth SWT coefficient detail, the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes, as well as detection and identification of the QRS individual waves peaks of the pre-processed ECG signal. Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, achieving excellent performance on different databases, on the MIT-BIH database (Se=99.84%, P=99.88%), on the QT Database (Se=99.94%, P=99.89%) and on MIT-BIH Noise Stress Test Database, (Se=95.30%, P=93.98%). Reliability and accuracy are close to the highest among the ones obtained in other studies. Experiments results being satisfactory, the SWT may represent a novel QRS detection tool, for a robust ECG signal analysis.
Wavelet-transform-based time-frequency domain reflectometry for reduction of blind spot
NASA Astrophysics Data System (ADS)
Lee, Sin Ho; Park, Jin Bae; Choi, Yoon Ho
2012-06-01
In this paper, wavelet-transform-based time-frequency domain reflectometry (WTFDR) is proposed to reduce the blind spot in reflectometry. TFDR has a blind spot problem when the time delay between the reference signal and the reflected signal is short enough compared with the time duration of the reference signal. To solve the blind spot problem, the wavelet transform (WT) is used because the WT has linearity. Using the characteristics of the WT, the overlapped reference signal at the measured signal can be separated and the blind spot is reduced by obtaining the difference of the wavelet coefficients for the reference and reflected signals. In the proposed method, the complex wavelet is utilized as a mother wavelet because the reference signal in WTFDR has a complex form. Finally, the computer simulations and the real experiments are carried out to confirm the effectiveness and accuracy of the proposed method.
Methods of compression of digital holograms, based on 1-level wavelet transform
NASA Astrophysics Data System (ADS)
Kurbatova, E. A.; Cheremkhin, P. A.; Evtikhiev, N. N.
2016-08-01
To reduce the size of memory required for storing information about 3D-scenes and to decrease the rate of hologram transmission, digital hologram compression can be used. Compression of digital holograms by wavelet transforms is among most powerful methods. In the paper the most popular wavelet transforms are considered and applied to the digital hologram compression. Obtained values of reconstruction quality and hologram's diffraction efficiencies are compared.
"NONLINEAR DYNAMIC SYSTEMS RESPONSE TO NON-STATIONARY EXCITATION USING THE WAVELET TRANSFORM"
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.
NASA Astrophysics Data System (ADS)
Xu, Li; Tao, Gu
2007-04-01
A hybrid algorithm of using a wavelet transform and a neural network is presented which solves the problems confronted in public watermarking systems. First, to get the wavelet coefficients, db1 wavelet is used to decompose the selected image. Second, to ensure better quality of the watermarked image, some wavelet coefficients and their closely relevant wavelet coefficients are randomly selected from the wavelet coefficients decomposed by the low pass filter and used to establish the relational model by using a neural network. Third, the bit information of the watermark is also enlarged by increasing the amount of zeros or ones and then one bit of the results is embedded by adjusting the polarity between a chosen wavelet coefficient and the output value of the model. Finally, a new image with watermark information is reconstructed by using the modified wavelet coefficients and other unmodified wavelet coefficients. On the other hand, the process of retrieving the watermark is the inverse of the embedding process. The embedded watermark can also be retrieved by using the hybrid algorithm and the restore function without knowing the original image and watermark. Experimental results show that the proposed technique is very robust against some image processing operations and JPEG lossy compression. Meanwhile, the extracted watermark can be proved by the proposed method. Because of the neural network, the proposed method is also robust against attack of false authentication. Therefore, the hybrid algorithm can be used to protect the copyright of one important image.
Cell classification by moments and continuous wavelet transform methods
Chen, Qian; Fan, Yuan; Udpa, Lalita; Ayres, Virginia M
2007-01-01
Image processing techniques are bringing new insights to biomedical research. The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this work, a simple rule-based decision tree classifier is developed to classify typical features of mixed cell types investigated by atomic force microscopy (AFM). A combination of continuous wavelet transform (CWT) and moment-based features are extracted from the AFM data to represent that shape information of different cellular objects at multiple resolution levels. The features are shown to be invariant under operations of translation, rotation, and scaling. The features are then used in a simple rule-based classifier to discriminate between anucleate versus nucleate cell types or to distinguish cells from a fibrous environment such as a tissue scaffold or stint. Since each feature has clear physical meaning, the decision rule of this tree classifier is simple, which makes it very suitable for online processing. Experimental results on AFM data confirm that the performance of this classifier is robust and reliable. PMID:17722546
Classification of Histological Images Based on the Stationary Wavelet Transform
NASA Astrophysics Data System (ADS)
Nascimento, M. Z.; Neves, L.; Duarte, S. C.; Duarte, Y. A. S.; Ramos Batista, V.
2015-01-01
Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.
Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram
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 P 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
Forecasting performance of denoising signal by Wavelet and Fourier Transforms using SARIMA model
NASA Astrophysics Data System (ADS)
Ismail, Mohd Tahir; Mamat, Siti Salwana; Hamzah, Firdaus Mohamad; Karim, Samsul Ariffin Abdul
2014-07-01
The goal of this research is to determine the forecasting performance of denoising signal. Monthly rainfall and monthly number of raindays with duration of 20 years (1990-2009) from Bayan Lepas station are utilized as the case study. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used in this research to find the denoise signal. The denoise data obtained by Fast Fourier Transform and Wavelet Transform are being analyze by seasonal ARIMA model. The best fitted model is determined by the minimum value of MSE. The result indicates that Wavelet Transform is an effective method in denoising the monthly rainfall and number of rain days signals compared to Fast Fourier Transform.
Application of dual tree complex wavelet transform in tandem mass spectrometry.
Murugesan, Selvaraaju; Tay, David B H; Cooke, Ira; Faou, Pierre
2015-08-01
Mass Spectrometry (MS) is a widely used technique in molecular biology for high throughput identification and sequencing of peptides (and proteins). Tandem mass spectrometry (MS/MS) is a specialised mass spectrometry technique whereby the sequence of peptides can be determined. Preprocessing of the MS/MS data is indispensable before performing any statistical analysis on the data. In this work, preprocessing of MS/MS data is proposed based on the Dual Tree Complex Wavelet Transform (DTCWT) using almost symmetric Hilbert pair of wavelets. After the preprocessing step, the identification of peptides is done using the database search approach. The performance of the proposed preprocessing technique is evaluated by comparing its performance against Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT). The preprocessing performed using DTCWT identified more peptides compared to DWT and SWT.
Tensor representation of color images and fast 2D quaternion discrete Fourier transform
NASA Astrophysics Data System (ADS)
Grigoryan, Artyom M.; Agaian, Sos S.
2015-03-01
In this paper, a general, efficient, split algorithm to compute the two-dimensional quaternion discrete Fourier transform (2-D QDFT), by using the special partitioning in the frequency domain, is introduced. The partition determines an effective transformation, or color image representation in the form of 1-D quaternion signals which allow for splitting the N × M-point 2-D QDFT into a set of 1-D QDFTs. Comparative estimates revealing the efficiency of the proposed algorithms with respect to the known ones are given. In particular, a proposed method of calculating the 2r × 2r -point 2-D QDFT uses 18N2 less multiplications than the well-known column-row method and method of calculation based on the symplectic decomposition. The proposed algorithm is simple to apply and design, which makes it very practical in color image processing in the frequency domain.
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. PMID:25571265
Impulse-noise suppression in speech using the stationary wavelet transform.
Nongpiur, R C; Shpak, D J
2013-02-01
An approach for detecting and removing impulse noise from speech using the wavelet transform is proposed. The approach utilizes the multi-resolution property of the wavelet transform, which provides finer time resolution at higher frequencies than the short-time Fourier transform to effectively identify and remove impulse noise. The paper then describes how the impulse-detection performance is dependent on certain wavelet features and their relationships with the impulse noise and the underlying speech signal. Performance comparisons carried out with an existing method show that the wavelet approach yields much better features for detecting the impulses. To remove the impulses, an algorithm that uses the stationary wavelet transform has been developed. The algorithm uses a two-step approach where the wavelet coefficients corresponding to the impulses are suppressed in the first step and then substituted by suitable coefficients located within the vicinity of the impulse in the second step. Performance evaluations with an existing method show that the proposed algorithm gives superior results.
Wavelet transform analysis of electromyography kung fu strikes data.
Neto, Osmar Pinto; Marzullo, Ana Carolina de Miranda
2009-11-01
In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG) analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP) values instead of root mean square (rms) values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF). EMG data of the deltoid anterior (DA), triceps brachii (TB) and brachioradialis (BR) muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023) for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007) for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners. Key PointsThe results show higher muscle activity and lower electromyography median frequencies for strikes with impact compared to strikes without.SSP results presented higher sensitivity and lower inter-subject coefficient of variations than rms results.Kung Fu palm strikes with impact may present better motor units' synchronization than strikes without.
Wavelet Transform Analysis of Electromyography Kung Fu Strikes Data
Neto, Osmar Pinto; Marzullo, Ana Carolina de Miranda
2009-01-01
In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG) analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP) values instead of root mean square (rms) values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF). EMG data of the deltoid anterior (DA), triceps brachii (TB) and brachioradialis (BR) muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023) for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007) for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners. Key Points The results show higher muscle activity and lower electromyography median frequencies for strikes with impact compared to strikes without. SSP results presented higher sensitivity and lower inter-subject coefficient of variations than rms results. Kung Fu palm strikes with impact may present better motor units’ synchronization than strikes without. PMID:24474883
Continuous wavelet transform analysis of acceleration signals measured from a wave buoy.
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
Detection Of Ventricular Late Potentials Using Wavelet Transform And ANT Colony Optimization
NASA Astrophysics Data System (ADS)
Subramanian, A. Sankara; Gurusamy, G.; Selvakumar, G.
2010-10-01
Ventricular late Potentials (VLPs) are low-level high frequency signals that are usually found with in the terminal part of the QRS complex from patients after Myocardial Infraction. Patients with VLPs are at risk of developing Ventricular Tachycardia, which is the major cause of death if patients suffering from heart disease. In this paper the Discrete Wavelet Transform was used to detect VLPs and then ANT colony optimization (ACO) was applied to classify subjects with and without VLPs. A set of Discrete Wavelet Transform (DWT) coefficients is selected from the wavelet decomposition. Three standard parameters of VLPs such as QRST, D40 and V40 are also established. After that a novel clustering algorithm based on Ant Colony Optimization is developed for classifying arrhythmia types. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.
Pattern discrimination of joint transform correlator based on wavelet subband filtering
NASA Astrophysics Data System (ADS)
Lin, Li-Chien; Cheng, Chau-Jern
2004-04-01
We propose and demonstrate a Gabor wavelet prefiltering prior to classical and binarized joint transform correlator implementation to enhance texture features of fingerprints. The frequency- and orientation-selective properties of the wavelet subband filter are utilized to extract important textural features for optimal correlation recognition. A selection criterion for wavelet subbands is derived, and it is shown that the maximum signal-to-noise ratio of the correlator is achieved by optimizing the threshold level. Simulation results show that the proposed method increases the discrimination power of the correlator, especially under noisy environments.
Application of wavelet transforms in terahertz spectroscopy of rough surface targets
NASA Astrophysics Data System (ADS)
Arbab, M. Hassan; Winebrenner, Dale P.; Thorsos, Eric I.; Chen, Antao
2010-02-01
Previously, it has been shown that scattering of terahertz waves by surface roughness of a target can alter the terahertz absorption spectrum and thus obscure the detection of some chemicals in both transmission and reflection geometries. In this paper it is demonstrated that by employing Maximal Overlap Discrete Wavelet Transform (MODWT) coefficients, wavelet-based methods can be used to retrieve spectroscopic information from a broadband terahertz signal reflected from a rough surface target. It is concluded that while the commonly used direct frequency domain deconvolution method fails to accurately characterize and detect the resonance in the dielectric constant of rough surface lactose pellets, wavelet techniques were able to successfully identify such features.
Gopalan, K.; Gopalsami, N.; Bakhtiari, S.; Raptis, A.C.
1995-07-01
This paper reports on wavelet-based decomposition methods and neural networks for remote monitoring of airborne chemicals using millimeter wave spectroscopy. Because of instrumentation noise and the presence of untargeted chemicals, direct decomposition of the spectra requires a large number of training data and yields low accuracy. A neural network trained with features obtained from a discrete wavelet transform is demonstrated to have better decomposition with faster training time. Results based on simulated and experimental spectra are presented to show the efficacy of the wavelet-based methods.
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.
NASA Astrophysics Data System (ADS)
Sharma, K. K.; Jain, Heena
2013-01-01
The security of digital data including images has attracted more attention recently, and many different image encryption methods have been proposed in the literature for this purpose. In this paper, a new image encryption method using wavelet packet decomposition and discrete linear canonical transform is proposed. The use of wavelet packet decomposition and DLCT increases the key size significantly making the encryption more robust. Simulation results of the proposed technique are also presented.
NASA Astrophysics Data System (ADS)
Abrishamchi, A.; Mehdikhani, H.; Tajrishy, M.; Marino, M. A.; Abrishamchi, A.
2007-12-01
Drought forecasting plays an important role in mitigation of economic, environmental and social impacts of drought. Traditional statistical time series methods have a limited ability to capture non-stationarities and nonlinearities in data. Artificial Neural Network (ANN) because of highly flexible function estimator that has self- learning and self-adaptive feature has shown great ability in forecasting nonlinear and nonstationary time series in hydrology. Recently wavelet transforms have become a common tool for analyzing local variation in time series. Wavelet transforms provide a useful decomposition of a signal, or time series; therefore, hybrid models have been proposed for forecasting a time series based on a wavelet transform preprocessing. Wavelet-transformed data aids in improving the ability of forecasting models by diagnosing signal's main frequency component and abstract local information of the original time series on various resolution levels. This paper presents a conjunctive nonlinear model using Wavelet Transforms and Artificial Neural Network. Application of the model in Zayandeh-Rood River basin (Iran) shows that the conjunctive model significantly improves the ability of artificial neural networks for 1, 3, 6 and 9 months ahead forecasting of EDI (effective drought indices) time series. Improved forecasts allow water resources decision makers to develop drought preparedness plans far in advance.
NASA Astrophysics Data System (ADS)
Xue, Wentong; Song, Jianshe; Yuan, Lihai; Shen, Tao
2005-11-01
An efficient and novel imagery compression system for Synthetic Aperture Radar (SAR) which uses integer to integer wavelet transform and Modified Set Partitioning Embedded Block Coder (M-SPECK) has been presented in this paper. The presence of speckle noise, detailed texture, high dynamic range in SAR images, and even its vast data volume show the great differences of SAR imagery. Integer to integer wavelet transform is invertible in finite precision arithmetic, it maps integers to integers, and approximates linear wavelet transforms from which they are derived. Considering in terms of computational load, compression ratio and subjective visual quality metrics, several filter banks are compared together and some factors affecting the compression performance of the integer to integer wavelet transform are discussed in details. Then the optimal filter banks which are more appropriate for the SAR images compression are given. Information of high frequency has relatively larger proportion in SAR images compared with those of nature images. Measures to modify the quantizing thresholds in traditional SPECK are taken, which could be suitable to the contents of SAR imagery for the purpose of compression. Both the integer to integer wavelet transform and modified SPECK have the desirable feature of low computational complexity. Experimental results show its superiority over the traditional approaches in the condition of tradeoffs between compression efficiency and computational complexity.
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.
Detailed resolution of the nonlinear Schrodinger equation using the full adaptive wavelet transform
NASA Astrophysics Data System (ADS)
Stedham, Mark A.; Banerjee, Partha P.
2000-04-01
The propagation of optical pulses in nonlinear optical fibers is described by the nonlinear Schrodinger (NLS) equation. This equation can generally be solved exactly using the inverse scattering method, or for more detailed analysis, through the use of numerical techniques. Perhaps the best known numerical technique for solving he NLS equation is the split-step Fourier method, which effects a solution by assuming that the dispersion and nonlinear effects act independently during pulse propagation along the fiber. In this paper we describe an alternative numerical solution to the NLS equation using an adaptive wavelet transform technique, done entirely in the wavelet domain. This technique differs form previous work involving wavelet solutions tithe NLS equation in that these previous works used a 'split-step wavelet' method in which the linear analysis was performed in the wavelet domain while the nonlinear portion was done in the space domain. Our method takes ful advantage of the set of wavelet coefficients, thus allowing the flexibility to investigate pulse propagation entirely in either the wavelet or the space domain. Additionally, this method is fully adaptive in that it is capable of accurately tracking steep gradients which may occur during the numerical simulation.
Combining 2D synchrosqueezed wave packet transform with optimization for crystal image analysis
NASA Astrophysics Data System (ADS)
Lu, Jianfeng; Wirth, Benedikt; Yang, Haizhao
2016-04-01
We develop a variational optimization method for crystal analysis in atomic resolution images, which uses information from a 2D synchrosqueezed transform (SST) as input. The synchrosqueezed transform is applied to extract initial information from atomic crystal images: crystal defects, rotations and the gradient of elastic deformation. The deformation gradient estimate is then improved outside the identified defect region via a variational approach, to obtain more robust results agreeing better with the physical constraints. The variational model is optimized by a nonlinear projected conjugate gradient method. Both examples of images from computer simulations and imaging experiments are analyzed, with results demonstrating the effectiveness of the proposed method.
Construction of 2D quasi-periodic Rauzy tiling by similarity transformation
Zhuravlev, V. G.; Maleev, A. V.
2009-05-15
A new approach to constructing self-similar fractal tilings is proposed based on the construction of semigroups generated by a finite set of similarity transformations. The Rauzy tiling-a 2D analog of 1D Fibonacci tiling generated by the golden mean-is used as an example to illustrate this approach. It is shown that the Rauzy torus development and the elementary fractal boundary of Rauzy tiling can be constructed in the form of a set of centers of similarity semigroups generated by two and three similarity transformations, respectively. A centrosymmetric tiling, locally dual to the Rauzy tiling, is constructed for the first time and its parameterization is developed.
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.
Directional dual-tree complex wavelet packet transforms for processing quadrature signals.
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.
Noise reduction of FBG sensor signal by using a wavelet transform
NASA Astrophysics Data System (ADS)
Cho, Yo-Han; Song, Minho
2011-05-01
We constructed a FBG (fiber Bragg grating) sensor system based on a fiber-optic Sagnac interferometer. A fiber-optic laser source is used as a strong light source to attain high signal-to-noise ratio. However the unstable output power and coherence noises of the fiber laser made it hard to separate the FBG signals from the interference signals of the fiber coils. To reduce noises and extract FBG sensor signals, we used a Gaussian curve-fitting and a wavelet transform. The wavelet transform is a useful tool for analyzing and denoising output signals. The feasibility of the wavelet transform denoising process is presented with the preliminary experimental results, which showed much better accuracy than the case with only the Gaussian curve-fitting algorithm.
Identification of turbulence structures above a forest canopy using a wavelet transform
NASA Technical Reports Server (NTRS)
Turner, B. J.; Leclerc, M. Y.; Gauthier, M.; Moore, K. E.; Fitzjarrald, D. R.
1994-01-01
The wavelet transform is used to identify scales of large coherent structures present in atmospheric turbulence above the subarctic forest at Schefferville. Individual coherent structures contributing to much of the exchange between the forest and the atmosphere are depicted in terms of both scale and location using contour diagrams of wavelet transform coefficients. Three typical case studies of turbulence and flux observations were selected to examine the physical characteristics of these flux-filled events and their evolution with distance away from the forest canopy. A wavelet transform spectral technique is applied to vertical velocity, temperature, and turbulent heat flux data observed over the sparse coniferous forest to extract the relative importance of each scale present in those data series. The scale of turbulence structures in relation with their characteristic spacing is discussed.
Analysis of photonic Doppler velocimetry data based on the continuous wavelet transform
Liu Shouxian; Wang Detian; Li Tao; Chen Guanghua; Li Zeren; Peng Qixian
2011-02-15
The short time Fourier transform (STFT) cannot resolve rapid velocity changes in most photonic Doppler velocimetry (PDV) data. A practical analysis method based on the continuous wavelet transform (CWT) was presented to overcome this difficulty. The adaptability of the wavelet family predicates that the continuous wavelet transform uses an adaptive time window to estimate the instantaneous frequency of signals. The local frequencies of signal are accurately determined by finding the ridge in the spectrogram of the CWT and then are converted to target velocity according to the Doppler effects. A performance comparison between the CWT and STFT is demonstrated by a plate-impact experiment data. The results illustrate that the new method is automatic and adequate for analysis of PDV data.
[Application of kalman filtering based on wavelet transform in ICP-AES].
Qin, Xia; Shen, Lan-sun
2002-12-01
Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis. PMID:12914186
Impedance cardiography signal denoising using discrete wavelet transform.
Chabchoub, Souhir; Mansouri, Sofienne; Salah, Ridha Ben
2016-09-01
Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal. The Haar, Daubechies (db2, db4, db6, and db8), Symlet (sym2, sym4, sym6, sym8) and Coiflet (coif2, coif3, coif4, coif5) wavelet families are tested and evaluated in order to select the most suitable denoising method. The wavelet family with best performance is compared with two denoising methods: one based on Savitzky-Golay filtering and the other based on median filtering. Each method is evaluated by means of the signal to noise ratio (SNR), the root mean square error (RMSE) and the percent difference root mean square (PRD). The results show that the Daubechies wavelet family (db8) has superior performance on noise reduction in comparison to other methods. PMID:27376722
Wavelet transform: a future of rock fabric analysis?
NASA Astrophysics Data System (ADS)
Gaillot, Philippe; Darrozes, José; Bouchez, Jean-Luc
1999-11-01
Although twenty years ago fabric was defined as "the complete spatial and geometrical configuration of all those components that make up a deformed rock", fabric was mainly synonymous with lattice preferred orientation. Very little attention has been paid to the multi-scale geometrical and spatial relationships of the rock components. Fabric quantification, in terms of size, shape, orientation and location, at all scales, is now performed in two dimensions using anisotropic wavelets. As a first example, the wavelets are applied to the famous colour plate of Sander, and the results compared to his Axial Distribution Analysis. Applied to the K-feldspars of a rock section from the Sidobre granite pluton (Montagne Noire, France), the wavelet analysis shows: (i) alignment of grains, resulting from mechanical interactions between grains, and shows that shearing occurred within the crystalline frame; (ii) preferred grain orientation, the classical grain-shape fabric, parallel to the overall mineral lineation; and (iii) small tensional domains, elongate perpendicular to the lineation, infilled by the residual melt just before total crystallisation, attesting to the stretching nature of the mineral lineation. The future of rock fabric analysis will come from new steps in understanding the processes acting during fabric development along with a further development of wavelet analysis using high resolution three-dimensional fabric data.
Wavelet Transform for Real-Time Detection of Action Potentials in Neural Signals
Quotb, Adam; Bornat, Yannick; Renaud, Sylvie
2011-01-01
We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold. PMID:21811455
Wavelet transform for real-time detection of action potentials in neural signals.
Quotb, Adam; Bornat, Yannick; Renaud, Sylvie
2011-01-01
We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.
Edward Jero, S; Ramu, Palaniappan; Ramakrishnan, S
2014-10-01
ECG Steganography provides secured transmission of secret information such as patient personal information through ECG signals. This paper proposes an approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal. The novelty of the proposed method is to embed the watermark using SVD into the two dimensional (2D) ECG image. The embedding of secret information in a selected sub band of the decomposed ECG is achieved by replacing the singular values of the decomposed cover image by the singular values of the secret data. The performance assessment of the proposed approach allows understanding the suitable sub-band to hide secret data and the signal degradation that will affect diagnosability. Performance is measured using metrics like Kullback-Leibler divergence (KL), percentage residual difference (PRD), peak signal to noise ratio (PSNR) and bit error rate (BER). A dynamic location selection approach for embedding the singular values is also discussed. The proposed approach is demonstrated on a MIT-BIH database and the observations validate that HH is the ideal sub-band to hide data. It is also observed that the signal degradation (less than 0.6%) is very less in the proposed approach even with the secret data being as large as the sub band size. So, it does not affect the diagnosability and is reliable to transmit patient information. PMID:25187409
Edward Jero, S; Ramu, Palaniappan; Ramakrishnan, S
2014-10-01
ECG Steganography provides secured transmission of secret information such as patient personal information through ECG signals. This paper proposes an approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal. The novelty of the proposed method is to embed the watermark using SVD into the two dimensional (2D) ECG image. The embedding of secret information in a selected sub band of the decomposed ECG is achieved by replacing the singular values of the decomposed cover image by the singular values of the secret data. The performance assessment of the proposed approach allows understanding the suitable sub-band to hide secret data and the signal degradation that will affect diagnosability. Performance is measured using metrics like Kullback-Leibler divergence (KL), percentage residual difference (PRD), peak signal to noise ratio (PSNR) and bit error rate (BER). A dynamic location selection approach for embedding the singular values is also discussed. The proposed approach is demonstrated on a MIT-BIH database and the observations validate that HH is the ideal sub-band to hide data. It is also observed that the signal degradation (less than 0.6%) is very less in the proposed approach even with the secret data being as large as the sub band size. So, it does not affect the diagnosability and is reliable to transmit patient information.
NASA Astrophysics Data System (ADS)
Gdeisat, Munther; Burton, David; Lilley, Francis; Lalor, Michael; Moore, Chris
2010-04-01
This paper proposes the use of the two-dimensional continuous Paul wavelet transform to extract the phase of spatial carrier fringe patterns. The proposed algorithm has been tested using computer-generated and real fringe patterns, and these tests have demonstrated the suitability of the proposed technique for the phase demodulation of fringe patterns. Additionally, this algorithm is compared to three two-dimensional continuous wavelet algorithms that have figured prominently in the literature, specifically the Morlet, advanced Morlet and fan mother wavelets. This comparison has revealed that the proposed algorithm outperforms the other three mother wavelets in terms of its suitability for extracting the phase of fringe patterns that exhibit large phase variations.
Coelho, Clarimar José; Galvão, Roberto K H; de Araújo, Mário César U; Pimentel, Maria Fernanda; da Silva, Edvan Cirino
2003-01-01
A novel strategy for the optimization of wavelet transforms with respect to the statistics of the data set in multivariate calibration problems is proposed. The optimization follows a linear semi-infinite programming formulation, which does not display local maxima problems and can be reproducibly solved with modest computational effort. After the optimization, a variable selection algorithm is employed to choose a subset of wavelet coefficients with minimal collinearity. The selection allows the building of a calibration model by direct multiple linear regression on the wavelet coefficients. In an illustrative application involving the simultaneous determination of Mn, Mo, Cr, Ni, and Fe in steel samples by ICP-AES, the proposed strategy yielded more accurate predictions than PCR, PLS, and nonoptimized wavelet regression. PMID:12767151
[Detection of R-wave in Fetal EGG Based on Wavelet Transform and Matched Filtering].
Yan, Wenhong; Jiang, Ning
2015-09-01
By analyzing the characteristics of maternal abdominal ECG (Electrocardiogram), a method based on wavelet transform and matched filtering is proposed to detect the R-wave in fetal EGG (FECG). In this method, the high-frequency coefficients are calculated by using wavelet transform. First, the maternal QRS template is obtained by using the arithmetic mean scheme. Finally, the R-wave of FECG is detected based on matched filtering. The experimental results show that this method can effectively eliminate the noises, such as the maternal ECG signal and baseline drift, enhancing the accuracy of the detection of fetal ECG. PMID:26904869
Eyebrows Identity Authentication Based on Wavelet Transform and Support Vector Machines
NASA Astrophysics Data System (ADS)
Jun-bin, CAO; Haitao, Yang; Lili, Ding
In order to study the novel biometric of eyebrow,,this paper presents an Eyebrows identity authentication based on wavelet transform and support vector machines. The features of the eyebrows image are extracted by wavelet transform, and then classifies them based on SVM. Verification results of the experiment on an eyebrow database taken from 100 of self-built personal demonstrate the effectiveness of the system. The system has a lower FAR 0.22%and FRR 28% Therefore, eyebrow recongnition may possibly apply to personal identification.
[Segmentation of medical images based on dyadic wavelet transform and active contour model].
Li, Hong; Wang, Huinan; Chang, Linfeng; Shao, Xiaoli
2008-12-01
The interference of noise and the weak edge characteristic of symptom information on medical images prevent the traditional methods of segmentation from having good effects. In this paper is proposed a boundary detection method of focus which is based on dyadic wavelet transform and active contour model. In this method, the true edge points are detected by dyadic wavelet transform and linked by improved fast active contour model algorithm. The result of experiment on MRI of brain shows that the method can remove the influence of noise effective and detect the contour of brain tumor actually. PMID:19166191
NASA Astrophysics Data System (ADS)
Zhang, Hongbo; Yi, Xingwen; Chen, Lei; Zhang, Jing; Deng, Mingliang; Qiu, Kun
2012-10-01
As an alternate to fast Fourier transform-based orthogonal frequency-division multiplexing (OFDM), wavelet packet transform (WPT)-based OFDM (WPT-OFDM) does not require cyclic prefix to avoid inter-symbol-interference. The wavelet has many varieties and therefore, it can provide more freedom for system design to suit different applications. We propose a real-valued WPT-OFDM that uses intensity modulation/direct detection. We also conduct an experiment to verify its performance through a 75-km standard single-mode fiber.
Abbate, A; Koay, J; Frankel, J; Schroeder, S C; Das, P
1997-01-01
The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.
A new methodology to map double-cropping croplands based on continuous wavelet transform
NASA Astrophysics Data System (ADS)
Qiu, Bingwen; Zhong, Ming; Tang, Zhenghong; Wang, Chongyang
2014-02-01
Cropping intensity is one of the major factors in crop production and agricultural intensification. A new double-cropping croplands mapping methodology using Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets through continuous wavelet transform was proposed in this study. This methodology involved four steps. First, daily continuous MODIS Enhanced Vegetation Index (EVI) time series datasets were developed for the study year. Next, the EVI time series datasets were transformed into a two dimensional (time-frequency) wavelet scalogram based on continuous wavelet transform. Third, a feature extraction process was conducted on the wavelet scalogram, where the characteristic spectra were calculated from the wavelet scalogram and the feature peak within two skeleton lines was obtained. Finally, a threshold was determined for feature peak values to discriminate double-cropping croplands within each pixel. The application of the proposed procedure to China's Henan Province in 2010 produced an objective and accurate spatial distribution map, which correlated well with in situ observation data (over 90% agreement). The proposed new methodology efficiently handled complex variability that might be caused by regional variation in climate, management practices, growth peaks by winter weed or winter wheat, and data noise. Therefore, the methodology shows promise for future studies at regional and global scales.
Fast multi-scale edge detection algorithm based on wavelet transform
NASA Astrophysics Data System (ADS)
Zang, Jie; Song, Yanjun; Li, Shaojuan; Luo, Guoyun
2011-11-01
The traditional edge detection algorithms have certain noise amplificat ion, making there is a big error, so the edge detection ability is limited. In analysis of the low-frequency signal of image, wavelet analysis theory can reduce the time resolution; under high time resolution for high-frequency signal of the image, it can be concerned about the transient characteristics of the signal to reduce the frequency resolution. Because of the self-adaptive for signal, the wavelet transform can ext ract useful informat ion from the edge of an image. The wavelet transform is at various scales, wavelet transform of each scale provides certain edge informat ion, so called mult i-scale edge detection. Multi-scale edge detection is that the original signal is first polished at different scales, and then detects the mutation of the original signal by the first or second derivative of the polished signal, and the mutations are edges. The edge detection is equivalent to signal detection in different frequency bands after wavelet decomposition. This article is use of this algorithm which takes into account both details and profile of image to detect the mutation of the signal at different scales, provided necessary edge information for image analysis, target recognition and machine visual, and achieved good results.
NASA Astrophysics Data System (ADS)
Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Dawood, Sameer A.; Abdullah, Farah Salwani
2015-05-01
In this paper, three levels of analysis and synthesis filter banks were used to create coefficients for a continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The main property of these wavelet transform schemes is their ability to construct the transmitted signal across a log-normal fading channel over additive white Gaussian noise (AWGN). Wireless rake-receiver structure was chosen as a major application to reduce the inter-symbol interference (ISI) and to minimize the noise. In this work, a new scheme of rake receiver is proposed to receive indoor, multi-path components (MPCs) for ultra-wideband (UWB) wireless communication systems. Rake receivers consist of a continuous wavelet rake (CW-rake) and a discrete wavelet rake (DW-rake), and they use huge bandwidth (7.5 GHz), as reported by the Federal Communications Commission (FCC). The indoor channel models chose for analysis in this research were the non line-of-sight (LOS) channel model (CM4 from 4 to 10 meters) to show the behavior of bit error rate (BER) with respect to signal-to noise ratio (SNR). Two types of rake receiver were used in the simulation, i.e., partial-rake and selective-rake receivers with the maximal ratio combining (MRC) technique to capture the energy of the signal from the output of the rake's fingers.
Wavelet transform approach for fitting financial time series data
NASA Astrophysics Data System (ADS)
Ahmed, Amel Abdoullah; Ismail, Mohd Tahir
2015-10-01
This study investigates a newly developed technique; a combined wavelet filtering and VEC model, to study the dynamic relationship among financial time series. Wavelet filter has been used to annihilate noise data in daily data set of NASDAQ stock market of US, and three stock markets of Middle East and North Africa (MENA) region, namely, Egypt, Jordan, and Istanbul. The data covered is from 6/29/2001 to 5/5/2009. After that, the returns of generated series by wavelet filter and original series are analyzed by cointegration test and VEC model. The results show that the cointegration test affirms the existence of cointegration between the studied series, and there is a long-term relationship between the US, stock markets and MENA stock markets. A comparison between the proposed model and traditional model demonstrates that, the proposed model (DWT with VEC model) outperforms traditional model (VEC model) to fit the financial stock markets series well, and shows real information about these relationships among the stock markets.
Analysis of spike-wave discharges in rats using discrete wavelet transform.
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. PMID:19230874
NASA Astrophysics Data System (ADS)
Santos, C. A. G.; Freire, P. K. M. M.; Silva, G. B. L.; Silva, R. M.
2014-09-01
This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).
CW-THz image contrast enhancement using wavelet transform and Retinex
NASA Astrophysics Data System (ADS)
Chen, Lin; Zhang, Min; Hu, Qi-fan; Huang, Ying-Xue; Liang, Hua-Wei
2015-10-01
To enhance continuous wave terahertz (CW-THz) scanning images contrast and denoising, a method based on wavelet transform and Retinex theory was proposed. In this paper, the factors affecting the quality of CW-THz images were analysed. Second, an approach of combination of the discrete wavelet transform (DWT) and a designed nonlinear function in wavelet domain for the purpose of contrast enhancing was applied. Then, we combine the Retinex algorithm for further contrast enhancement. To evaluate the effectiveness of the proposed method in qualitative and quantitative, it was compared with the adaptive histogram equalization method, the homomorphic filtering method and the SSR(Single-Scale-Retinex) method. Experimental results demonstrated that the presented algorithm can effectively enhance the contrast of CW-THZ image and obtain better visual effect.
On the use of lossless integer wavelet transforms in medical image segmentation
NASA Astrophysics Data System (ADS)
Nagaraj, Nithin; Mallya, Yogish
2005-04-01
Recent trends in medical image processing involve computationally intensive processing techniques on large data sets, especially for 3D applications such as segmentation, registration, volume rendering etc. Multi-resolution image processing techniques have been used in order to speed-up these methods. However, all well-known techniques currently used in multi-resolution medical image processing rely on using Gaussain-based or other equivalent floating point representations that are lossy and irreversible. In this paper, we study the use of Integer Wavelet Transforms (IWT) to address the issue of lossless representation and reversible reconstruction for such medical image processing applications while still retaining all the benefits which floating-point transforms offer such as high speed and efficient memory usage. In particular, we consider three low-complexity reversible wavelet transforms namely the - Lazy-wavelet, the Haar wavelet or (1,1) and the S+P transform as against the Gaussian filter for multi-resolution speed-up of an automatic bone removal algorithm for abdomen CT Angiography. Perfect-reconstruction integer wavelet filters have the ability to perfectly recover the original data set at any step in the application. An additional advantage with the reversible wavelet representation is that it is suitable for lossless compression for purposes of storage, archiving and fast retrieval. Given the fact that even a slight loss of information in medical image processing can be detrimental to diagnostic accuracy, IWTs seem to be the ideal choice for multi-resolution based medical image segmentation algorithms. These could also be useful for other medical image processing methods.
Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.
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.
Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.
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. PMID:25863694
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.
NASA Astrophysics Data System (ADS)
Singh, Hukum
2016-05-01
An optical color image encryption in the Fractional Wavelet Transform (FWT) domain is carried out. The original images are segregated into three colors components: R (red), G (green) and B (blue). After that the components are encrypted separately using double random phase encoding (DRPE) in the FWT domain. Random phase masks (RPMs) are used in the input as well as in Fourier plane. The images to be encrypted are transformed with the discrete wavelet transform (DWT), the resulting coefficients from the DWT are multiplied each one by masks different form RPM. Masks are independent each other and the results are applied an inverse discrete Wavelet Transform (IDWT), obtaining the encrypted images. The input images are recovered from their corresponding encrypted images by using the correct parameters of the FWT, and its digital implementation has been performed using MATLAB 7.6.0 (R2008a). The mother wavelet family and fractional orders associated with the FWT are extra keys that access difficulty an attacker; thereby the scheme is more secure as compared to conventional techniques. The sensitivity of proposed scheme is verified with encryption parameters, occlusions, and noise attacks.
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform
NASA Astrophysics Data System (ADS)
Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang
2011-08-01
Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover
The general 2-D moments via integral transform method for acoustic radiation and scattering
NASA Astrophysics Data System (ADS)
Smith, Jerry R.; Mirotznik, Mark S.
2001-05-01
The moments via integral transform method (MITM) is a technique to analytically reduce the 2-D method of moments (MoM) impedance double integrals into single integrals. By using a special integral representation of the Green's function, the impedance integral can be analytically simplified to a single integral in terms of transformed shape and weight functions. The reduced expression requires fewer computations and reduces the fill times of the MoM impedance matrix. Furthermore, the resulting integral is analytic for nearly arbitrary shape and weight function sets. The MITM technique is developed for mixed boundary conditions and predictions with basic shape and weight function sets are presented. Comparisons of accuracy and speed between MITM and brute force are presented. [Work sponsored by ONR and NSWCCD ILIR Board.
Blind watermark algorithm on 3D motion model based on wavelet transform
NASA Astrophysics Data System (ADS)
Qi, Hu; Zhai, Lang
2013-12-01
With the continuous development of 3D vision technology, digital watermark technology, as the best choice for copyright protection, has fused with it gradually. This paper proposed a blind watermark plan of 3D motion model based on wavelet transform, and made it loaded into the Vega real-time visual simulation system. Firstly, put 3D model into affine transform, and take the distance from the center of gravity to the vertex of 3D object in order to generate a one-dimensional discrete signal; then make this signal into wavelet transform to change its frequency coefficients and embed watermark, finally generate 3D motion model with watermarking. In fixed affine space, achieve the robustness in translation, revolving and proportion transforms. The results show that this approach has better performances not only in robustness, but also in watermark- invisibility.
Audio watermarking robust to geometrical distortions based on dyadic wavelet transform
NASA Astrophysics Data System (ADS)
Wang, Yong; Wu, Shaoquan; Huang, Jiwu
2007-02-01
Geometrical transforms such as time-scale modification (TSM), random removal(RR), random duplication(RD), and cropping, are of common operations on audio signals while presents many challenges to robust audio watermarking. The existing algorithms aiming at solving the geometrical distortions have various drawbacks e.g. high false alarm probability, heavy computation load, small data hiding capacity, and low robustness performance. In this paper an audio watermarking algorithm based on dyadic wavelet transform robust to geometrical distortions is proposed. Watermark synchronization is achieved using the geometrical invariant properties of dyadic wavelet transform. A well-designed coding scheme is proposed for lowering the bit error rate of the watermark. The experimental results show that the watermark is robust to geometrical transforms and other common operations. Compared with other existing algorithms the proposed algorithm has several advantages of high robustness, large data hiding capacity and low computation load.
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. PMID:23041878
Pulse Propagation Effects in Optical 2D Fourier-Transform Spectroscopy: Theory.
Spencer, Austin P; Li, Hebin; Cundiff, Steven T; Jonas, David M
2015-04-30
A solution to Maxwell's equations in the three-dimensional frequency domain is used to calculate rephasing two-dimensional Fourier transform (2DFT) spectra of the D2 line of atomic rubidium vapor in argon buffer gas. Experimental distortions from the spatial propagation of pulses through the sample are simulated in 2DFT spectra calculated for the homogeneous Bloch line shape model. Spectral features that appear at optical densities of up to 3 are investigated. As optical density increases, absorptive and dispersive distortions start with peak shape broadening, progress to peak splitting, and ultimately result in a previously unexplored coherent transient twisting of the split peaks. In contrast to the low optical density limit, where the 2D peak shape for the Bloch model depends only on the total dephasing time, these distortions of the 2D peak shape at finite optical density vary with the waiting time and the excited state lifetime through coherent transient effects. Experiment-specific conditions are explored, demonstrating the effects of varying beam overlap within the sample and of pseudo-time domain filtering. For beam overlap starting at the sample entrance, decreasing the length of beam overlap reduces the line width along the ωτ axis but also reduces signal intensity. A pseudo-time domain filter, where signal prior to the center of the last excitation pulse is excluded from the FID-referenced 2D signal, reduces propagation distortions along the ωt axis. It is demonstrated that 2DFT rephasing spectra cannot take advantage of an excitation-detection transformation that can eliminate propagation distortions in 2DFT relaxation spectra. Finally, the high optical density experimental 2DFT spectrum of rubidium vapor in argon buffer gas [J. Phys. Chem. A 2013, 117, 6279-6287] is quantitatively compared, in line width, in depth of peak splitting, and in coherent transient peak twisting, to a simulation with optical density higher than that reported.
Application Of Continuous Wavelet Transform On Aeromagnetic Data To Identify Volcanic Rocks
NASA Astrophysics Data System (ADS)
Yang, Y.; Li, Y.; Liu, T.
2008-12-01
This paper focuses on the application of continuous wavelet transform on aeromagnetic data, to locate and characterize volcanic rocks. The studied structure is sited in the north centre of the Huanghua depression in the Bohaiwan basin of east China. As channels of magmatism activities, the faults have caused multi-stage magma outpouring and intrusion, forming igneous rocks of different series of strata. As a traditional frequency decomposition method, the discrete wavelet transform is unable to localize frequency variations over time. To handle this problem, the short time Fourier transform method is widely used for the decomposition of non-stationary signals. One problem with this approach is that the fixed width `window function' results in limited resolution. Therefore, the continuous wavelet transform decomposition was used as an alternative approach to overcome this resolution problem. In the continuous wavelet transform, the signal is multiplied with a function similar to a `window function' but the width of the window is not fixed. The time window width is allowed to vary depending upon the frequency that is being considered. As for the magnetic anomalies of igneous rocks, they have different frequencies due to their depths; by analyzing the complex wavelet-based time-frequency characteristics of certain frequencies, we can identify the residual anomalies caused by volcanic rocks in different depths. The theoretical results show that local high frequency spectrum anomalies are the reflection of magnetic sources, and different scales (or different center frequencies) reflect different source depths, with larger scales for deeper sources. Therefore, by analyzing the complex wavelet-based frequency spectrum under different centre frequencies, we can analyze the distribution of magnetic field sources. Then the continuous wavelet transform was applied on the RTP aeromagnetic data of our study area. The data processing results present a detailed description of the
High-contrast active cavitation imaging technique based on multiple bubble wavelet transform.
Lu, Shukuan; Xu, Shanshan; Liu, Runna; Hu, Hong; Wan, Mingxi
2016-08-01
In this study, a unique method that combines the ultrafast active cavitation imaging technique with multiple bubble wavelet transform (MBWT) for improving cavitation detection contrast was presented. The bubble wavelet was constructed by the modified Keller-Miksis equation that considered the mutual effect among bubbles. A three-dimensional spatial model was applied to simulate the spatial distribution of multiple bubbles. The effects of four parameters on the signal-to-noise ratio (SNR) of cavitation images were evaluated, including the following: initial radii of bubbles, scale factor in the wavelet transform, number of bubbles, and the minimum inter-bubble distance. And the other two spatial models and cavitation bubble size distributions were introduced in the MBWT method. The results suggested that in the free-field experiments, the averaged SNR of images acquired by the MBWT method was improved by 7.16 ± 0.09 dB and 3.14 ± 0.14 dB compared with the values of images acquired by the B-mode and single bubble wavelet transform (SBWT) methods. In addition, in the tissue experiments, the averaged cavitation-to-tissue ratio of cavitation images acquired by the MBWT method was improved by 4.69 ± 0.25 dB and 1.74± 0.29 dB compared with that of images acquired by B-mode and SBWT methods.
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.
Efficient local statistical analysis via integral histograms with discrete wavelet transform.
Lee, Teng-Yok; Shen, Han-Wei
2013-12-01
Histograms computed from local regions are commonly used in many visualization applications, and allowing the user to query histograms interactively in regions of arbitrary locations and sizes plays an important role in feature identification and tracking. Computing histograms in regions with arbitrary location and size, nevertheless, can be time consuming for large data sets since it involves expensive I/O and scan of data elements. To achieve both performance- and storage-efficient query of local histograms, we present a new algorithm called WaveletSAT, which utilizes integral histograms, an extension of the summed area tables (SAT), and discrete wavelet transform (DWT). Similar to SAT, an integral histogram is the histogram computed from the area between each grid point and the grid origin, which can be be pre-computed to support fast query. Nevertheless, because one histogram contains multiple bins, it will be very expensive to store one integral histogram at each grid point. To reduce the storage cost for large integral histograms, WaveletSAT treats the integral histograms of all grid points as multiple SATs, each of which can be converted into a sparse representation via DWT, allowing the reconstruction of axis-aligned region histograms of arbitrary sizes from a limited number of wavelet coefficients. Besides, we present an efficient wavelet transform algorithm for SATs that can operate on each grid point separately in logarithmic time complexity, which can be extended to parallel GPU-based implementation. With theoretical and empirical demonstration, we show that WaveletSAT can achieve fast preprocessing and smaller storage overhead than the conventional integral histogram approach with close query performance. PMID:24051836
NASA Astrophysics Data System (ADS)
Liu, Zhiyong; Zhou, Ping; Chen, Gang; Guo, Ledong
2014-11-01
This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition level, and edge effect) were proposed to consider for improving the accuracy of the DWT-SVR model. The performance of DWT-SVR models with different combinations of these three factors was compared with the regular SVR model. The effectiveness of these models was evaluated using the root-mean-squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models. The results demonstrated that the different hybrid models did not always outperform the SVR model for 1-day and 1-month lead time streamflow forecasting. This suggests that it is crucial to consider and compare the three key factors when using the DWT-SVR model (or other machine learning methods coupled with the wavelet transform), rather than choosing them based on personal preferences. We then combined forecasts from multiple candidate DWT-SVR models using a model averaging technique based upon Akaike's information criterion (AIC). This ensemble prediction was superior to the single best DWT-SVR model and regular SVR model for both 1-day and 1-month ahead predictions. With respect to longer lead times (i.e., 2- and 3-day and 2-month), the ensemble predictions using the AIC averaging technique were consistently better than the best DWT-SVR model and SVR model. Therefore, integrating model averaging techniques with the hybrid DWT-SVR model would be a promising approach for daily and monthly streamflow forecasting. Additionally, we strongly recommend considering these three key factors when using wavelet-based SVR models (or other wavelet-based forecasting models).
NASA Astrophysics Data System (ADS)
Elbarghathi, F.; Wang, T.; Zhen, D.; Gu, F.; Ball, A.
2012-05-01
Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.
Coastal hurricane damage assessment via wavelet transform of remotely sensed imagery
NASA Astrophysics Data System (ADS)
Crowsey, Ricky Carl
This dissertation uses post storm imagery processed using wavelet transforms to investigate the capability of wavelet transform-based methods to classify post storm damage of residential areas. Five level Haar, Meyer, Symlets, and Coiflets wavelet transform decompositions of the post storm imagery are inputs to damage classification models of post hurricane and tornado damage. Hurricanes Ike, Rita, Katrina, and Ivan are examined as are the 2011 Joplin and Tuscaloosa tornadoes. Wavelet transform-based classification methods yielded varying classification accuracies for the four hurricanes examined, ranging from 67 percent to 89 percent classification accuracy for classification models informed by samples from the storms classified. Classification accuracies fall when the samples being classified are from a hurricane not informing the classification model, from 17 percent for Rita classified with an Ike-based model, 41 percent for Rita classified with an Ike-Katrina-based model, to 69 percent for Rita classified with an Ike-Katrina-Ivan-based model. The variability within and poor classification accuracy of these models can be attributed to the large variations in the four hurricane events studied and the significant differences in impacted land cover for each of these storms. Classification accuracies improved when these variations were limited via examination of residential areas impacted by 2011 Joplin and Tuscaloosa tornadoes. Damage classification models required as few as nineteen to as many as fifty nine wavelet coefficients to explain the variability in the hurricane storm data samples, and included all four wavelet functions studied. A similar analysis of the tornado damaged areas resulted in a damage classification model with only six wavelet coefficients, four Meyer-based, one Symlets-based and one Haar-based. Classification accuracies ranged from 96 percent for samples included in the model formation to 85 percent for samples not included in the model
An application of wavelet transform for decomposition of millimeter-wave spectroscopic signals
Gopalan, K.; Gopalsami, N.; Bakhtiari, S.; Raptis, A.C.
1994-08-01
Millimeter-wave technique, based on rotational energy transitions of molecules, holds promise for remote monitoring of environmentally hazardous effluents from processes. Argonne National Laboratory is developing a millimeter-wave sensor based on active swept-frequency radar technique in the frequency range of 220-320 GHz. Because the line widths of millimeter-wave spectra of molecules at atmospheric pressure are broad ({approximately} 4 GHz half-width at half height), the composite spectrum of multicomponent mixtures of chemicals is generally complex and overlapping. This paper presents an application of discrete wavelet transform for efficient representation and decomposition of millimeter-wave spectral data. A two-layer back propagation neural network is trained using multifrequency wavelet coefficients of the signals as input features and the known composition of different chemicals in the mixture as target output vectors. After training, composition of an unknown mixture of the base chemicals is determined using the wavelet representation of its absorption spectra. Simulated and experimental spectral data were used to test the wavelet transform technique. Accurate values of individual chemical compositions resulted for noise-free laboratory data. In addition, the technique showed more robustness than conventional multivariate techniques under noisy conditions.
A FPGA system for QRS complex detection based on Integer Wavelet Transform
NASA Astrophysics Data System (ADS)
Stojanović, R.; Karadaglić, D.; Mirković, M.; Milošević, D.
2011-01-01
Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit.
Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink
2015-03-01
This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
Hoang, Vu Dang; Loan, Nguyen Thi; Tho, Vu Thi; Nguyen, Hue Minh Thi
2014-01-01
Signal processing methods based on the use of derivative, Fourier and wavelet transforms were proposed for the spectrophotometric simultaneous determination of cefoperazone and sulbactam in powders for injection. These transforms were successfully applied to UV spectra and ratio spectra to find suitable working wavelengths. Wavelet signal processing was proved to have distinct advantages (i.e. higher peak intensity obtained, additional smooth function and scaling factor process eliminated) over derivative and Fourier transforms. Especially, a better resolution of spectral overlapping bands was obtained by the use of double signal transform in the sequences such as (i) spectra pre-processed by Fractional Wavelet Transform and subsequently subjected to Continuous Wavelet Transform or Discrete Wavelet Transform, and (ii) derivative - wavelet transforms combined. Calibration graphs for cefoperazone and sulbactam were recorded for the range 10-35 mg/L. Good accuracy and precision were reported for all proposed methods by analyzing synthetic mixtures of cefoperazone and sulbactam. Furthermore, these methods were statistically comparable to RP-HPLC.
Malignant transformation in a defined genetic background: proteome changes displayed by 2D-PAGE
2010-01-01
Background Cancer arises from normal cells through the stepwise accumulation of genetic alterations. Cancer development can be studied by direct genetic manipulation within experimental models of tumorigenesis. Thereby, confusion by the genetic heterogeneity of patients can be circumvented. Moreover, identification of the critical changes that convert a pre-malignant cell into a metastatic, therapy resistant tumor cell, however, is one necessary step to develop effective and selective anti-cancer drugs. Thus, for the current study a cell culture model for malignant transformation was used: Primary human fibroblasts of the BJ strain were sequentially transduced with retroviral vectors encoding the genes for hTERT (cell line BJ-T), simian virus 40 early region (SV40 ER, cell line BJ-TE) and H-Ras V12 (cell line BJ-TER). Results The stepwise malignant transformation of human fibroblasts was analyzed on the protein level by differential proteome analysis. We observed 39 regulated protein spots and therein identified 67 different proteins. The strongest change of spot patterns was detected due to integration of SV40 ER. Among the proteins being significantly regulated during the malignant transformation process well known proliferating cell nuclear antigen (PCNA) as well as the chaperones mitochondrial heat shock protein 75 kDa (TRAP-1) and heat shock protein HSP90 were identified. Moreover, we find out, that TRAP-1 is already up-regulated by means of SV40 ER expression instead of H-Ras V12. Furthermore Peroxiredoxin-6 (PRDX6), Annexin A2 (p36), Plasminogen activator inhibitor 2 (PAI-2) and Keratin type II cytoskeletal 7 (CK-7) were identified to be regulated. For some protein candidates we confirmed our 2D-PAGE results by Western Blot. Conclusion These findings give further hints for intriguing interactions between the p16-RB pathway, the mitochondrial chaperone network and the cytoskeleton. In summary, using a cell culture model for malignant transformation analyzed
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
Best tree wavelet packet transform based copyright protection scheme for digital images
NASA Astrophysics Data System (ADS)
Rawat, Sanjay; Raman, Balasubramanian
2012-05-01
In this paper, a dual watermarking scheme based on discrete wavelet transform (DWT), wavelet packet transform (WPT) with best tree, and singular value decomposition (SVD) is proposed. In our algorithm, the cover image is sub-sampled into four sub-images and then two sub-images, having the highest sum of singular values are selected. Two different gray scale images are embedded in the selected sub-images. For embedding first watermark, one of the selected sub-image is decomposed via WPT. The entropy based algorithm is adopted to find the best tree of WPT. Watermark is embedded in all frequency sub-bands of the best tree. For embedding second watermark, l-level discrete wavelet transform (DWT) is performed on the second selected sub-image. The watermark is embedded by modifying the singular values of the transformed image. To enhance the security of the scheme, Zig-Zag scan in applied on the second watermark before embedding. The robustness of the proposed scheme is demonstrated through a series of attack simulations. Experimental results demonstrate that the proposed scheme has good perceptual invisibility and is also robust against various image processing operations, geometric attacks and JPEG Compression.
NASA Astrophysics Data System (ADS)
Kalteh, Aman Mohammad
2013-04-01
Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.
A millimeter wave image fusion algorithm design and optimization based on CDF97 wavelet transform
NASA Astrophysics Data System (ADS)
Yu, Jian-cheng; Chen, Bo-yang; Xia, A.-lin; Liu, Xin-guang
2011-08-01
Millimeter wave imaging technology provides a new detection method for security, fast and safe. But the wave of the images is its own shortcomings, such as noise and low sensitivity. Systems used for security, since only the corresponding specific objects to retain the information, and other information missing, so the actual image is difficult to locate in the millimeter wave . Image fusion approach can be used to effectively solve this problem. People usually use visible and millimeter-wave image fusion. The use of visible image contains the visual information. The fused image can be more convenient site for the detection of concealed weapons and to provide accurate positioning. The integration of information from different detectors, and there are different between the two levels of signal to noise ratio and pixel resolution, so traditional pixel-level fusion methods often cannot satisfy the fusion. Many experts and scholars apply wavelet transform approach to deal with some remote sensing image fusion, and the performance has been greatly improved. Due to these wavelet transform algorithm with complexity and large amount of computation, many algorithms are still in research stage. In order to improve the fusion performance and gain the real-time image fusion, an Integer Wavelet Transform CDF97 based with regional energy enhancement fusion algorithm is proposed in this paper. First, this paper studies of choice of wavelet operator. The paper invites several characteristics to evaluate the performance of wavelet operator used in image fusion. Results show that CDF97 wavelet fusion performance is better than traditional wavelet wavelets such as db wavelet, the vanishing moment longer the better. CDF97 wavelet has good energy concentration characteristic. The low frequency region of the transformed image contains almost the whole image energy. The target in millimeter wave image often has the low-pass characteristics and with a higher energy compare to the ambient
NASA Astrophysics Data System (ADS)
Cao, Guangxi; Xu, Wei
2016-02-01
This paper investigates the nonlinear structure between carbon and energy markets by employing the maximum overlap wavelet transform (MODWT) as well as the multifractal detrended cross-correlation analysis based on maximum overlap wavelet transform (MFDCCA-MODWT). Based on the MODWT multiresolution analysis and the statistic Qcc(m) significance, relatively significant cross-correlations are obtained between carbon and energy future markets either on different time scales or on the whole. The result of the Granger causality test indicates bidirectional Granger causality between carbon and electricity future markets, although the Granger causality relationship between the carbon and oil price is not evident. The existence of multifractality for the returns between carbon and energy markets is proven with the MFDCCA-MODWT algorithm. In addition, results of investigating the origin of multifractality demonstrate that both long-range correlations and fat-tailed distributions play important roles in the contributions of multifractality.
Structural damage detection in pipeline using Lamb waves and wavelet transform
NASA Astrophysics Data System (ADS)
Shoeibi, Fatemeh; Ebrahimi, Afshin; Ghavifekr, Habib Badri
2011-10-01
Over the last few decades, damage identification methods of civil and mechanical structures have been drawing much interest from various fields. This article presented a novel time-frequency based approach for detecting structural damage in pipeline. This is done by applying lamb wave technique and wavelet transforms for detection of change in the structural response data. Lamb waves were propagated through pipes and the received signals were subjected to wavelet transforms. The viability of the proposed approach is demonstrated using simulation examples. The pipes were modeled using finite -element method. This damage detection technique may serve the purpose of structural health monitoring because of long -distance inspection capability in situations where spatially distributed measurement of structural response is difficult.
Determination of curvature and twist by digital shearography and wavelet transforms.
Tay, Cho Jui; Fu, Yu
2005-11-01
A new technique based on digital shearography for determining the transient curvature and twist of a continuously deforming object from a series of speckle patterns is presented. The intensity variation of each pixel is analyzed along the time axis by using a complex Morlet wavelet transform. The absolute sign of the phase variation is determined by introduction of a temporal carrier when the speckle patterns are captured by a high-speed camera. A high-quality spatial distribution of the deflection derivative is extracted at any instant without the need for temporal or spatial phase unwrapping. The continuous Haar wavelet transform is subsequently processed as a differentiation operator to reconstruct the instantaneous curvature and twist of a continuously deforming object. PMID:16279454
A 64-channel neural signal processor/ compressor based on Haar wavelet transform.
Shaeri, Mohammad Ali; Sodagar, Amir M; Abrishami-Moghaddam, Hamid
2011-01-01
A signal processor/compressor dedicated to implantable neural recording microsystems is presented. Signal compression is performed based on Haar wavelet. It is shown in this paper that, compared to other mathematical transforms already used for this purpose, compression of neural signals using this type of wavelet transform can be of almost the same quality, while demanding less circuit complexity and smaller silicon area. Designed in a 0.13-μm standard CMOS process, the 64-channel 8-bit signal processor reported in this paper occupies 113 μm x 110 μm of silicon area. It operates under a 1.8-V supply voltage at a master clock frequency of 3.2 MHz.
Active health system based on wavelet transform analysis of diffracted Lamb waves
NASA Astrophysics Data System (ADS)
Lemistre, Michel B.; Osmont, Daniel L.; Balageas, Daniel L.
2000-08-01
In composite materials, delaminations are discontinuities producing mode conversion processes generating various out-going modes. The Discrete Wavelet Transform allows isolating various propagation modes and extracting them in order to measure the time delay between the arrivals of the main burst and a specific out-going mode, for various propagation paths. This process permits, with a good accuracy, to localize a damage and to estimate its extension. An active health monitoring system composed of integrated disc-shaped, 100 (mu) m-thick and 5 mm-dia PZT transducers working sequentially as actuators and receives is presented. The diagnostic is based on multiresolution process by wavelet transform applied on recorded Lamb wave signals obtained before and after damage. The robustness and portability of this technique is demonstrated by the fact that, after validation in our laboratory it was successfully applied to data coming from an experiment conducted in an other Laboratory using its own Health Monitoring system.
Distributed edge detection algorithm based on wavelet transform for wireless video sensor network
NASA Astrophysics Data System (ADS)
Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng
2010-12-01
Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.
Distributed edge detection algorithm based on wavelet transform for wireless video sensor network
NASA Astrophysics Data System (ADS)
Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng
2011-05-01
Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.
Detecting laser-range-finding signals in surveying converter lining based on wavelet transform
NASA Astrophysics Data System (ADS)
Li, Hongsheng; Yang, Xiaofei; Shi, Tielin; Yang, Shuzi
1998-08-01
The precision of the laser range finding subsystem has important influences on the performances of the whole measurement system applied to survey the steelmaking converter lining erosion state. In the system, the object of laser beams is some rough lighting surfaces in high temperature. the laser range finding signals to reach the microcomputer system would be submerged in intense disturb environments. Common laser range finding devices could not work normally. This paper presents a method based on the wavelet transform to test solving the problem. The idea of this method includes encoding the measuring signals, decomposing the encoded received signals of components in different frequency scales and time domains by the wavelet transform method, extracting the features of encoded signals according to queer points to confirm the arrival of signals, and accurately calculating out the measured distances. In addition, the method is also helpful to adopt some digital filter algorithms in time. It could make further in improvement on the precision.
NASA Astrophysics Data System (ADS)
Chien, Chiang-Ju; Lee, Fu-Shin; Wang, Jhen-Cheng
2007-01-01
For trajectory tracking of a piezoelectric actuator system, an enhanced iterative learning control (ILC) scheme based on wavelet transform filtering (WTF) is proposed in this research. The enhanced ILC scheme incorporates a state compensation in the ILC formula. Combining state compensation with iterative learning, the scheme enhances tracking accuracies substantially, in comparison to the conventional D-type ILC and a proportional control-aided D-type ILC. The wavelet transform is adopted to filter learnable tracking errors without phase shift. Based on both a time-frequency analysis of tracking errors and a convergence bandwidth analysis of ILC, a two-level WTF is chosen for ILC in this study. The enhanced ILC scheme using WTF was applied to track two desired trajectories, one with a single frequency and the other with multiple frequencies, respectively. Experimental results validate the efficacy of the enhanced ILC in terms of the speed of convergence and the level of long-term tracking errors.
Heterogeneities Analysis Using the Generalized Fractal Dimension and Continuous Wavelet Transform
NASA Astrophysics Data System (ADS)
Ouadfeul, S.; Aliouane, L.; Boudella, A.
2012-04-01
The main goal of this work is analyze heterogeneities from well-logs data using the wavelet transform modulus maxima lines (WTMM). Firstly, the continuous wavelet transform (CWT) with sliding window is calculated. The next step consists to calculate the maxima of the modulus of the CWT and estimate the spectrum of exponents. The three generalized fractal dimensions D0, D1 and D2 are then estimated. Application of the proposed idea at the synthetic and real well-logs data of a borehole located in the Algerian Sahara shows that the fractal dimensions are very sensitive to lithological variations. The generalized fractal dimensions are a very robust tool than can be used for petroleum reservoir characterization. Keywrods: reservoir, Heterogeneities, WTMM, fractal dimension.
Winklewski, P J; Gruszecki, M; Wolf, J; Swierblewska, E; Kunicka, K; Wszedybyl-Winklewska, M; Guminski, W; Zabulewicz, J; Frydrychowski, A F; Bieniaszewski, L; Narkiewicz, K
2015-05-01
Pial artery adjustments to changes in blood pressure (BP) may last only seconds in humans. Using a novel method called near-infrared transillumination backscattering sounding (NIR-T/BSS) that allows for the non-invasive measurement of pial artery pulsation (cc-TQ) in humans, we aimed to assess the relationship between spontaneous oscillations in BP and cc-TQ at frequencies between 0.5 Hz and 5 Hz. We hypothesized that analysis of very short data segments would enable the estimation of changes in the cardiac contribution to the BP vs. cc-TQ relationship during very rapid pial artery adjustments to external stimuli. BP and pial artery oscillations during baseline (70s and 10s signals) and the response to maximal breath-hold apnea were studied in eighteen healthy subjects. The cc-TQ was measured using NIR-T/BSS; cerebral blood flow velocity, the pulsatility index and the resistive index were measured using Doppler ultrasound of the left internal carotid artery; heart rate and beat-to-beat systolic and diastolic blood pressure were recorded using a Finometer; end-tidal CO2 was measured using a medical gas analyzer. Wavelet transform analysis was used to assess the relationship between BP and cc-TQ oscillations. The recordings lasting 10s and representing 10 cycles with a frequency of ~1 Hz provided sufficient accuracy with respect to wavelet coherence and wavelet phase coherence values and yielded similar results to those obtained from approximately 70cycles (70s). A slight but significant decrease in wavelet coherence between augmented BP and cc-TQ oscillations was observed by the end of apnea. Wavelet transform analysis can be used to assess the relationship between BP and cc-TQ oscillations at cardiac frequency using signals intervals as short as 10s. Apnea slightly decreases the contribution of cardiac activity to BP and cc-TQ oscillations. PMID:25804326
Khaloo, Shokooh S; Ensafi, Ali A; Khayamian, T
2007-01-15
A new method is proposed for the determination of bismuth and copper in the presence of each other based on adsorptive stripping voltammetry of complexes of Bi(III)-chromazorul-S and Cu(II)-chromazorul-S at a hanging mercury drop electrode (HMDE). Copper is an interfering element for the determination of Bi(III) because, the voltammograms of Bi(III) and Cu(II) overlapped with each other. Continuous wavelet transform (CWT) was applied to separate the voltammograms. In this regards, wavelet filter, resolution of the peaks and the fitness were optimized to obtain minimum detection limit for the elements. Through continuous wavelet transform Symlet4 (Sym4) wavelet filter at dilation 6, quantitative and qualitative analysis the mixture solutions of bismuth and copper was performed. It was also realized that copper imposes a matrix effect on the determination of Bi(III) and the standard addition method was able to cope with this effect. Bismuth does not have matrix effect on copper determination, therefore, the calibration curve using wavelet coefficients of CWT was used for determination of Cu(II) in the presence of Bi(III). The detection limits were 0.10 and 0.05ngml(-1) for bismuth and copper, respectively. The linear dynamic range of 0.1-30.0 and 0.1-32.0ngml(-1) were obtained for determination of bismuth in the presence of 24.0ngml(-1) of copper and copper in the presence of 24.0ngml(-1) of bismuth, respectively. The method was used for determination of these two cations in water and human hair samples. The results indicate the ability of method for the determination of these two elements in real samples. PMID:19071307
Solenoid and non-solenoid protein recognition using stationary wavelet packet transform
Vo, An; Nguyen, Nha; Huang, Heng
2010-01-01
Motivation: Solenoid proteins are emerging as a protein class with properties intermediate between structured and intrinsically unstructured proteins. Containing repeating structural units, solenoid proteins are expected to share sequence similarities. However, in many cases, the sequence similarities are weak and non-detectable. Moreover, solenoids can be degenerated and widely vary in the number of units. So that it is difficult to detect them. Recently, several solenoid repeats detection methods have been proposed, such as self-alignment of the sequence, spectral analysis and discrete Fourier transform of sequence. Although these methods have shown good performance on certain data sets, they often fail to detect repeats with weak similarities. In this article, we propose a new approach to recognize solenoid repeats and non-solenoid proteins using stationary wavelet packet transform (SWPT). Our method associates with three advantages: (i) naturally representing five main factors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavelet features that can capture hidden components from solenoid sequence similarities and distinguish them from global proteins; (iii) obtaining statistics features that capture repeating motifs of solenoid proteins. Results: Our method analyzes the characteristics of amino acid sequence in both spectral and temporal domains using SWPT. Both global and local information of proteins are captured by SWPT coefficients. We obtain and integrate wavelet-based features and statistics-based features of amino acid sequence to improve the classification task. Our proposed method is evaluated by comparing to state-of-the-art methods such as HHrepID and REPETITA. The experimental results show that our algorithm consistently outperforms them in areas under ROC curve. At the same false positive rate, the sensitivity of our WAVELET method is higher than other methods. Availability: http
Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series
NASA Astrophysics Data System (ADS)
Aboofazeli, Mohammad; Abolmaesumi, Purang; Moradi, Mehdi; Sauerbrei, Eric; Siemens, Robert; Boag, Alexander; Mousavi, Parvin
2009-02-01
The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features.
The wavelet transform and the suppression theory of binocular vision for stereo image compression
Reynolds, W.D. Jr; Kenyon, R.V.
1996-08-01
In this paper a method for compression of stereo images. The proposed scheme is a frequency domain approach based on the suppression theory of binocular vision. By using the information in the frequency domain, complex disparity estimation techniques can be avoided. The wavelet transform is used to obtain a multiresolution analysis of the stereo pair by which the subbands convey the necessary frequency domain information.
Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
Huang, Heng; Nguyen, Nha; Oraintara, Soontorn; Vo, An
2008-01-01
Background Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model. Results In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border. Conclusion We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches. PMID:18831782
A frequency measurement algorithm for non-stationary signals by using wavelet transform
NASA Astrophysics Data System (ADS)
Seo, Seong-Heon; Oh, Dong Keun
2016-11-01
Scalogram is widely used to measure instantaneous frequencies of non-stationary signals. However, the basic property of the scalogram is observed only for stationary sinusoidal functions. A property of the scalogram for non-stationary signal is analytically derived in this paper. Based on the property, a new frequency measurement algorithm is proposed. In addition, a filter that can separate two similar frequency signals is developed based on the wavelet transform.
Estimation of the Tool Condition by Applying the Wavelet Transform to Acoustic Emission Signals
Gomez, M. P.; Piotrkowski, R.; Ruzzante, J. E.; D'Attellis, C. E.
2007-03-21
This work follows the search of parameters to evaluate the tool condition in machining processes. The selected sensing technique is acoustic emission and it is applied to a turning process of steel samples. The obtained signals are studied using the wavelet transformation. The tool wear level is quantified as a percentage of the final wear specified by the Standard ISO 3685. The amplitude and relevant scale obtained of acoustic emission signals could be related with the wear level.
Computational algorithms for discrete wavelet transform using fixed-size filter matrices
NASA Astrophysics Data System (ADS)
Baraniecki, Anna Z.; Karim, Salahadin O.
1992-07-01
This paper describes matrix based algorithms for computing wavelet transform representations with application to multiresolution analysis. Structure of the algorithm presented is well suited for programming purpose and also for the implementation on VLSI processors. By using overlap-add or overlap-save techniques, constant matrix size can be used to accommodate arbitrary data lengths. Performance of the algorithm described in this paper is illustrated by decomposing an image into details and smoothed components.
Automatic measure of the split in the second cardiac sound by using the wavelet transform technique.
Debbal, S M; Bereksi-Reguig, F
2007-03-01
This paper is concerned with the identification and automatic measure of the split in the second heart sound (S2) of the phonocardiogram signal (PCGs) for normal or pathological case. The second heart sound S2 consists of two acoustic components A2 and P2, the former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as "split". A automatic technique based on the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT) is developed in this paper to measure the split of the second cardiac sound (S2) for the normal and pathological cases of the PCG signals. To quantify the splitting, the two components in S2 (i.e. A2 and P2) are identified and, the delay between the two components can be estimated. It is shown that the wavelet transform can provide best information and features of the split of S2 and the major components (A2 and P2) and consequently aid in medical diagnosis.
Neural network and wavelet transform for scale-invariant data classification
NASA Astrophysics Data System (ADS)
Szu, Harold H.; Yang, Xiang-Yang; Telfer, Brian A.; Sheng, Yunlong
1993-08-01
Given an astrophysical observation with an arbitrary carrier frequency and an unknown scale under an additive white noise, s'(t)≡s(αt)+n(t), its wavelet transform is W'(a,b)≡(s'(t),hab(t)), as computed by the inner product with a daughter wavelet hab(t)≡h((t-b)/a)/a. W'(a,b) equals the original transform W(a,b)≡(s(t),hab(t)) displaced along the radial direction W'(a,b)=W(αa,αb) plus noise in the time-scale joint-representation plane. A bank of wedge-shaped detectors collects those displaced transforms W'(a,b) to create a set of invariant features. These features are fed into a two-layer feed-forward artificial neural network, to interpolate discrete sampling, as demonstrated successfully for real-time-signal automatic classification. Useful wavelet applications in turbulence onset, spectrum analyses, fractal aggregates, and bubble-chamber particle-track pattern-recognition problems are indicated but are modeled, in the interest of simplicity, in a one-dimensional example.
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.
Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features
NASA Astrophysics Data System (ADS)
Rezaeian, A.; Homayouni, S.; Safari, A.
2015-12-01
Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.
Tokmakçi, Mahmut; Erdoğan, Nuri
2009-05-01
In this paper, the effects of a wavelet transform based denoising strategy on clinical Doppler parameters are analyzed. The study scheme included: (a) Acquisition of arterial and venous Doppler signals by sampling the audio output of an ultrasound scanner from 20 healthy volunteers, (b) Noise reduction via decomposition of the signals through discrete wavelet transform, (c) Spectral analysis of noisy and noise-free signals with short time Fourier transform, (d) Curve fitting to spectrograms, (e) Calculation of clinical Doppler parameters, (f) Statistical comparison of parameters obtained from noisy and noise-free signals. The decomposition level was selected as the highest level at which the maximum power spectral density and its corresponding frequency were preserved. In all subjects, noise-free spectrograms had smoother trace with less ripples. In both arterial and venous spectrograms, denoising resulted in a significant decrease in the maximum (systolic) and mean frequency, with no statistical difference in the minimum (diastolic) frequency. In arterial signals, this leads to a significant decrease in the calculated parameters such as Systolic/Diastolic Velocity Ratio, Resistivity Index, Pulsatility Index and Acceleration Time. Acceleration Index did not change significantly. Despite a successful denoising, the effects of wavelet decomposition on high frequency components in the Doppler signal should be challenged by comparison with reference data, or, through clinical investigations. PMID:19470316
NASA Astrophysics Data System (ADS)
Yang, Wei; Yang, Yuanhong; Yang, Mingwei
2014-06-01
We propose a fast digital envelope detector (DED) based on the generalized harmonic wavelet transform to improve the performance of coherent heterodyne Brillouin optical time domain reflectometry. The proposed DED can obtain undistorted envelopes due to the zero phase-shift ideal bandpass filter (BPF) characteristics of the generalized harmonic wavelet (GHW). Its envelope average ability benefits from the passband designing flexibility of the GHW, and its demodulation speed can be accelerated by using a fast algorithm that only analyses signals of interest within the passband of the GHW with reduced computational complexity. The feasibility and advantage of the proposed DED are verified by simulations and experiments. With an optimized bandwidth, Brillouin frequency shift accuracy improvements of 19.4% and 11.14%, as well as envelope demodulation speed increases of 39.1% and 24.9%, are experimentally attained by the proposed DED over Hilbert transform (HT) and Morlet wavelet transform (MWT) based DEDs, respectively. Spatial resolution by the proposed DED is undegraded, which is identical to the undegraded value by HT-DED with an allpass filter characteristic and better than the degraded value by MWT-DED with a Gaussian BPF characteristic.
ECG compression using non-recursive wavelet transform with quality control
NASA Astrophysics Data System (ADS)
Liu, Je-Hung; Hung, King-Chu; Wu, Tsung-Ching
2016-09-01
While wavelet-based electrocardiogram (ECG) data compression using scalar quantisation (SQ) yields excellent compression performance, a wavelet's SQ scheme, however, must select a set of multilevel quantisers for each quantisation process. As a result of the properties of multiple-to-one mapping, however, this scheme is not conducive for reconstruction error control. In order to address this problem, this paper presents a single-variable control SQ scheme able to guarantee the reconstruction quality of wavelet-based ECG data compression. Based on the reversible round-off non-recursive discrete periodised wavelet transform (RRO-NRDPWT), the SQ scheme is derived with a three-stage design process that first uses genetic algorithm (GA) for high compression ratio (CR), followed by a quadratic curve fitting for linear distortion control, and the third uses a fuzzy decision-making for minimising data dependency effect and selecting the optimal SQ. The two databases, Physikalisch-Technische Bundesanstalt (PTB) and Massachusetts Institute of Technology (MIT) arrhythmia, are used to evaluate quality control performance. Experimental results show that the design method guarantees a high compression performance SQ scheme with statistically linear distortion. This property can be independent of training data and can facilitate rapid error control.
Mouse EEG spike detection based on the adapted continuous wavelet transform
NASA Astrophysics Data System (ADS)
Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.
2016-04-01
Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.
Sun, Shao-bo; Du, Hua-qiangl; Li, Ping-heng; Zhou, Guo-mo; Xu, Xiao-juni; Gao, Guo-long; Li, Xue-jian
2016-01-01
This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data. PMID:27228592
High-contrast active cavitation imaging technique based on multiple bubble wavelet transform.
Lu, Shukuan; Xu, Shanshan; Liu, Runna; Hu, Hong; Wan, Mingxi
2016-08-01
In this study, a unique method that combines the ultrafast active cavitation imaging technique with multiple bubble wavelet transform (MBWT) for improving cavitation detection contrast was presented. The bubble wavelet was constructed by the modified Keller-Miksis equation that considered the mutual effect among bubbles. A three-dimensional spatial model was applied to simulate the spatial distribution of multiple bubbles. The effects of four parameters on the signal-to-noise ratio (SNR) of cavitation images were evaluated, including the following: initial radii of bubbles, scale factor in the wavelet transform, number of bubbles, and the minimum inter-bubble distance. And the other two spatial models and cavitation bubble size distributions were introduced in the MBWT method. The results suggested that in the free-field experiments, the averaged SNR of images acquired by the MBWT method was improved by 7.16 ± 0.09 dB and 3.14 ± 0.14 dB compared with the values of images acquired by the B-mode and single bubble wavelet transform (SBWT) methods. In addition, in the tissue experiments, the averaged cavitation-to-tissue ratio of cavitation images acquired by the MBWT method was improved by 4.69 ± 0.25 dB and 1.74± 0.29 dB compared with that of images acquired by B-mode and SBWT methods. PMID:27586732
Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform
2012-01-01
Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202
Feng, Ziang; Gao, Zhan; Zhang, Xiaoqiong; Wang, Shengjia; Yang, Dong; Yuan, Hao; Qin, Jie
2015-09-01
Digital shearing speckle pattern interferometry (DSSPI) has been recognized as a practical tool in testing strain. The DSSPI system which is based on temporal analysis is attractive because of its ability to measure strain dynamically. In this paper, such a DSSPI system with Wollaston prism has been built. The principles and system arrangement are described and the preliminary experimental result of the displacement-derivative test of an aluminum plate is shown with the wavelet transformation method and the Fourier transformation method. The simulations have been conducted with the finite element method. The comparison of the results shows that quantitative measurement of displacement-derivative has been realized.
Feng, Ziang; Gao, Zhan; Zhang, Xiaoqiong; Wang, Shengjia; Yang, Dong; Yuan, Hao; Qin, Jie
2015-09-01
Digital shearing speckle pattern interferometry (DSSPI) has been recognized as a practical tool in testing strain. The DSSPI system which is based on temporal analysis is attractive because of its ability to measure strain dynamically. In this paper, such a DSSPI system with Wollaston prism has been built. The principles and system arrangement are described and the preliminary experimental result of the displacement-derivative test of an aluminum plate is shown with the wavelet transformation method and the Fourier transformation method. The simulations have been conducted with the finite element method. The comparison of the results shows that quantitative measurement of displacement-derivative has been realized. PMID:26429424
Transforming 2d Cadastral Data Into a Dynamic Smart 3d Model
NASA Astrophysics Data System (ADS)
Tsiliakou, E.; Labropoulos, T.; Dimopoulou, E.
2013-08-01
3D property registration has become an imperative need in order to optimally reflect all complex cases of the multilayer reality of property rights and restrictions, revealing their vertical component. This paper refers to the potentials and multiple applications of 3D cadastral systems and explores the current state-of-the art, especially the available software with which 3D visualization can be achieved. Within this context, the Hellenic Cadastre's current state is investigated, in particular its data modeling frame. Presenting the methodologies and specifications addressing the registration of 3D properties, the operating cadastral system's shortcomings and merits are pointed out. Nonetheless, current technological advances as well as the availability of sophisticated software packages (proprietary or open source) call for 3D modeling. In order to register and visualize the complex reality in 3D, Esri's CityEngine modeling software has been used, which is specialized in the generation of 3D urban environments, transforming 2D GIS Data into Smart 3D City Models. The application of the 3D model concerns the Campus of the National Technical University of Athens, in which a complex ownership status is established along with approved special zoning regulations. The 3D model was built using different parameters based on input data, derived from cadastral and urban planning datasets, as well as legal documents and architectural plans. The process resulted in a final 3D model, optimally describing the cadastral situation and built environment and proved to be a good practice example of 3D visualization.
Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review
NASA Astrophysics Data System (ADS)
Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia
2016-03-01
As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.
NASA Astrophysics Data System (ADS)
Chouakri, S. A.; Djaafri, O.; Taleb-Ahmed, A.
2013-08-01
We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.
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.
An efficient and robust 3D mesh compression based on 3D watermarking and wavelet transform
NASA Astrophysics Data System (ADS)
Zagrouba, Ezzeddine; Ben Jabra, Saoussen; Didi, Yosra
2011-06-01
The compression and watermarking of 3D meshes are very important in many areas of activity including digital cinematography, virtual reality as well as CAD design. However, most studies on 3D watermarking and 3D compression are done independently. To verify a good trade-off between protection and a fast transfer of 3D meshes, this paper proposes a new approach which combines 3D mesh compression with mesh watermarking. This combination is based on a wavelet transformation. In fact, the used compression method is decomposed to two stages: geometric encoding and topologic encoding. The proposed approach consists to insert a signature between these two stages. First, the wavelet transformation is applied to the original mesh to obtain two components: wavelets coefficients and a coarse mesh. Then, the geometric encoding is done on these two components. The obtained coarse mesh will be marked using a robust mesh watermarking scheme. This insertion into coarse mesh allows obtaining high robustness to several attacks. Finally, the topologic encoding is applied to the marked coarse mesh to obtain the compressed mesh. The combination of compression and watermarking permits to detect the presence of signature after a compression of the marked mesh. In plus, it allows transferring protected 3D meshes with the minimum size. The experiments and evaluations show that the proposed approach presents efficient results in terms of compression gain, invisibility and robustness of the signature against of many attacks.
Determination of human EEG alpha entrainment ERD/ERS using the continuous complex wavelet transform
NASA Astrophysics Data System (ADS)
Chorlian, David B.; Porjesz, Bernice; Begleiter, Henri
2003-04-01
Alpha entrainment caused by exposure to a background stimulus continuously flickering at a rate of 8 1/3 Hz was affected by the appearance of a foreground target stimulus to which the subjects were requested to press a button. With the use of bipolar derivations (to reduce volume conduction effects), scalp recorded EEG potentials were subjected to a continuous wavelet transform using complex Morlet wavelets at a range of scales. Complex Morlet wavelets were used to calculate efficiently instantaneous amplitudes and phases on a per-trial basis, rather than using the Hilbert transform on band-pass filtered data. Multiple scales were employed to contrast the pattern of alpha activity with those in other bands, and to determine whether the harmonics observed in the spectral analysis of the data were simply a result of the non-sinusoidal response to the entraining signal or a distinct neural phenomenon. We were thus able to calculate desynchronization/resynchronization for both the entrained and non-entrained alpha activity. The occurance of the target stimulus caused a sharp increase in amplitude in both the entrained and non-entrained alpha activity, followed by a sharp decrease, and then a return to baseline, over a period of 2.5 seconds. However, the entrained alpha activity showed a much more rapid recovery than non-entrained activity.
Adaptive Threshold Neural Spike Detector Using Stationary Wavelet Transform in CMOS.
Yang, Yuning; Boling, C Sam; Kamboh, Awais M; Mason, Andrew J
2015-11-01
Spike detection is an essential first step in the analysis of neural recordings. Detection at the frontend eases the bandwidth requirement for wireless data transfer of multichannel recordings to extra-cranial processing units. In this work, a low power digital integrated spike detector based on the lifting stationary wavelet transform is presented and developed. By monitoring the standard deviation of wavelet coefficients, the proposed detector can adaptively set a threshold value online for each channel independently without requiring user intervention. A prototype 16-channel spike detector was designed and tested in an FPGA. The method enables spike detection with nearly 90% accuracy even when the signal-to-noise ratio is as low as 2. The design was mapped to 130 nm CMOS technology and shown to occupy 0.014 mm(2) of area and dissipate 1.7 μW of power per channel, making it suitable for implantable multichannel neural recording systems. PMID:25955990
A hybrid group method of data handling with discrete wavelet transform for GDP forecasting
NASA Astrophysics Data System (ADS)
Isa, Nadira Mohamed; Shabri, Ani
2013-09-01
This study is proposed the application of hybridization model using Group Method of Data Handling (GMDH) and Discrete Wavelet Transform (DWT) in time series forecasting. The objective of this paper is to examine the flexibility of the hybridization GMDH in time series forecasting by using Gross Domestic Product (GDP). A time series data set is used in this study to demonstrate the effectiveness of the forecasting model. This data are utilized to forecast through an application aimed to handle real life time series. This experiment compares the performances of a hybrid model and a single model of Wavelet-Linear Regression (WR), Artificial Neural Network (ANN), and conventional GMDH. It is shown that the proposed model can provide a promising alternative technique in GDP forecasting.
NASA Astrophysics Data System (ADS)
Gu, Junhua; Xu, Haiguang; Wang, Jingying; An, Tao; Chen, Wen
2013-08-01
We propose a continuous wavelet transform based non-parametric foreground subtraction method for the detection of redshifted 21 cm signal from the epoch of reionization. This method works based on the assumption that the foreground spectra are smooth in frequency domain, while the 21 cm signal spectrum is full of saw-tooth-like structures, thus their characteristic scales are significantly different. We can distinguish them in the wavelet coefficient space easily and perform the foreground subtraction. Compared with the traditional spectral fitting based method, our method is more tolerant to complex foregrounds. Furthermore, we also find that when the instrument has uncorrected response error, our method can also work significantly better than the spectral fitting based method. Our method can obtain similar results with the Wp smoothing method, which is also a non-parametric method, but our method consumes much less computing time.
Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform
NASA Astrophysics Data System (ADS)
Zhang, ChunLin; Li, Bing; Chen, BinQiang; Cao, HongRui; Zi, YanYang; He, ZhengJia
2015-12-01
Extracting and revealing the weak periodic fault vibration impulses is reasonable for damage detection of rotating machinery. However, the widely used dyadic WT suffers fixed frequency partition manner and low oscillating bases which would weaken its performance in weak fault detection. A new method based on flexible analytic wavelet transform (FAWT) is proposed in this article. Employing fractional and arbitrary scaling and translation factors, FAWT possesses attractive properties such as flexible time-frequency (TF) covering manner, better shift-invariance and tunable oscillatory nature of the bases, offering proper wavelet frame and bases shape to match the weak fault components. Moreover, FAWT is effective in revealing the amplitude modulation feature of the periodic fault impulses that occur in some damaged rotating components. The applications to a rolling bearing, a planetary gearbox of, and a flue gas turbine unit show that the proposed method is effective in extracting weak impulsive fault signature.
Wavelet Transform Of Acoustic Signal From A Ranque- Hilsch Vortex Tube
NASA Astrophysics Data System (ADS)
Istihat, Y.; Wisnoe, W.
2015-09-01
This paper presents the frequency analysis of flow in a Ranque-Hilsch Vortex Tube (RHVT) obtained from acoustic signal using microphones in an isolated formation setup. Data Acquisition System (DAS) that incorporates Analog to Digital Converter (ADC) with laptop computer has been used to acquire the wave data. Different inlet pressures (20, 30, 40, 50 and 60 psi) are supplied and temperature differences are recorded. Frequencies produced from a RHVT are experimentally measured and analyzed by means of Wavelet Transform (WT). Morlet Wavelet is used and relation between Pressure variation, Temperature and Frequency are studied. Acoustic data has been analyzed using Matlab® and time-frequency analysis (Scalogram) is presented. Results show that the Pressure is proportional with the Frequency inside the RHVT whereby two distinct working frequencies is pronounced in between 4-8 kHz.
Implantable neural spike detection using lifting-based stationary wavelet transform.
Yang, Yuning; Mason, Andrew J
2011-01-01
Spike detection from high data rate neural recordings is desired to ease the bandwidth bottleneck of bio-telemetry. An appropriate spike detection method should be able to detect spikes under low signal-to-noise ratio (SNR) while meeting the power and area constraints of implantation. This paper introduces a spike detection system utilizing lifting-based stationary wavelet transform (SWT) that decomposes neural signals into 2 levels using 'symmlet2' wavelet basis. This approach enables accurate spike detection down to an SNR of only 2. The lifting-based SWT architecture permits a hardware implementation consuming only 6.6 μW power and 0.07 mm(2) area for 32 channels with 3.2 MHz master clock.
Adaptive Threshold Neural Spike Detector Using Stationary Wavelet Transform in CMOS.
Yang, Yuning; Boling, C Sam; Kamboh, Awais M; Mason, Andrew J
2015-11-01
Spike detection is an essential first step in the analysis of neural recordings. Detection at the frontend eases the bandwidth requirement for wireless data transfer of multichannel recordings to extra-cranial processing units. In this work, a low power digital integrated spike detector based on the lifting stationary wavelet transform is presented and developed. By monitoring the standard deviation of wavelet coefficients, the proposed detector can adaptively set a threshold value online for each channel independently without requiring user intervention. A prototype 16-channel spike detector was designed and tested in an FPGA. The method enables spike detection with nearly 90% accuracy even when the signal-to-noise ratio is as low as 2. The design was mapped to 130 nm CMOS technology and shown to occupy 0.014 mm(2) of area and dissipate 1.7 μW of power per channel, making it suitable for implantable multichannel neural recording systems.
NASA Astrophysics Data System (ADS)
Palaniswamy, Sumithra; Duraisamy, Prakash; Alam, Mohammad Showkat; Yuan, Xiaohui
2012-04-01
Automatic speech processing systems are widely used in everyday life such as mobile communication, speech and speaker recognition, and for assisting the hearing impaired. In speech communication systems, the quality and intelligibility of speech is of utmost importance for ease and accuracy of information exchange. To obtain an intelligible speech signal and one that is more pleasant to listen, noise reduction is essential. In this paper a new Time Adaptive Discrete Bionic Wavelet Thresholding (TADBWT) scheme is proposed. The proposed technique uses Daubechies mother wavelet to achieve better enhancement of speech from additive non- stationary noises which occur in real life such as street noise and factory noise. Due to the integration of human auditory system model into the wavelet transform, bionic wavelet transform (BWT) has great potential for speech enhancement which may lead to a new path in speech processing. In the proposed technique, at first, discrete BWT is applied to noisy speech to derive TADBWT coefficients. Then the adaptive nature of the BWT is captured by introducing a time varying linear factor which updates the coefficients at each scale over time. This approach has shown better performance than the existing algorithms at lower input SNR due to modified soft level dependent thresholding on time adaptive coefficients. The objective and subjective test results confirmed the competency of the TADBWT technique. The effectiveness of the proposed technique is also evaluated for speaker recognition task under noisy environment. The recognition results show that the TADWT technique yields better performance when compared to alternate methods specifically at lower input SNR.
NASA Astrophysics Data System (ADS)
Gaillot, P.; Bardaine, T.; Lyon-Caen, H.
2004-12-01
Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro
NASA Astrophysics Data System (ADS)
Jones, J. P.; Carniel, R.; Malone, S.
2005-12-01
The time-varying properties of volcanic tremor demand advanced techniques capable of analyzing changes in both time and frequency domains. Specifically, rapid data preprocessing techniques with the ability to distinguish signal from noise are especially valuable in analyzing the temporal, spatial, and spectral properties of these signals. To this end, we use the Discrete Wavelet Packet Transform and the Best Shift Basis algorithm to select an orthonormal basis for continuous volcanic tremor data, then apply a simple statistical test to eliminate frequency bands that primarily consist of Gaussian white noise. We then use the Maximal Overlap Discrete Wavelet Packet Transform to compute and analyze features in the detail coefficients of each "signal" band. Because MODWPT detail coefficients are equivalent to a time series convolved with a zero phase filter, we apply standard polarization and amplitude-based location techniques to each frequency band's detail coefficients to analyze possible source locations and mechanisms. To demonstrate the usefulness of these techniques, we present a sample analysis of data from Erta 'Ale volcano, Ethiopia, recorded on a temporary network in November 2003. Data were sampled at 100 Hz and the DWPT was computed with the LA(16) wavelet to a maximum level of j = 7. The optimal basis for this data set consists of 54 frequency bands, but only 9 contain meaningful "signal" energy. We identify two frequency bands whose locations suggest a distributed source; three frequency bands whose signals may come from the lava lake itself; three high-frequency bands of scattered energy; and one very high frequency band of non-Gaussian instrument noise. Finally, we discuss optimization efforts, computational efficiency, and the feasibility of using similar wavelet methods to preprocess data in real time or near real time.
Montazery-Kordy, Hussain; Miran-Baygi, Mohammad Hossein; Moradi, Mohammad Hassan
2008-01-01
Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power. PMID:18988305
ECG compression using Slantlet and lifting wavelet transform with and without normalisation
NASA Astrophysics Data System (ADS)
Aggarwal, Vibha; Singh Patterh, Manjeet
2013-05-01
This article analyses the performance of: (i) linear transform: Slantlet transform (SLT), (ii) nonlinear transform: lifting wavelet transform (LWT) and (iii) nonlinear transform (LWT) with normalisation for electrocardiogram (ECG) compression. First, an ECG signal is transformed using linear transform and nonlinear transform. The transformed coefficients (TC) are then thresholded using bisection algorithm in order to match the predefined user-specified percentage root mean square difference (UPRD) within the tolerance. Then, the binary look up table is made to store the position map for zero and nonzero coefficients (NZCs). The NZCs are quantised by Max-Lloyd quantiser followed by Arithmetic coding. The look up table is encoded by Huffman coding. The results show that the LWT gives the best result as compared to SLT evaluated in this article. This transform is then considered to evaluate the effect of normalisation before thresholding. In case of normalisation, the TC is normalised by dividing the TC by ? (where ? is number of samples) to reduce the range of TC. The normalised coefficients (NC) are then thresholded. After that the procedure is same as in case of coefficients without normalisation. The results show that the compression ratio (CR) in case of LWT with normalisation is improved as compared to that without normalisation.
Exploring the evolution of reionization using a wavelet transform and the light cone effect
NASA Astrophysics Data System (ADS)
Trott, Cathryn M.
2016-09-01
The Cosmic Dawn and Epoch of Reionization, during which collapsed structures produce the first ionizing photons and proceed to reionize the intergalactic medium, span a large range in redshift (z ˜ 30-6) and time (tage ˜ 0.1-1.0 Gyr). Exploration of these epochs using the redshifted 21 cm emission line from neutral hydrogen is currently limited to statistical detection and estimation metrics (e.g. the power spectrum) due to the weakness of the signal. Brightness temperature fluctuations in the line-of-sight dimension are probed by observing the emission line at different frequencies, and their structure is used as a primary discriminant between the cosmological signal and contaminating foreground extragalactic and Galactic continuum emission. Evolution of the signal over the observing bandwidth leads to the `line cone effect' whereby the H I structures at the start and end of the observing band are not statistically consistent, yielding a biased estimate of the signal power, and potential reduction in signal detectability. We implement a wavelet transform to wide bandwidth radio interferometry experiments to probe the local statistical properties of the signal. We show that use of the wavelet transform yields estimates with improved estimation performance, compared with the standard Fourier Transform over a fixed bandwidth. With the suite of current and future large bandwidth reionization experiments, such as with the 300 MHz instantaneous bandwidth of the Square Kilometre Array, a transform that retains local information will be important.
R-D optimized tree-structured compression algorithms with discrete directional wavelet transform
NASA Astrophysics Data System (ADS)
Liu, Hui; Ma, Siliang
2008-09-01
A new image coding method based on discrete directional wavelet transform (S-WT) and quad-tree decomposition is proposed here. The S-WT is a kind of transform proposed in [V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, P.L. Dragotti, Directionlets: anisotropic multidirectional representation with separable filtering, IEEE Trans. Image Process. 15(7) (2006)], which is based on lattice theory, and with the difference with the standard wavelet transform is that the former allows more transform directions. Because the directional property in a small region is more regular than in a big block generally, in order to sufficiently make use of the multidirectionality and directional vanishing moment (DVM) of S-WT, the input image is divided into many small regions by means of the popular quad-tree segmentation, and the splitting criterion is on the rate-distortion sense. After the optimal quad-tree is obtained, by means of the embedded property of SPECK, a resource bit allocation algorithm is fast implemented utilizing the model proposed in [M. Rajpoot, Model based optimal bit allocation, in: IEEE Data Compression Conference, 2004, Proceedings, DCC 2004.19]. Experiment results indicate that our algorithms perform better compared to some state-of-the-art image coders.
Liu, Runna; Hu, Hong; Xu, Shanshan; Huo, Rui; Wang, Supin; Wan, Mingxi
2015-06-01
The quality of ultrafast active cavitation imaging (UACI) using plane wave transmission is hindered by low transmission pressure, which is necessary to prevent bubble destruction. In this study, a UACI method that combined wavelet transform with pulse inversion (PI) was proposed to enhance the contrast between the cavitation bubbles and surrounding tissues. The main challenge in using wavelet transform is the selection of the optimum mother wavelet. A mother wavelet named "cavitation bubble wavelet" and constructed according to Rayleigh-Plesset-Noltingk-Neppiras-Poritsky model was expected to obtain a high correlation between the bubbles and beamformed echoes. The method was validated by in vitro experiments. Results showed that the image quality was associated with the initial radius of bubble and the scale. The signal-to-noise ratio (SNR) of the best optimum cavitation bubble wavelet transform (CBWT) mode image was improved by 3.2 dB compared with that of the B-mode image in free-field experiments. The cavitation-to-tissue ratio of the best optimum PI-based CBWT mode image was improved by 2.3 dB compared with that of the PI-based B-mode image in tissue experiments. Furthermore, the SNR versus initial radius curve had the potential to estimate the size distribution of cavitation bubbles.
NASA Astrophysics Data System (ADS)
Campo, D.; Quintero, O. L.; Bastidas, M.
2016-04-01
We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.
NASA Astrophysics Data System (ADS)
Campo, D.; Quintero, O. L.; Bastidas, M.
2016-04-01
We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform - was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.
Transient-spatial pattern mining of eddy current pulsed thermography using wavelet transform
NASA Astrophysics Data System (ADS)
Yang, Hailong; Gao, Bin; Tian, Guiyun; Ren, Wenwei; Woo, Wai Lok
2014-07-01
Eddy current pulsed thermography(ECPT) is an emerging Non-destructive testing and evaluation(NDT & E) technique, which uses hybrid eddy current and thermography NDT & E techniques that enhances the detectability from their compensation. Currently, this technique is limited by the manual selection of proper contrast frames and the issue of improving the efficiency of defect detection of complex structure samples remains a challenge. In order to select a specific frame from transient thermal image sequences to maximize the contrast of thermal variation and defect pattern from complex structure samples, an energy driven approach to compute the coefficient energy of wavelet transform is proposed which has the potential of automatically selecting both optimal transient frame and spatial scale for defect detection using ECPT. According to analysis of the variation of different frequency component and the comparison study of the detection performance of different scale and wavelets, the frame at the end of heating phase is automatically selected as an optimal transient frame for defect detection. In addition, the detection capabilities of the complex structure samples can be enhanced through proper spatial scale and wavelet selection. The proposed method has successfully been applied to low speed impact damage detection of carbon fibre reinforced polymer(CFRP) composite as well as providing the guidance to improve the detectability of ECPT technique.
Gur, Berke M; Niezrecki, Christopher
2007-07-01
Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations.
A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs
NASA Astrophysics Data System (ADS)
de Lannoy, G.; de Decker, A.; Verleysen, M.
The wavelet transform is a widely used pre-filtering step for subsequent R spike detection by thresholding of the coefficients. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptativity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record's characteristics. The selected scales are then used for the decomposition of the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and average positive predictivity rate of 99.7%.
Gur, Berke M; Niezrecki, Christopher
2007-07-01
Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations. PMID:17614478
NASA Astrophysics Data System (ADS)
Dai, Yu; Xue, Yuan; Zhang, Jianxun
2016-01-01
Impulsive noise caused by some random events has the main character of short rise-time and wide frequency spectrum range, so it has the potential to degrade the performance and reliability of the harmonic estimation. This paper focuses on the harmonic estimation procedure based on continuous wavelet transform (CWT) when the analyzed signal is corrupted by the impulsive noise. The digital CWT of both the time-varying sinusoidal signal and the impulsive noise are analyzed, and there are two cross ridges in the time-frequency plane of CWT, which are generated by the signal and the noise separately. In consideration of the amplitude of the noise and the number of the spike event, two inequalities are derived to provide limitations on the wavelet parameters. Based on the amplitude distribution of the noise, the optimal wavelet parameters determined by solving these inequalities are used to suppress the contamination of the noise, as well as increase the amplitude of the ridge corresponding to the signal, so the parameters of each harmonic component can be estimated accurately. The proposed procedure is applied to a numerical simulation and a bone vibration signal test giving satisfactory results of stationary and time-varying harmonic parameter estimation.
Application of cross-wavelet transform to pulse velocity data: seeking for inter-limb coherence
NASA Astrophysics Data System (ADS)
Tsoy, Maria O.; Stiukhina, Elena S.; Postnov, Dmitry E.
2016-04-01
Assessment of pulse waves that recorded in the microvascular bed when the heart throwing blood appears to be the essential diagnostic method. The conventional non-invasive methods are mostly based on measurement of pulse wave velocity (PWV) which was proved to be the predictor of cardiovascular system state. Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. Since many factors contribute to PWV formation, it shows considerable variability and sensitive to the current physiological state. Traditional mathematical methods that examine this variability in the frequency domain, such as Fourier analysis, not always the best choice since the non-stationary features of PWV signal. A relatively new, but already popular tool, Wavelet transform, allows multiresolution analysis in time-frequency domain of non-stationary signals. In our work we apply Wavelet Cross Spectrum (WCS) and Wavelet-Based Coherence (WBC) to reveal the similarities between two PWV time series recorded simultaneously from left and right arms. We find that the degree correlation and the time lag between these signals considerably depend on frequency range. On this basis, we hypothesize the systemic (neurogenic) origin of high-frequency (0.2 Hz) PWV variations.
Automatic detection of position and depth of potential UXO using continuous wavelet transforms
NASA Astrophysics Data System (ADS)
Billings, Stephen D.; Herrmann, Felix J.
2003-09-01
Inversion algorithms for UXO discrimination using magnetometery have recently been used to achieve very low False Alarm Rates, with 100% recovery of detected ordnance. When there are many UXO and/or when the UXO are at significantly different depths, manual estimation of the initial position and scale for each item, is a laborious and time-consuming process. In this paper, we utilize the multi-resolution properties of wavelets to automatically estimate both the position and scale of dipole peaks. The Automated Wavelet Detection (AWD) algorithm that we develop consists of four-stages: (i) maxima and minima in the data are followed across multiple scales as we zoom with a continuous wavelet transform; (ii) the decay of the amplitude of each peak with scale is used to estimate the depth to source; (iii) adjacent maxima and minima of comparable depth are joined together to form dipole anomalies; and (iv) the relative positions and amplitudes of the extrema, along with their depths, are used to estimate a dipole model. We demonstrate the application of the AWD algorithm to three datasets with different characteristics. In each case, the method rapidly located the majority of dipole anomalies and produced accurate estimates of dipole parameters.
A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data
Gregoire, John M.; Dale, Darren; van Dover, R. Bruce
2011-01-01
Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta–theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.
Brychta, Robert J.; Shiavi, Richard; Robertson, David; Diedrich, André
2007-01-01
The accurate assessment of autonomic sympathetic function is important in the diagnosis and study of various autonomic and cardiovascular disorders. Sympathetic function in humans can be assessed by recording the muscle sympathetic nerve activity, which is characterized by synchronous neuronal discharges separated by periods of neural silence dominated by colored Gaussian noise. The raw nerve activity is generally rectified, integrated, and quantified using the integrated burst rate or area. We propose an alternative quantification involving spike detection using a two-stage stationary wavelet transform (SWT) de-noising method. The SWT coefficients are first separated into noise-related and burst-related coefficients on the basis of their local kurtosis. The noise-related coefficients are then used to establish a threshold to identify spikes within the bursts. This method demonstrated better detection performance than an unsupervised amplitude discriminator and similar wavelet-based methods when confronted with simulated data of varying burst rate and signal to noise ratio. Additional validation on data acquired during a graded head-up tilt protocol revealed a strong correlation between the mean spike rate and the mean integrate burst rate (r = 0.85) and burst area rate (r = 0.91). In conclusion, the kurtosis-based wavelet de-noising technique is a potentially useful method of studying sympathetic nerve activity in humans. PMID:17083982
Brychta, Robert J; Shiavi, Richard; Robertson, David; Diedrich, André
2007-03-15
The accurate assessment of autonomic sympathetic function is important in the diagnosis and study of various autonomic and cardiovascular disorders. Sympathetic function in humans can be assessed by recording the muscle sympathetic nerve activity, which is characterized by synchronous neuronal discharges separated by periods of neural silence dominated by colored Gaussian noise. The raw nerve activity is generally rectified, integrated, and quantified using the integrated burst rate or area. We propose an alternative quantification involving spike detection using a two-stage stationary wavelet transform (SWT) de-noising method. The SWT coefficients are first separated into noise-related and burst-related coefficients on the basis of their local kurtosis. The noise-related coefficients are then used to establish a threshold to identify spikes within the bursts. This method demonstrated better detection performance than an unsupervised amplitude discriminator and similar wavelet-based methods when confronted with simulated data of varying burst rate and signal to noise ratio. Additional validation on data acquired during a graded head-up tilt protocol revealed a strong correlation between the mean spike rate and the mean integrate burst rate (r=0.85) and burst area rate (r=0.91). In conclusion, the kurtosis-based wavelet de-noising technique is a potentially useful method of studying sympathetic nerve activity in humans.
Edge extraction of CT medical image based on wavelet transform algorithm
NASA Astrophysics Data System (ADS)
Wang, Xiaojun; Li, Xinzheng; Lai, Weidong
2011-06-01
Since computer tomography (CT) image has been widely applied in clinic diagnostics, while for many applications the information directly provided by CT images is incomplete corrupted by noise or instrument defect, there has great demand to further the processing methods for improving the CT image quality. Among all image features, the edge profile of clinic focus has obvious influence on accurately translating CT image. In this paper, the wavelet filtering algorithm based on modulus maximum method is put forward to extract and enhance the CT image edges. Edges in the brain lobe CT image can be outlined after wavelet transform, during which the wavelet assigned as the first order derivative of Gauss function. Further manipulation through maximum threshold checking to the modulus have been attenuated the pseudo-edges. After segmented with the original CT image, the edge structure has been distinctly enhanced, and high contrast is achieved between the brain lobe microstructure and the artificially established edges. The proposed algorithm is more efficient than the common first order differential operator, for the latter it even deteriorates the edge features. The algorithm proposed in this article can be integrated in medical image analyzing software to obtain higher accuracy for symptom interpretation.
Robust 4D Flow Denoising Using Divergence-Free Wavelet Transform
Ong, Frank; Uecker, Martin; Tariq, Umar; Hsiao, Albert; Alley, Marcus T; Vasanawala, Shreyas S.; Lustig, Michael
2014-01-01
Purpose To investigate four-dimensional flow denoising using the divergence-free wavelet (DFW) transform and compare its performance with existing techniques. Theory and Methods DFW is a vector-wavelet that provides a sparse representation of flow in a generally divergence-free field and can be used to enforce “soft” divergence-free conditions when discretization and partial voluming result in numerical nondivergence-free components. Efficient denoising is achieved by appropriate shrinkage of divergence-free wavelet and nondivergence-free coefficients. SureShrink and cycle spinning are investigated to further improve denoising performance. Results DFW denoising was compared with existing methods on simulated and phantom data and was shown to yield better noise reduction overall while being robust to segmentation errors. The processing was applied to in vivo data and was demonstrated to improve visualization while preserving quantifications of flow data. Conclusion DFW denoising of four-dimensional flow data was shown to reduce noise levels in flow data both quantitatively and visually. PMID:24549830
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.
Evaluation of blood access dysfunction based on a wavelet transform analysis of shunt murmurs.
Sato, Toshio; Tsuji, Kiichi; Kawashima, Norimichi; Agishi, Tetsuzo; Toma, Hiroshi
2006-01-01
We investigated shunt murmurs based on wavelet transform analysis as a new method for assessing vascular access function. In the present study, in patients with venous stenosis near an arteriovenous fistula (A-V fistula), a sensor was placed at different positions around the stenosis and shunt murmur signals obtained using a measurement system were subjected to time-frequency analysis based on wavelet transforms. The shunt murmurs obtained from the stenotic region closely represented some features of murmurs that are often referred to as "high-pitch" murmurs in the clinical setting. In contrast, shunt murmurs obtained about 5 cm downstream of the stenotic region closely represented some features of murmurs that are often referred to as "low-pitch" murmurs in the clinical setting. Furthermore, with the aim of extending the lifespan of arteriovenous grafts (A-V grafts) by detecting and treating stenotic lesions before the A-V graft becomes occluded, we evaluated the possibility of utilizing the present shunt murmur analysis for monitoring stenosis in such A-V grafts. When shunt murmurs from patients with A-V grafts were analyzed, the results suggested that the blood flow through the venous anastomosis of the graft was the most turbulent. This present method whereby blood flow in an A-V fistula is assessed based on the frequency distribution on a time-frequency plane by wavelet transform analysis is advantageous because findings are not markedly affected by sensor attachment. Furthermore, because the sensor is attached using an adhesive collar, measurements can be taken over a short period of time before each dialysis session. PMID:16807812
Oltean, Gabriel; Ivanciu, Laura-Nicoleta
2016-01-01
The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms), efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer), and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination). The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each parameter on the
Time-frequency analysis of spike-wave discharges using a modified wavelet transform.
Bosnyakova, Daria; Gabova, Alexandra; Kuznetsova, Galina; Obukhov, Yuri; Midzyanovskaya, Inna; Salonin, Dmitrij; van Rijn, Clementina; Coenen, Anton; Tuomisto, Leene; van Luijtelaar, Gilles
2006-06-30
The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency dynamics of the dominating rhythm during the discharges. SWD were analyzed pre- and post-administration of certain drugs. SWD recorded predrug demonstrate quite uniform time-frequency dynamics of the dominant rhythm. The beginning of the discharge has a short period with the highest frequency value (up to 15 Hz). Then the frequency decreases to 7-9 Hz and frequency modulation occurs during the discharge in this range with a period of 0.5-0.7 s. Specific changes of SWD time-frequency dynamics were found after the administration of psychoactive drugs, addressing different brain mediator and modulator systems. Short multiple SWDs appeared under low (0.5 mg/kg) doses of haloperidol, they are characterized by a fast frequency decrease to 5-6 Hz at the end of every discharge. The frequency of the dominant frequency of SWD was not stable in long lasting SWD after 1.0 mg/kg or more haloperidol: then two periodicities were found. Long lasting SWD seen after the administration of vigabatrin showed a stable frequency of the discharge. The EEG after Ketamin showed a distinct 5 s quasiperiodicity. No clear changes of time-frequency dynamics of SWD were found after perilamine. It can be concluded that the use of the modified Morlet wavelet transform allows to describe significant parameters of the dynamics in the time-frequency domain of the dominant rhythm of SWD that were not previously detected.
Oltean, Gabriel; Ivanciu, Laura-Nicoleta
2016-01-01
The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms), efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer), and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination). The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each parameter on the
NASA Astrophysics Data System (ADS)
He, Wangpeng; Zi, Yanyang; Chen, Binqiang; Wu, Feng; He, Zhengjia
2015-03-01
Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.
NASA Astrophysics Data System (ADS)
Paliwal, Deepak; Choudhur, Achintya; Govandhan, T.
2014-06-01
Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.
Suto, Noriko; Harada, Makoto; Izutsu, Jun; Nagao, Toshiyasu
2006-07-01
In order to accurately estimate the geomagnetic transfer functions in the area of the volcano Mt. Iwate (IWT), we applied the interstation transfer function (ISTF) method to the three-component geomagnetic field data observed at Mt. Iwate station (IWT), using the Kakioka Magnetic Observatory, JMA (KAK) as remote reference station. Instead of the conventional Fourier transform, in which temporary transient noises badly degrade the accuracy of long term properties, continuous wavelet transform has been used. The accuracy of the results was as high as that of robust estimations of transfer functions obtained by the Fourier transform method. This would provide us with possibilities for routinely monitoring the transfer functions, without sophisticated statistical procedures, to detect changes in the underground electrical conductivity structure. PMID:25792780
NASA Astrophysics Data System (ADS)
Sarrafi, Aral; Mao, Zhu
2016-04-01
In the application of Structural Health Monitoring (SHM), processing the online-acquired data plays a very important role, among which wavelet transform is an outstanding tool and compared to Fourier transform, it handles the nonstationary behaviors in the time series in an adaptive fashion. When dealing with time-variant data, there are uncertainties from numerous resources inherent to the feature estimation, such as measurement noise, operational and environmental variability, hardware limitation, etc. The corruption from uncertainty will make the data interpretation ambiguous and thereby dramatically degrades the decision quality with regard to the occurrence, location, severity, and extent of damages. This paper derives a probabilistic model to quantify analytically the uncertainty of wavelet transform feature as a random variable, and variance is derived analytically in this work. Considering central limit theorem, Gaussian probability density function characterizes the distribution and this has been validated via Monte Carlo testing. By fully characterizing the uncertainty, the damage detection implementations may be facilitated with a quantified false alarm rate and miss catch rate.
Harmonic signal extraction from noisy chaotic interferencebased on synchrosqueezed wavelet transform
NASA Astrophysics Data System (ADS)
Wang, Xiang-Li; Wang, Wen-Bo
2015-08-01
For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform (SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform (SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities. The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method. Project supported by the National Natural Science Foundation of China (Grant No. 61171075), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303).
[A wavelet-transform-based method for the automatic detection of late-type stars].
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.
[Study of analysis of the singularity of R-wave by using wavelet transform].
Wang, Weidong; Wang, Buqing; Liu, Guangrong
2011-08-01
Singularity is a basic feature of biological signals. Based on the variations of wavelet transform modulus maxima in multi-scales, we studied the basic theorem for analyzing the singularity and proposed an algorithm for calculating lipschitz exponent. Then we applied the algorithm to calculate the singularity of R-wave in ECG. Our study found that the level of singularity of R-waves in ECG between the 10 arrhythm patients randomly chosen and the healthy persons was significantly different, with the level of singularity of R-wave of normal persons remarkably higher than that of arrhythmia patients.
Rahbar, Kambiz; Faez, Karim; Attaran-Kakhki, Ebrahim
2012-06-01
Reduction of image quality under the effects of wavefront aberration of the optical system has a direct impact on the vision system's performance. This paper tries to estimate the amount of aberration with the use of wavelet transform profilometry. The basic idea is based on the principle that under aberration effects, the position of the fringes' image on the image plane will change, and this change correlates with the amount of aberration. So the distribution of aberration function can directly be extracted through measuring the amount of changes in the fringes' image on the image plane. Experimental results and the empirical validity of this idea are evaluated.
[An improved motion estimation of medical image series via wavelet transform].
Zhang, Ying; Rao, Nini; Wang, Gang
2006-10-01
The compression of medical image series is very important in telemedicine. The motion estimation plays a key role in the video sequence compression. In this paper, an improved square-diamond search (SDS) algorithm is proposed for the motion estimation of medical image series. The improved SDS algorithm reduces the number of the searched points. This improved SDS algorithm is used in wavelet transformation field to estimate the motion of medical image series. A simulation experiment for digital subtraction angiography (DSA) is made. The experiment results show that the algorithm accuracy is higher than that of other algorithms in the motion estimation of medical image series. PMID:17121333
Application of chaotic prediction model based on wavelet transform on water quality prediction
NASA Astrophysics Data System (ADS)
Zhang, L.; Zou, Z. H.; Zhao, Y. F.
2016-08-01
Dissolved oxygen (DO) is closely related to water self-purification capacity. In order to better forecast its concentration, the chaotic prediction model, based on the wavelet transform, is proposed and applied to a certain monitoring section of the Mentougou area of the Haihe River Basin. The result is compared with the simple application of the chaotic prediction model. The study indicates that the new model aligns better with the real data and has a higher accuracy. Therefore, it will provide significant decision support for water protection and water environment treatment.
Bahoura, M; Hassani, M; Hubin, M
1997-01-01
An algorithm based on wavelet transform (WTs) suitable for real time implementation has been developed in order to detect ECG characteristics. In particular, QRS complexes, P and T waves may be distinguished from noise, baseline drift or artefacts. This algorithm is implemented in a DSP (SPROC-1400) with a 50 MHz frequency clock. The performance of this algorithm is discussed, its accuracy is evaluated and a comparison is made with a similar algorithm implemented in C language. For the standard MIT/BIH arrhythmia database, this algorithm correctly detects 99.7% of the QRS complexes. PMID:9034668
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
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. PMID:22319311
Fabric defect detection using the wavelet transform in an ARM processor
NASA Astrophysics Data System (ADS)
Fernández, J. A.; Orjuela, S. A.; Álvarez, J.; Philips, W.
2012-01-01
Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations.
Pukhova, Valentina; Banfi, Francesco; Ferrini, Gabriele
2015-05-01
The transient eigenmode structure of an interacting cantilever during a single impact on different surfaces evidences the excitation of higher flexural modes and low frequency oscillations. The frequency shift of the fundamental mode after the tip comes into contact with the sample surface allows calculating the tip-sample interaction stiffness and evidences the role of capillary condensation and surface wettability on the cantilever dynamics. Wavelet transforms are used to trace the origin of spectral features in the cantilever spectra and calculate force gradients of the tip-sample interaction.
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.
NASA Astrophysics Data System (ADS)
Zhang, Fang; Su, Rongguo; He, Jianfeng; Cai, Minghong; Luo, Wei; Wang, Xiulin
2010-02-01
The feasibility of using time domain of wavelet transform as characteristics to establish a fluorometric discrimination method of phytoplankton was discussed. Twelve phytoplankton species belonging to nine genera of five divisions were studied. Five steps were introduced: firstly, the feasibility of utilizing 3D fluorescence spectra (3D-FS) to discriminate phytoplankton was discussed; the relative standard deviation (RSD) and included angle cosine (IAC) were used as the test criterion. 3D-FS had such potentials, for most RSD were <5% and most IAC were >0.990. Secondly, the 3D-FS were decomposed by db7 wavelet and time-series vectors (TSVs) were generated. Thirdly, the optimal characteristic spectra (OCS) were selected from the TSV by Bayesian linear discriminant analysis (BLDA). The ability of OCS to classify phytoplankton was tested, and the correct classification ratios (CCRs) at different levels were obtained. Most CCRs were 90-100% at the species level. They were >98% at the genus level, and >99% at the division level. Fourthly, the growth and light stability of the OCS were tested. Both stabilities were high with lower RSD (<3%) and higher IAC (>0.999) compared with 3D-FS. Fifthly, a "database of reference spectra" consisting of 46 reference spectra was established by hierarchical cluster analysis (HCA). Based on this, the discrimination method of phytoplankton species was established by nonnegative least squares (NNLSs). Most reference spectra were representative to phytoplankton species; and had moderate anti-noise ability: With noise ≤10%, the correct discrimination ratios (CDRs) were >98% at the genus level and >99% at the division level. 20% noise was a larger interference which made CDRs down to 85% at the genus level and to 99% at the division level. A fluorometric discrimination method of phytoplankton could be established based on TSV of wavelet transform.
NASA Astrophysics Data System (ADS)
Riel, B.; Simons, M.; Agram, P.
2012-12-01
Transients are a class of deformation signals on the Earth's surface that can be described as non-periodic accumulation of strain in the crust. Over seismically and volcanically active regions, these signals are often challenging to detect due to noise and other modes of deformation. Geodetic datasets that provide precise measurements of surface displacement over wide areas are ideal for exploiting both the spatial and temporal coherence of transient signals. We present an extension to the Multiscale InSAR Time Series (MInTS) approach for analyzing geodetic data by combining the localization benefits of wavelet transforms (localizing signals in space) with sparse optimization techniques (localizing signals in time). Our time parameterization approach allows us to reduce geodetic time series to sparse, compressible signals with very few non-zero coefficients corresponding to transient events. We first demonstrate the temporal transient detection by analyzing GPS data over the Long Valley caldera in California and along the San Andreas fault near Parkfield, CA. For Long Valley, we are able to resolve the documented 2002-2003 uplift event with greater temporal precision. Similarly for Parkfield, we model the postseismic deformation by specific integrated basis splines characterized by timescales that are largely consistent with postseismic relaxation times. We then apply our method to ERS and Envisat InSAR datasets consisting of over 200 interferograms for Long Valley and over 100 interferograms for Parkfield. The wavelet transforms reduce the impact of spatially correlated atmospheric noise common in InSAR data since the wavelet coefficients themselves are essentially uncorrelated. The spatial density and extended temporal coverage of the InSAR data allows us to effectively localize ground deformation events in both space and time with greater precision than has been previously accomplished.
Nie, Xinhua; Pan, Zhongming; Zhang, Dasha; Zhou, Han; Chen, Min; Zhang, Wenna
2014-01-01
Magnetic anomaly detection (MAD) is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise) with a power spectral density of 1/fa (0wavelet decomposition can play the role of a Karhunen-Loève-type expansion to the 1/f-type signal by its decorrelation abilities, an effective energy detection method based on undecimated discrete wavelet transform (UDWT) is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI) magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT), the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method. PMID:25343484
Wavelet transform to discriminate between crop and weed in agronomic images
NASA Astrophysics Data System (ADS)
Bossu, Jérémie; Gée, Christelle; Truchetet, Frédéric
2007-09-01
In precision agriculture, the reduction of herbicide applications requires an accurate detection of weed patches. From image detection, to quantify weed infestations, it would be necessary to identify crop rows from line detection algorithm and to discriminate weed from crop. Our laboratory developed several methods for line detection based on Hough Transform, double Hough Transform or Gabor filtering. The Hough Transform is well adapted to image affected by perspective deformations but the computation burden is heavy and on-line applications are hardly handled. To lighten this problem, we have used a Gabor filter to enhance the crop rows present into the image but, if this method is robust with parallel crop rows (without perspective distortions), it implies to deform image with an inverse projection matrix to be applied in the case of an embedded camera. We propose, in order to implement a filter in the scale / space domain, to use a discrete dyadic wavelet transform. Thus, we can extract the vertical details contained in various parts of the image from different levels of resolution. Each vertical detail level kept allows to enhance the crop rows in a specific part of the initial image. The combination of these details enable us to discriminate crop from weeds with a simple logical operation. This spatial method, thanks to the fast wavelet transform algorithm, can be easily implemented for a real time application and it leads to better results than those obtained from Gabor filtering. For this method, the weed infestation rate is estimated and the performance are compared to those given by other methods. A discussion concludes about the ability of this method to detect the crop rows in agronomic images. Finally we consider the ability of this spatial-only approach to classify weeds from crop.
Neurochip based on light-addressable potentiometric sensor with wavelet transform de-noising*
Liu, Qing-jun; Ye, Wei-wei; Yu, Hui; Hu, Ning; Du, Li-ping; Wang, Ping
2010-01-01
Neurochip based on light-addressable potentiometric sensor (LAPS), whose sensing elements are excitable cells, can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed. Here we report a kind of neurochip with rat pheochromocytoma (PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform. Cells were cultured on LAPS for several days to form networks, and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space. The signal was decomposed into various scales, and coefficients were processed based on the properties of each layer. At last, signal was reconstructed based on the new coefficients. The results show that after de-noising, baseline drift is removed and signal-to-noise ratio is increased. It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform, taking advantage of its time-frequency localization analysis to reduce noise. PMID:20443210
NASA Astrophysics Data System (ADS)
Qian, Jinfang; Zhang, Changjiang
2014-11-01
An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.
[A novel method to determine the redshifts of active galaxies based on wavelet transform].
Tu, Liang-Ping; Luo, A-Li; Jiang, Bin; Wei, Peng; Zhao, Yong-Heng; Liu, Rong
2012-10-01
Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.
Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform
Chen, Bo; Chen, Zhi-wei; Wang, Gan-jun; Xie, Wei-ping
2014-01-01
The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited. PMID:24991647
Gaseous emboli detection based on a dual-wavelet transform analysis.
Ng, H S; Nygaard, H; Hasenkam, J M; Johansen, P
2007-08-01
Emboli monitoring is nowadays based on the assessment of microembolic signals by Doppler ultrasound. However, the present systems have problems in detecting multiple emboli. A more dedicated algorithm for post-processing of the recorded Doppler signals was proposed. Based on the hypothesis that single and multiple gaseous emboli can be quantified by combining discrete and continuous wavelet transformation, the aim of this study was to detect gaseous emboli and to validate our method visually. A flow rig was used where gaseous emboli were generated. Doppler signals and visual validation data of gaseous emboli were acquired simultaneously. Microembolic signals were extracted and analysed using wavelet transformation. Results were validated against a visual reference. At various degrees of bubble generation, the system had 100 per cent detection during a low frequency of bubble generation but an estimation error of 7.4 per cent during a high frequency of bubble generation. The estimation error varied between -7.4 and +3 per cent. The system had a higher rate of success in detecting large gaseous emboli in small numbers than small gaseous emboli in large numbers. Single and double emboli were successfully detected and separated, whereas gaseous emboli clouds could be detected but not quantified. Being able to separate simultaneous gaseous emboli may offer new means of increasing detectability for embolism monitoring.
Enhanced mu rhythm extraction using blind source separation and wavelet transform.
Ng, Siew-Cheok; Raveendran, Paramesran
2009-08-01
The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.
Damage detection on sudden stiffness reduction based on discrete wavelet transform.
Chen, Bo; Chen, Zhi-wei; Wang, Gan-jun; Xie, Wei-ping
2014-01-01
The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited.
Damage detection on sudden stiffness reduction based on discrete wavelet transform.
Chen, Bo; Chen, Zhi-wei; Wang, Gan-jun; Xie, Wei-ping
2014-01-01
The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited. PMID:24991647
Salmanpour, Aryan; Brown, Lyndon J; Shoemaker, J Kevin
2008-01-01
Accurate investigation of the sympathetic nervous system is important in the diagnosis and study of various autonomic and cardiovascular control and disorders. Sympathetic function associated with blood pressure regulation in humans can be evaluated by recording muscle sympathetic nerve activity (MSNA), which is characterised by synchronous neuronal discharges separated by periods of neural silence dominated by colored gaussian noise. In this paper two common methods for detecting filtered action potential in MSNA recordings is compared. These methods are based on stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The performance analysis are evaluated using simulated MSNA using templates extracted from real MSNA recorded from three healthy subjects.
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.
On Quantization in Light-cone Variables Compatible with Wavelet Transform
NASA Astrophysics Data System (ADS)
Altaisky, M. V.; Kaputkina, N. E.
2016-06-01
Canonical quantization of quantum field theory models is inherently related to the Lorentz invariant partition of classical fields into the positive and the negative frequency parts u( x) = u +( x) + u -( x), performed with the help of Fourier transform in Minkowski space. That is the commutation relations are being established between nonlocalized solutions of field equations. At the same time the construction of divergence free physical theory requires the separation of the contributions of different space-time scales. In present paper, using the light-cone variables, we propose a quantization procedure which is compatible with separation of scales using continuous wavelet transform, as described in our previous paper (Altaisky, M.V., Kaputkina, N.E.: Phys. Rev. D 88, 025015 2013).
NASA Astrophysics Data System (ADS)
Sinha, Pampa; Nath, Sudipta
2010-10-01
The main aspects of power system delivery are reliability and quality. If all the customers of a power system get uninterrupted power through the year then the system is considered to be reliable. The term power quality may be referred to as maintaining near sinusoidal voltage at rated frequency at the consumers end. The power component definitions are defined according to the IEEE Standard 1459-2000 both for single phase and three phase unbalanced systems based on Fourier Transform (FFT). In the presence of nonstationary power quality (PQ) disturbances results in accurate values due to its sensitivity to the spectral leakage problem. To overcome these limitations the power quality components are calculated using Discrete Wavelet Transform (DWT). In order to handle the uncertainties associated with electric power systems operations fuzzy logic has been incorporated in this paper. A new power quality index has been introduced here which can assess the power quality under nonstationary disturbances.
Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges
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.
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
Lai, Zongying; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Ye, Jing; Zhan, Zhifang; Chen, Zhong
2016-01-01
Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
NASA Astrophysics Data System (ADS)
Kikuchi, Kazuyoshi
2014-09-01
Convectively coupled equatorial waves (CCEWs) are major sources of tropical day-to-day variability. The majority of CCEWs-related studies for the past decade or so have based their analyses, in one form or another, on the Fourier-based space-time spectral analysis method developed by Wheeler and Kiladis (WK). Like other atmospheric and oceanic phenomena, however, CCEWs exhibit pronounced nonstationarity, which the conventional Fourier-based method has difficulty elucidating. The purpose of this study is to introduce an analysis method that is able to describe the time-varying spectral features of CCEWs. The method is based on a transform, referred to as the combined Fourier-wavelet transform (CFWT), defined as a combination of the Fourier transform in space (longitude) and wavelet transform in time, providing an instantaneous space-time spectrum at any given time. The elaboration made on how to display the CFWT spectrum in a manner analogous to the conventional method (i.e., as a function of zonal wavenumber and frequency) and how to estimate the background noise spectrum renders the method more practically feasible. As a practical example, this study analyzes 3-hourly cloud archive user service (CLAUS) cloudiness data for 23 years. The CFWT and WK methods exhibit a remarkable level of agreement in the distributions of climatological-mean space-time spectra over a wide range of space-time scales ranging in time from several hours to several tens of days, indicating the instantaneous CFWT spectrum provides a reasonable snapshot. The usefulness of the capability to localize space-time spectra in time is demonstrated through examinations of the annual cycle, interannual variability, and a case study.
Liu, Runna; Xu, Shanshan; Hu, Hong; Huo, Rui; Wang, Supin; Wan, Mingxi
2016-08-01
Cavitation detection and imaging are essential for monitoring high-intensity focused ultrasound (HIFU) therapies. In this paper, an active cavitation imaging method based on wavelet transform is proposed to enhance the contrast between the cavitation bubbles and surrounding tissues. The Yang-Church model, which is a combination of the Keller-Miksis equation with the Kelvin-Voigt equation for the pulsations of gas bubbles in simple linear viscoelastic solids, is utilized to construct the bubble wavelet. Experiments with porcine muscles demonstrate that image quality is associated with the initial radius of the bubble wavelet and the scale. Moreover, the Yang-Church model achieves a somewhat better performance compared with the Rayleigh-Plesset-Noltingk-Neppiras-Poritsky model. Furthermore, the pulse inversion (PI) technique is combined with bubble wavelet transform to achieve further improvement. The cavitation-to-tissue ratio (CTR) of the best tissue bubble wavelet transform (TBWT) mode image is improved by 5.1 dB compared with that of the B-mode image, while the CTR of the best PI-based TBWT mode image is improved by 7.9 dB compared with that of the PI-based B-mode image. This work will be useful for better monitoring of cavitation in HIFU-induced therapies.
Liu, Runna; Xu, Shanshan; Hu, Hong; Huo, Rui; Wang, Supin; Wan, Mingxi
2016-08-01
Cavitation detection and imaging are essential for monitoring high-intensity focused ultrasound (HIFU) therapies. In this paper, an active cavitation imaging method based on wavelet transform is proposed to enhance the contrast between the cavitation bubbles and surrounding tissues. The Yang-Church model, which is a combination of the Keller-Miksis equation with the Kelvin-Voigt equation for the pulsations of gas bubbles in simple linear viscoelastic solids, is utilized to construct the bubble wavelet. Experiments with porcine muscles demonstrate that image quality is associated with the initial radius of the bubble wavelet and the scale. Moreover, the Yang-Church model achieves a somewhat better performance compared with the Rayleigh-Plesset-Noltingk-Neppiras-Poritsky model. Furthermore, the pulse inversion (PI) technique is combined with bubble wavelet transform to achieve further improvement. The cavitation-to-tissue ratio (CTR) of the best tissue bubble wavelet transform (TBWT) mode image is improved by 5.1 dB compared with that of the B-mode image, while the CTR of the best PI-based TBWT mode image is improved by 7.9 dB compared with that of the PI-based B-mode image. This work will be useful for better monitoring of cavitation in HIFU-induced therapies. PMID:27586712
NASA Astrophysics Data System (ADS)
Lee, Sin Ho; Park, Jin Bae; Choi, Yoon Ho
2013-09-01
In this paper, wavelet-transform-based time-frequency domain reflectometry (WTFDR) is proposed for load impedance measurement. In order to measure the load impedance, the energy of the measured signal in the time-frequency domain, the phase difference between the reference signal and the reflected signal, the characteristic impedance, and the attenuation factor of the measured cable must all be known. Since the complex wavelet transform is composed of real and imaginary parts, the phase difference is easily obtained using the ratio of the real coefficient to the imaginary coefficient. In addition, the wavelet energy denotes the sum of the square of the modulus of the wavelet transform and describes the energy of the measured signal in the time and frequency domains. To accurately determine the characteristic impedance and attenuation factors, the power cable should be estimated as a coaxial cable. Using WTFDR with the complex mother wavelet and the estimated power cable, the load impedance can be obtained simply and accurately. Finally, real experiments for the evaluation of various load impedances are carried out to confirm the effectiveness and accuracy of the proposed method compared to the conventional time-frequency domain reflectometry.
Pan, X.; Metz, C.E.
1995-12-01
A general approach that the authors proposed elsewhere reveals the intrinsic relationship among methods for inversion of the 2-D exponential Radon transform described by Bellini et al., by Tretiak and Metz, by Hawkins et al., and by Inouye et al. Moreover, the approach provides an infinite class of linear methods for inverting the 2-D exponential Radon transform. In the work reported here, they systematically investigated the noise characteristics of the methods in this class, obtaining analytical forms for the autocovariance and the variance of the images reconstructed by use of various methods. The noise properties of a new quasi-optimal method were then compared theoretically to those of other methods of the class. The analysis demonstrates that the quasi-optimal method achieves smaller global variance in the reconstructed images than do the other methods of the class. Extensive numerical simulation studies confirm this prediction.
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.
Determination of solar cycle length variations using the continuous wavelet transform
NASA Astrophysics Data System (ADS)
Fligge, M.; Solanki, S. K.; Beer, J.
1999-06-01
The length of the sunspot cycle determined by Friis-Christensen & Lassen (1991) correlates well with indicators of terrestrial climate, but has been criticized as being subjective. In the present paper we present a more objective and general cycle-length determination. Objectivity is achieved by using the continuous wavelet transform based on Morlet wavelets and carrying out a careful error analysis. Greater generality comes from the application of this technique to different records of solar activity, e.g. sunspot number, sunspot area, plage area or (10) Be records. The use of different indicators allows us to track cycle length variations back to the 15th century. All activity indicators give cycle length records which agree with each other within the error bars, whereby the signal due to the solar cycle is weaker within (10) Be than in the other indicators. In addition, all records exhibit cycle length variations which are, within the error bars, in accordance with the record originally proposed by Friis-Christensen & Lassen (1991). In the 16th century, however, the (10) Be record suggests a much longer cycle than the auroral record used by Friis-Christensen & Lassen. Also, the presence of a distinct 11-year cycle in the (10) Be record during the Maunder Minimum is confirmed. By combining the results from all the indicators a composite of the solar cycle length is constructed, which we expect to be more reliable than the length derived from individual records.
Instrument-independent analysis of music by means of the continuous wavelet transform
NASA Astrophysics Data System (ADS)
Olmo, Gabriella; Dovis, Fabio; Benotto, Paolo; Calosso, Claudio; Passaro, Pierluigi
1999-10-01
This paper deals with the problem of automatic recognition of music. Segments of digitized music are processed by means of a Continuous Wavelet Transform, properly chosen so as to match the spectral characteristics of the signal. In order to achieve a good time-scale representation of the signal components a novel wavelet has been designed suited to the musical signal features. particular care has been devoted towards an efficient implementation, which operates in the frequency domain, and includes proper segmentation and aliasing reduction techniques to make the analysis of long signals feasible. The method achieves very good performance in terms of both time and frequency selectivity, and can yield the estimate and the localization in time of both the fundamental frequency and the main harmonics of each tone. The analysis is used as a preprocessing step for a recognition algorithm, which we show to be almost independent on the instrument reproducing the sounds. Simulations are provided to demonstrate the effectiveness of the proposed method.
NASA Astrophysics Data System (ADS)
Hussain, M. S.; Mamun, Md.
2012-01-01
Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.
A continuous wavelet transform and classification method for delirium motoric subtyping.
Godfrey, Alan; Conway, Richard; Leonard, Maeve; Meagher, David; Olaighin, Gearóid M
2009-06-01
The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels. PMID:19497833
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942
Lahmiri, Salim; Boukadoum, Mounir
2013-01-01
A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906
Silva, M Z; Gouyon, R; Lepoutre, F
2003-06-01
Preliminary results of hidden corrosion detection in aircraft aluminum structures using a noncontact laser based ultrasonic technique are presented. A short laser pulse focused to a line spot is used as a broadband source of ultrasonic guided waves in an aluminum 2024 sample cut from an aircraft structure and prepared with artificially corroded circular areas on its back surface. The out of plane surface displacements produced by the propagating ultrasonic waves were detected with a heterodyne Mach-Zehnder interferometer. Time-frequency analysis of the signals using a continuous wavelet transform allowed the identification of the generated Lamb modes by comparison with the calculated dispersion curves. The presence of back surface corrosion was detected by noting the loss of the S(1) mode near its cutoff frequency. This method is applicable to fast scanning inspection techniques and it is particularly suited for early corrosion detection.
A wavelet transform based feature extraction and classification of cardiac disorder.
Sumathi, S; Beaulah, H Lilly; Vanithamani, R
2014-09-01
This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652
Research on power-law acoustic transient signal detection based on wavelet transform
NASA Astrophysics Data System (ADS)
Han, Jian-hui; Yang, Ri-jie; Wang, Wei
2007-11-01
Aiming at the characteristics of acoustic transient signal emitted from antisubmarine weapon which is being dropped into water (torpedo, aerial sonobuoy and rocket assisted depth charge etc.), such as short duration, low SNR, abruptness and instability, based on traditional power-law detector, a new method to detect acoustic transient signal is proposed. Firstly wavelet transform is used to de-noise signal, removes random spectrum components and improves SNR. Then Power- Law detector is adopted to detect transient signal. The simulation results show the method can effectively extract envelop characteristic of transient signal on the condition of low SNR. The performance of WT-Power-Law markedly outgoes that of traditional Power-Law detection method.
x-ray irradiation analysis based on wavelet transform in tokamak plasma.
Ghanbari, K; Ghoranneviss, M; Elahi, A Salar; Saviz, S
2014-01-01
Hard x-ray emission from the Runaway electrons is an important issue in tokamaks. Suggesting methods to reduce the Runaway electrons and therefore the emitted hard x-ray is important for tokamak plasma operation. In this manuscript, we have investigated the effects of external fields on hard x-ray intensity and Magneto-Hydro-Dynamic (MHD) activity. In other words, we have presented the effects of positive biased limiter and Resonant Helical Field (RHF) on the MHD fluctuations and hard x-ray emission from the Runaway electrons. MHD activity and hard x-ray intensity were analyzed using Wavelet transform in the presence of external fields and without them. The results show that the MHD activity and therefore the hard x-ray intensity can be controlled by the external electric and magnetic fields.
Wavelet Transform Based Higher Order Statistical Analysis of Wind and Wave Time Histories
NASA Astrophysics Data System (ADS)
Habib Huseni, Gulamhusenwala; Balaji, Ramakrishnan
2016-06-01
Wind, blowing on the surface of the ocean, imparts the energy to generate the waves. Understanding the wind-wave interactions is essential for an oceanographer. This study involves higher order spectral analyses of wind speeds and significant wave height time histories, extracted from European Centre for Medium-Range Weather Forecast database at an offshore location off Mumbai coast, through continuous wavelet transform. The time histories were divided by the seasons; pre-monsoon, monsoon, post-monsoon and winter and the analysis were carried out to the individual data sets, to assess the effect of various seasons on the wind-wave interactions. The analysis revealed that the frequency coupling of wind speeds and wave heights of various seasons. The details of data, analysing technique and results are presented in this paper.
Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.
Lee, Wonhee; Park, Chan Gook
2014-02-19
A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).
Real-time 2D floating-point fast Fourier transforms for seeker simulation
NASA Astrophysics Data System (ADS)
Chamberlain, Richard; Lord, Eric; Shand, David J.
2002-07-01
The floating point Fast Fourier Transform (FFT) is one of the most useful basic functions available to the image and signal processing engineer allowing many complex and detailed special functions to be implemented more simply in the frequency domain. In the Hardware-in-the-Loop field an image transformed using FFT would allow the designer to think about accurate frequency based simulation of seeker lens effects, motion blur, detector transfer functions and much more. Unfortunately, the transform requires many hundreds of thousands or millions of floating point operations on a single modest sized image making it impractical for realtime Hardware-in-the-Loop systems. .until now. This paper outlines the development, by Nallatech, of an FPGA based IEEE floating point core. It traces the subsequent use of this core to develop a full 256 X 256 FFT and filter process implemented on COTS hardware at frame rates up to 150Hz. This transform can be demonstrated to model optical transfer functions at a far greater accuracy than the current spatial models. Other applications and extensions of this technique will be discussed such as filtering for image tracking algorithms and in the simulation of radar processing in the frequency domain.
NASA Astrophysics Data System (ADS)
Sarma, Bornali; Chauhan, Sourabh S.; Wharton, A. M.; Iyengar, A. N. Sekar; Iyengar
2013-10-01
Characterization of self-similarity properties of turbulence in magnetized plasma is being carried out in DC glow discharge plasma. The time series floating potential fluctuation experimental data are acquired from the plasma by Langmuir probe. Continuous wavelet transform (CWT) analysis considering db4 mother wavelet has been applied to the experimental data and self-similarity properties are detected by evaluating the Hurst exponent from the wavelet variance plotting. From the CWT spectrum, effort is made to extract a highly correlated frequency by locating the brightest spot. Accordingly, those signals are treated for finding out correlation dimension and the Liapunov exponent so that the exact frequency responsible for the chaotic behavior could be found out.
NASA Astrophysics Data System (ADS)
Yaşar, Hüseyin; Ceylan, Murat
2015-03-01
Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.
NASA Astrophysics Data System (ADS)
Belayneh, A.; Adamowski, J.; Khalil, B.; Quilty, J.
2016-05-01
This study explored the ability of coupled machine learning models and ensemble techniques to predict drought conditions in the Awash River Basin of Ethiopia. The potential of wavelet transforms coupled with the bootstrap and boosting ensemble techniques to develop reliable artificial neural network (ANN) and support vector regression (SVR) models was explored in this study for drought prediction. Wavelet analysis was used as a pre-processing tool and was shown to improve drought predictions. The Standardized Precipitation Index (SPI) (in this case SPI 3, SPI 12 and SPI 24) is a meteorological drought index that was forecasted using the aforementioned models and these SPI values represent short and long-term drought conditions. The performances of all models were compared using RMSE, MAE, and R2. The prediction results indicated that the use of the boosting ensemble technique consistently improved the correlation between observed and predicted SPIs. In addition, the use of wavelet analysis improved the prediction results of all models. Overall, the wavelet boosting ANN (WBS-ANN) and wavelet boosting SVR (WBS-SVR) models provided better prediction results compared to the other model types evaluated.
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
Pérez-García, Arantxa; Pérez-Durán, Pablo; Wossning, Thomas; Sernandez, Isora V; Mur, Sonia M; Cañamero, Marta; Real, Francisco X; Ramiro, Almudena R
2015-08-17
Activation-induced deaminase (AID) initiates secondary antibody diversification in germinal center B cells, giving rise to higher affinity antibodies through somatic hypermutation (SHM) or to isotype-switched antibodies through class switch recombination (CSR). SHM and CSR are triggered by AID-mediated deamination of cytosines in immunoglobulin genes. Importantly, AID activity in B cells is not restricted to Ig loci and can promote mutations and pro-lymphomagenic translocations, establishing a direct oncogenic mechanism for germinal center-derived neoplasias. AID is also expressed in response to inflammatory cues in epithelial cells, raising the possibility that AID mutagenic activity might drive carcinoma development. We directly tested this hypothesis by generating conditional knock-in mouse models for AID overexpression in colon and pancreas epithelium. AID overexpression alone was not sufficient to promote epithelial cell neoplasia in these tissues, in spite of displaying mutagenic and genotoxic activity. Instead, we found that heterologous AID expression in pancreas promotes the expression of NKG2D ligands, the recruitment of CD8(+) T cells, and the induction of epithelial cell death. Our results indicate that AID oncogenic potential in epithelial cells can be neutralized by immunosurveillance protective mechanisms.
Pérez-García, Arantxa; Pérez-Durán, Pablo; Wossning, Thomas; Sernandez, Isora V; Mur, Sonia M; Cañamero, Marta; Real, Francisco X; Ramiro, Almudena R
2015-01-01
Activation-induced deaminase (AID) initiates secondary antibody diversification in germinal center B cells, giving rise to higher affinity antibodies through somatic hypermutation (SHM) or to isotype-switched antibodies through class switch recombination (CSR). SHM and CSR are triggered by AID-mediated deamination of cytosines in immunoglobulin genes. Importantly, AID activity in B cells is not restricted to Ig loci and can promote mutations and pro-lymphomagenic translocations, establishing a direct oncogenic mechanism for germinal center-derived neoplasias. AID is also expressed in response to inflammatory cues in epithelial cells, raising the possibility that AID mutagenic activity might drive carcinoma development. We directly tested this hypothesis by generating conditional knock-in mouse models for AID overexpression in colon and pancreas epithelium. AID overexpression alone was not sufficient to promote epithelial cell neoplasia in these tissues, in spite of displaying mutagenic and genotoxic activity. Instead, we found that heterologous AID expression in pancreas promotes the expression of NKG2D ligands, the recruitment of CD8+ T cells, and the induction of epithelial cell death. Our results indicate that AID oncogenic potential in epithelial cells can be neutralized by immunosurveillance protective mechanisms. PMID:26282919
Pérez-García, Arantxa; Pérez-Durán, Pablo; Wossning, Thomas; Sernandez, Isora V; Mur, Sonia M; Cañamero, Marta; Real, Francisco X; Ramiro, Almudena R
2015-10-01
Activation-induced deaminase (AID) initiates secondary antibody diversification in germinal center B cells, giving rise to higher affinity antibodies through somatic hypermutation (SHM) or to isotype-switched antibodies through class switch recombination (CSR). SHM and CSR are triggered by AID-mediated deamination of cytosines in immunoglobulin genes. Importantly, AID activity in B cells is not restricted to Ig loci and can promote mutations and pro-lymphomagenic translocations, establishing a direct oncogenic mechanism for germinal center-derived neoplasias. AID is also expressed in response to inflammatory cues in epithelial cells, raising the possibility that AID mutagenic activity might drive carcinoma development. We directly tested this hypothesis by generating conditional knock-in mouse models for AID overexpression in colon and pancreas epithelium. AID overexpression alone was not sufficient to promote epithelial cell neoplasia in these tissues, in spite of displaying mutagenic and genotoxic activity. Instead, we found that heterologous AID expression in pancreas promotes the expression of NKG2D ligands, the recruitment of CD8(+) T cells, and the induction of epithelial cell death. Our results indicate that AID oncogenic potential in epithelial cells can be neutralized by immunosurveillance protective mechanisms. PMID:26282919
Muniraj, Inbarasan; Guo, Changliang; Lee, Byung-Geun; Sheridan, John T
2015-06-15
We present a method of securing multispectral 3D photon-counted integral imaging (PCII) using classical Hartley Transform (HT) based encryption by employing optical interferometry. This method has the simultaneous advantages of minimizing complexity by eliminating the need for holography recording and addresses the phase sensitivity problem encountered when using digital cameras. These together with single-channel multispectral 3D data compactness, the inherent properties of the classical photon counting detection model, i.e. sparse sensing and the capability for nonlinear transformation, permits better authentication of the retrieved 3D scene at various depth cues. Furthermore, the proposed technique works for both spatially and temporally incoherent illumination. To validate the proposed technique simulations were carried out for both the 2D and 3D cases. Experimental data is processed and the results support the feasibility of the encryption method. PMID:26193568
2005-07-01
Aniso2d is a two-dimensional seismic forward modeling code. The earth is parameterized by an X-Z plane in which the seismic properties Can have monoclinic with x-z plane symmetry. The program uses a user define time-domain wavelet to produce synthetic seismograms anrwhere within the two-dimensional media.
Qiao, Shaoyu; Torkamani-Azar, Mastaneh; Salama, Paul; Yoshida, Ken
2012-10-01
Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.
GENSHELL: A genesis database 2D to 3D shell transformation program
Sjaardema, G.D.
1993-07-01
GENSHELL is a three-dimensional shell mesh generation program. The three-dimensional shell mesh is generated by mapping a two-dimensional quadrilateral mesh into three dimensions according to one of several types of transformations: translation, mapping onto a spherical, ellipsoidal, or cylindrical surface, and mapping onto a user-defined spline surface. The generated three-dimensional mesh can then be reoriented by offsetting, reflecting about an axis, revolving about an axis, and scaling the coordinates. GENSHELL can be used to mesh complex three-dimensional geometries composed of several sections when the sections can be defined in terms of transformations of two-dimensional geometries. The code GJOIN is then used to join the separate sections into a single body. GENSHELL updates the EXODUS quality assurance and information records to help track the codes and files used to generate the mesh. GENSHELL reads and writes two-dimensional and three-dimensional mesh databases in the GENESIS database format; therefore, it is compatible with the preprocessing, postprocessing, and analysis codes in the Sandia National Laboratories Engineering Analysis Code Access System (SEACAS).
Combining Haar Wavelet and Karhunen Loeve Transforms for Medical Images Watermarking
Hajjaji, Mohamed Ali; Bourennane, El-Bay; Ben Abdelali, Abdessalem; Mtibaa, Abdellatif
2014-01-01
This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient's data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate) can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and Karhunen Loeve Transforms is proposed. The Karhunen Loeve Transform is applied on the subblocks (sized 8 × 8) of the different wavelet coefficients (in the HL2, LH2, and HH2 subbands). In this manner, the watermark will be adapted according to the energy values of each of the Karhunen Loeve components, with the aim of ensuring a better watermark extraction under various types of attacks. For the correct identification of inserted data, the use of an Errors Correcting Code (ECC) mechanism is required for the check and, if possible, the correction of errors introduced into the inserted data. Concerning the enhancement of the imperceptibility factor, the main goal is to determine the optimal value of the visibility factor, which depends on several parameters of the DWT and the KLT transforms. As a first step, a Fuzzy Inference System (FIS) has been set up and then applied to determine an initial visibility factor value. Several features extracted from the Cooccurrence matrix are used as an input to the FIS and used to determine an initial visibility factor for each block; these values are subsequently reweighted in function of the eigenvalues extracted from each subblock. Regarding the integration rate, the previous works insert one bit per coefficient. In our proposal, the
Combining Haar Wavelet and Karhunen Loeve Transforms for medical images watermarking.
Hajjaji, Mohamed Ali; Bourennane, El-Bay; Ben Abdelali, Abdessalem; Mtibaa, Abdellatif
2014-01-01
This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient's data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate) can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and Karhunen Loeve Transforms is proposed. The Karhunen Loeve Transform is applied on the subblocks (sized 8 × 8) of the different wavelet coefficients (in the HL2, LH2, and HH2 subbands). In this manner, the watermark will be adapted according to the energy values of each of the Karhunen Loeve components, with the aim of ensuring a better watermark extraction under various types of attacks. For the correct identification of inserted data, the use of an Errors Correcting Code (ECC) mechanism is required for the check and, if possible, the correction of errors introduced into the inserted data. Concerning the enhancement of the imperceptibility factor, the main goal is to determine the optimal value of the visibility factor, which depends on several parameters of the DWT and the KLT transforms. As a first step, a Fuzzy Inference System (FIS) has been set up and then applied to determine an initial visibility factor value. Several features extracted from the Cooccurrence matrix are used as an input to the FIS and used to determine an initial visibility factor for each block; these values are subsequently reweighted in function of the eigenvalues extracted from each subblock. Regarding the integration rate, the previous works insert one bit per coefficient. In our proposal, the
NASA Astrophysics Data System (ADS)
Li, Yuanyuan; Braitenberg, Carla; Yang, Yushan
2013-03-01
A slightly bended gravity high along the Chad lineament in Central North Africa is analyzed and interpreted by the continuous wavelet transform (CWT) method. We use scale normalization on the continuous wavelet transform, allowing analysis of the gravity field in order to determine the sources at different depths. By focusing on homogenous standard sources, such as sphere or cube, horizontal cylinder or prism, sheet and infinite step, we derive the relationships between the source depth and pseudo-wavenumber. Then the source depth can be recovered from tracing the maximal values of the modulus of the complex wavelet coefficients in the CWT-based scalograms that are function of the pseudo-wavenumber. The studied area includes a central gravity high up to 75 km wide, and a secondary high that occurs at the southern part of the anomaly. The interpretation of the depth slices and vertical sections of the modulus maxima of the complex wavelet coefficients allows recognition of a relatively dense terrane located at middle crustal levels (10-25 km depth). A reasonable geological model derived from the 2.5D gravity forward modelling indicates the presence of high density bodies, probably linked to a buried suture, which were thrusted up into the mid-crust during the Neo-Proterozoic terrane collisions between the Saharan metacraton and the Arabian-Nubian shield. We conclude that the Chad line delineates a first order geological boundary, missing on the geologic maps.
Lamb wave feature extraction using discrete wavelet transformation and Principal Component Analysis
NASA Astrophysics Data System (ADS)
Ghodsi, Mojtaba; Ziaiefar, Hamidreza; Amiryan, Milad; Honarvar, Farhang; Hojjat, Yousef; Mahmoudi, Mehdi; Al-Yahmadi, Amur; Bahadur, Issam
2016-04-01
In this research, a new method is presented for eliciting the proper features for recognizing and classifying the kinds of the defects by guided ultrasonic waves. After applying suitable preprocessing, the suggested method extracts the base frequency band from the received signals by discrete wavelet transform and discrete Fourier transform. This frequency band can be used as a distinctive feature of ultrasonic signals in different defects. Principal Component Analysis with improving this feature and decreasing extra data managed to improve classification. In this study, ultrasonic test with A0 mode lamb wave is used and is appropriated to reduce the difficulties around the problem. The defects under analysis included corrosion, crack and local thickness reduction. The last defect is caused by electro discharge machining (EDM). The results of the classification by optimized Neural Network depicts that the presented method can differentiate different defects with 95% precision and thus, it is a strong and efficient method. Moreover, comparing the elicited features for corrosion and local thickness reduction and also the results of the two's classification clarifies that modeling the corrosion procedure by local thickness reduction which was previously common, is not an appropriate method and the signals received from the two defects are different from each other.
NASA Astrophysics Data System (ADS)
Xuan, Songbai; Shen, Chongyang; Li, Hui; Tan, Hongbo
2016-07-01
The Chuan-Dian tectonic block is a transitional zone between the Tibetan Plateau and the South China block. The crustal structure in this region has been studied in several ways, and in this work we present Bouguer gravity anomaly data with which to investigate the Chuan-Dian block and surrounding regions. Regional and local anomalies are decomposed using a method of discrete wavelet transform (DWT), and furthermore, the relief of the Moho is inverted based on the regional anomalies. Results of the transform show that there is a distinct belt of regional anomalies on the east and southeast margins of the Tibetan Plateau. In addition, there are two distinct gradient belts evident in the maps of the local gravity anomalies. The first of these, in the western Indo-China block, has a north-south strike with high anomalies around this belt, and the second is along the Longmenshan fault zone in the eastern margin of the Tibetan Plateau. The Chuan-Dian block can be divided into two discrete parts, separated by a broad and indistinct boundary observed from the fifth-order DWT detail and Moho relief. The DWT details reveal that parallel anomalies existing in the Indo-China block region were induced by subduction of the Burmese block. We conclude that the clockwise rotation of the Chuan-Dian block was synthetically affected by the extrusion of the Tibetan lithosphere and subduction of the Burmese block.
A Study of Two Dimensional Electron Gas Using 2D Fourier Transform Spectroscopy
NASA Astrophysics Data System (ADS)
McIntyre, Carl; Paul, Jagannath; Karaiskaj, Denis
2015-03-01
The dephasing of FES was measured in a symmetrically modulation doped 12 nm single quantum well GaAs/AlGaAs two dimensional electron gas system using time integrated four wave mixing (TIFWM) and a two dimensional Fourier transform spectroscopy (2DFTS). At high in-well carrier densities of ~4 x 1011 cm-2, many body effects that are prevalent and measurable with non-linear optical spectroscopy. Effects of exciton-exciton and exciton-phonon scattering events, exciton populations, and biexciton formation are detectable at these carrier concentrations. Homogeneous linewidths obtained from 2DFT and TIFWM yield a zero Kelvin linewidth of 1.42 meV and an acoustic phonon scattering coefficient of 158 μ eV/K. These observations indicate a rapid increase in homogeneous linewidth with increased temperature. NSF REU Grant # DMR-1263066: REU Site in Applied Physics at USF.
Adaptive Multilinear Tensor Product Wavelets.
Weiss, Kenneth; Lindstrom, Peter
2016-01-01
Many foundational visualization techniques including isosurfacing, direct volume rendering and texture mapping rely on piecewise multilinear interpolation over the cells of a mesh. However, there has not been much focus within the visualization community on techniques that efficiently generate and encode globally continuous functions defined by the union of multilinear cells. Wavelets provide a rich context for analyzing and processing complicated datasets. In this paper, we exploit adaptive regular refinement as a means of representing and evaluating functions described by a subset of their nonzero wavelet coefficients. We analyze the dependencies involved in the wavelet transform and describe how to generate and represent the coarsest adaptive mesh with nodal function values such that the inverse wavelet transform is exactly reproduced via simple interpolation (subdivision) over the mesh elements. This allows for an adaptive, sparse representation of the function with on-demand evaluation at any point in the domain. We focus on the popular wavelets formed by tensor products of linear B-splines, resulting in an adaptive, nonconforming but crack-free quadtree (2D) or octree (3D) mesh that allows reproducing globally continuous functions via multilinear interpolation over its cells.
Coordinate transformation method for the solution of inverse problem in 2D and 3D scatterometry
NASA Astrophysics Data System (ADS)
Ponnusamy, Sekar
2005-05-01
For scatterometry applications, diffraction analysis of gratings is carried out by using Rigorous Coupled Wave Analysis (RCWA). Though RCWA method is originally developed for lamellar gratings, arbitrary profiles can be analyzed using staircase approximation with S-Matrix propagation of field components. For improved accuracy, more number of Fourier waves need to be included in Floquet-Bloch expansion of the field components and also more number of slices are to be made in staircase approximation. These requirements increase the time required for the analysis. A coordinate transformation method (CTM) developed by Chandezon et. al renders the arbitrary grating profile into a plane surface in the new coordinate system and hence it does not require slicing. This method is extended to 3D structures by several authors notably, by Harris et al for non-orthogonal unit cells and by Granet for correct Fourier expansion. Also extended is to handle sharp-edged gratings through adaptive spatial resolution. In this paper, an attempt is made to employ CTM with correct Fourier expansion in conjunction with adaptive spatial resolution, for scatterometry applications. A MATLAB program is developed, and thereby, demonstrated that CTM can be used for diffraction analysis of trapezoidal profiles that are typically encountered in scatterometry applications.
NASA Astrophysics Data System (ADS)
Dai, Xiaoyan; Guo, Zhongyang; Zhang, Liquan; Xu, Wencheng
2009-12-01
Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.
NASA Astrophysics Data System (ADS)
Zhou, H.; Srinivasan, S.; Li, L.; Bryant, S. L.
2013-12-01
Uncertainty in prediction of flow performance stems from the uncertainty in model parameters such as conductivity, porosity etc., to a large extent, while the characterization of the model parameters is demanding due to the inherent heterogeneity of geologic structures. Inverse modeling approaches attempt to identify the unknown model structures and corresponding parameters by integrating observation data. Several inverse methods have been proposed in the literature ranging from trial-and-error methods to advanced ensemble Kalman filter assimilation, including those that use multiple point statistics to characterize complex geologic structures. However, these methods are hindered by the huge amount of data accumulated with time, for instance, the pressure data are recorded at very fine time intervals from the very early stage of bore hole drilling to mature production period. Assimilation of such large amount of data can be a computational burden to the inverse methods. The object of this work is to propose a computationally efficient approach to analyze the long observation records in order to recognize the subsurface structures, especially flow connectivity which plays a critical role in transport prediction. Wavelet transform is found to be a powerful technique that transforms data into different components and analyzes each component at corresponding scale. By analyzing the components transformed we relate the characteristics of the heterogeneity to signature in the production/injection records. Combining components at different scales we are able to recognize connectivity between wells, and thereby identify complex structure in aquifers. The method is demonstrated in a synthetic example where CO2 is injected into a deep saline aquifer for sequestration. The method is computationally efficient since it involves no iterative forward simulation or sensitivity matrix computation. Once the important episodes have been identified in the dynamic data, inverse
NASA Astrophysics Data System (ADS)
Tzanis, Andreas
2013-04-01
The Ground Probing Radar (GPR) has become a valuable means of exploring thin and shallow structures for geological, geotechnical, engineering, environmental, archaeological and other work. GPR images usually contain geometric (orientation/dip-dependent) information from point scatterers (e.g. diffraction hyperbolae), dipping reflectors (geological bedding, structural interfaces, cracks, fractures and joints) and other conceivable structural configurations. In geological, geotechnical and engineering applications, one of the most significant objectives is the detection of fractures, inclined interfaces and empty or filled cavities frequently associated with jointing/faulting. These types of target, especially fractures, are usually not good reflectors and are spatially localized. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is notoriously susceptible to noise. Quite frequently, extraneous (natural or anthropogenic) interference and systemic noise swamp the data with unusable information that obscures, or even conceals the reflections from such targets. In many cases, the noise has definite directional characteristics (e.g. clutter). Raw GPR data require post-acquisition processing, as they usually provide only approximate target shapes and distances (depths). The purpose of this paper is to investigate the Curvelet Transform (CT) as a means of S/N enhancement and information retrieval from 2-D GPR sections (B-scans), with particular emphasis placed on the problem of recovering features associated with specific temporal or spatial scales and geometry (orientation/dip). The CT is a multiscale and multidirectional expansion that formulates a sparse representation of the input data set (Candès and Donoho, 2003a, 2003b, 2004; Candés et al., 2006). A signal representation is sparse when it describes the signal as a superposition of a small number of components. What makes the CT appropriate for
Schlenke, Jan; Hildebrand, Lars; Moros, Javier; Laserna, J Javier
2012-11-19
Spectral signals are often corrupted by noise during their acquisition and transmission. Signal processing refers to a variety of operations that can be carried out on measurements in order to enhance the quality of information. In this sense, signal denoising is used to reduce noise distortions while keeping alterations of the important signal features to a minimum. The minimization of noise is a highly critical task since, in many cases, there is no prior knowledge of the signal or of the noise. In the context of denoising, wavelet transformation has become a valuable tool. The present paper proposes a noise reduction technique for suppressing noise in laser-induced breakdown spectroscopy (LIBS) signals using wavelet transform. An extension of the Donoho's scheme, which uses a redundant form of wavelet transformation and an adaptive threshold estimation method, is suggested. Capabilities and results achieved on denoising processes of artificial signals and actual spectroscopic data, both corrupted by noise with changing intensities, are presented. In order to better consolidate the gains so far achieved by the proposed strategy, a comparison with alternative approaches, as well as with traditional techniques, is also made.
Dinç, Erdal; Ragno, Gaetano; Ioele, Giuseppina; Baleanu, Dumitru
2006-01-01
Fractional wavelet transform (FWT) was applied to the original absorption spectra of lacidipine (LAC) and its photodegradation product (LACD), and the resulting FWT spectra were processed by continuous wavelet transform (CWT) and multilinear regression calibration (MLRC) for the simultaneous quantitative analysis of both products in their binary mixtures. These methods do not require any chemical separation step and chemical complex reaction to obtain a detectable signal for the degradation product. By using the Mexican hat function, 2 calibration functions for LAC and LACD were obtained by measuring the CWT transformed signals at 416.1 nm for LAC and 414.6 nm for LACD, after FWT processing of the original absorption spectra. The calibration graphs were linear in the concentration range of 5.08-40.64 microg/mL for LAC and 0.51-8.16 microg/mL for LACD. The limit of detection and the limit of quantitation were found to be 0.289 and 0.956 microg/mL for LAC and 0.036 and 0.118 microg/mL for LACD, respectively. For comparison, the MLRC algorithm was applied to the linear regression functions for the individual drug and its photoproduct. In this approach, a set of linear regression functions was obtained from the relationship between concentrations and FWT signals in the wavelength range 411.0-412.4 nm. Both methods were applied to the quantitative evaluation of LAC and LACD in laboratory and pharmaceutical samples, and produced very satisfactory results.
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2015-09-01
Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.
NASA Astrophysics Data System (ADS)
Heidary, Mohammad
2015-06-01
The variations of pore fluid energy encoded within resistivity well logs can be considered as a significant attribute in the determination of reservoir fluid contacts. As a paramount technique in isolation and manipulation of certain patterns hidden in masses of data, wavelet analysis can effectively unveil this attribute. In this study, the discrete wavelet transform was employed on new well logs generated by kernel principal component analysis to monitor the pore fluid energy of pay zones at two previously appraised wells. An expert wavelet-based model was extracted by revealing the latent pattern of pore fluid energy variations. This model was then used to specify the gas and oil interface in a target well contiguous with the appraised wells. The gas and oil interface obtained from the expert wavelet-based model was confirmed by the drill stem test analysis. Results of this investigation suggest that monitoring pore fluid energy with such a method can be considered a highly functional attribute in determining the gas and oil interface.
NASA Astrophysics Data System (ADS)
Zheng, Jincun; Tang, Zhilie; He, Yongheng; Guo, Lina
2008-05-01
This report presents a practical analytical method of photoacoustic (PA) spectroscopy that is based on wavelet transform (WT) and the first-derivative PA spectrum. An experimental setup is specially designed to obtain the first-derivative spectrum, which aims to identify some unnoticeable absorption peaks in the normal PA spectrum. To enhance the detectability of overlapping spectral bands, the WT is used to decompose the PA spectrum signals into a series of localized contributions (details and approximation) on the basis of the frequency. For the decomposed contributions do not change the absorption peak position of PA spectrum, one can retrieve the weak absorption signals by the decomposed result of WT. Because of the use of derivative spectroscopy and WT, three unnoticeable absorption peaks that are hidden in the PA spectrum of carbon absorption are precisely retrieved, the wavelengths of which are 699.7, 752.7, and 775.5nm, respectively. This analytical method, which has the virtue of using a physical method and using a computer software method, can achieve great sensitivity and accuracy for PA spectral analysis.
Efficient maximal repeat finding using the burrows-wheeler transform and wavelet tree.
Külekci, M Oğuzhan; Vitter, Jeffrey Scott; Xu, Bojian
2012-01-01
Finding repetitive structures in genomes and proteins is important to understand their biological functions. Many data compressors for modern genomic sequences rely heavily on finding repeats in the sequences. The notion of maximal repeats captures all the repeats in the data in a space-efficient way. Prior work on maximal repeat finding used either a suffix tree or a suffix array along with other auxiliary data structures. Their space usage is 19--50 times the text size with the best engineering efforts, prohibiting their usability on massive data. Our technique uses the Burrows-Wheeler Transform and wavelet trees. For data sets consisting of natural language texts, the space usage of our method is no more than three times the text size. For genomic sequences stored using one byte per base, the space usage is less than double the sequence size. Our method is also orders of magnitude faster than the prior methods for processing massive texts, since the prior methods must use external memory. For the first time, our method enables a desktop computer with 8GB internal memory to find all the maximal repeats in the whole human genome in less than 17 hours. We have implemented our method as general-purpose open-source software for public use.
Discrete wavelet transform-based spatial-temporal approach for quantized video watermarking
NASA Astrophysics Data System (ADS)
Faragallah, Osama S.
2011-07-01
We propose a new public digital watermarking technique for video copyright protection working in the discrete wavelet transform (DWT) domain. The proposed scheme is a combination of spread-spectrum and quantization-based watermarking. The proposed scheme is characterized by two achievements: (i) a spread-spectrum technique is used to spread the power spectrum of the watermark data and (ii) an error correction code is applied and embeds the watermark with spatial and temporal redundancy. The goal of these two achievements is to increase robustness against attacks, protect the watermark against bit errors, and achieve a very good perceptual quality. The effectiveness of the proposed scheme is verified through a series of experiments in which a number of video and standard image-processing attacks are conducted. The proposed scheme achieves a very good perceptual quality with mean peak signal-to-noise-ratio values of the watermarked videos of >40 dB and high resistance to a large spectrum of attacks.
The relevance of the cross-wavelet transform in the analysis of human interaction - a tutorial.
Issartel, Johann; Bardainne, Thomas; Gaillot, Philippe; Marin, Ludovic
2014-01-01
This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiology - neuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it. Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes.
Hamaneh, Mehdi Bagheri; Chitravas, Numthip; Kaiboriboon, Kitti; Lhatoo, Samden D; Loparo, Kenneth A
2014-06-01
The electrical potential produced by the cardiac activity sometimes contaminates electroencephalogram (EEG) recordings, resulting in spiky activities that are referred to as electrocardiographic (EKG) artifact. For a variety of reasons it is often desirable to automatically detect and remove these artifacts. Especially, for accurate source localization of epileptic spikes in an EEG recording from a patient with epilepsy, it is of great importance to remove any concurrent artifact. Due to similarities in morphology between the EKG artifacts and epileptic spikes, any automated artifact removal algorithm must have an extremely low false-positive rate in addition to a high detection rate. In this paper, an automated algorithm for removal of EKG artifact is proposed that satisfies such criteria. The proposed method, which uses combines independent component analysis and continuous wavelet transformation, uses both temporal and spatial characteristics of EKG related potentials to identify and remove the artifacts. The method outperforms algorithms that use general statistical features such as entropy and kurtosis for artifact rejection.
A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis.
Yang, Lina; Tang, Yuan Yan; Lu, Yang; Luo, Huiwu
2015-01-01
One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts. PMID:26357222
Evaluating Streamflow Changes in Continental U.S. Using Wavelet Transformation
NASA Astrophysics Data System (ADS)
Tamaddun, K. A.; Kalra, A.; Ahmad, S.
2015-12-01
This study focused on investigating the long term seasonal trends (gradual change and shifts) in 600 streamflow stations across the continental United States with each station having a continuous streamflow data of at least 30 years. The non-parametric Mann-Kendall test, with appropriate modifications to account for persistence in data, was used to identify the trends whereas the non-parametric Pettitt test was used to identify the shifts. Discrete Wavelet Transformation (DWT) was further applied on a subset of the selected stations (237/600 were selected for DWT) to evaluate the most significant periodicities or recurrence intervals present in the change patterns. The results showed a clear increase in streamflow in the northeast and upper-central regions whereas southeast and northwest regions underwent decrease. The central regions had assorted results while number of stations with decreasing trends was observed to increase from east to west. The shifts were found to be more spatially distributed across the whole study area and followed similar patterns as the trends. The seasons also showed certain patterns in all time-scales under DWT. The presence of persistence was also observed to increase with the increasing time-scales. The results may assist water managers to efficiently plan and manage the water resources under changing climatic conditions across continental United States.
Chamanzar, Alireza; Malekmohammadi, Alireza; Bahrani, Masih; Shabany, Mahdi
2015-01-01
The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML), electrooculography (EOG) artifact removal method to remove eye-related artifacts to significantly improve the detection performance. We have applied this technique to our locally recorded Emotiv(®) data set of 6 healthy subjects, where an average detection selectivity of 85 ± 6% and sensitivity of 88 ± 7.7% is achieved with a temporal precision in the range of -1250 to 367 ms in onset detections of single-trials.
Dual-tree complex wavelet transform applied on color descriptors for remote-sensed images retrieval
NASA Astrophysics Data System (ADS)
Sebai, Houria; Kourgli, Assia; Serir, Amina
2015-01-01
This paper highlights color component features that improve high-resolution satellite (HRS) images retrieval. Color component correlation across image lines and columns is used to define a revised color space. It is designed to simultaneously take both color and neighborhood information. From this space, color descriptors, namely rotation invariant uniform local binary pattern, histogram of gradient, and a modified version of local variance are derived through dual-tree complex wavelet transform (DT-CWT). A new color descriptor called smoothed local variance (SLV) using an edge-preserving smoothing filter is introduced. It is intended to offer an efficient way to represent texture/structure information using an invariant to rotation descriptor. This descriptor takes advantage of DT-CWT representation to enhance the retrieval performance of HRS images. We report an evaluation of the SLV descriptor associated with the new color space using different similarity distances in our content-based image retrieval scheme. We also perform comparison with some standard features. Experimental results show that SLV descriptor allied to DT-CWT representation outperforms the other approaches.
Mousavi, Seyed Mortaza; Adamoğlu, Ahmet; Demiralp, Tamer; Shayesteh, Mahrokh G.
2014-01-01
Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. We analyzed EEG changes for phase coupling between delta and alpha subbands using a new algorithm for depth of general anesthesia measurement based on complex wavelet transform (CWT) in patients anesthetized by Propofol. Entropy and histogram of modulated signals were calculated by taking bispectral index (BIS) values as reference. Entropies corresponding to different BIS intervals using Mann-Whitney U test showed that they had different continuous distributions. The results demonstrated that there is a phase coupling between 3 and 4 Hz in delta and 8-9 Hz in alpha subbands and these changes are shown better at the channel T7 of EEG. Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented. PMID:25276220
Analysis of the orbit distortion by the use of the wavelet transform
Matsushita, T.; Takao, M.; Aoyagi, H.; Takeuchi, M.; Tanaka, H.; Agui, A.; Yoshigoe, A.; Nakatani, T.
2004-05-12
We have adopted matching pursuit algorithm of discrete wavelet transform (DWT) for the analysis of the beam position shift correlated with the motion of insertion device (ID). The beam position data measured by the rf beam position monitors have included high-frequency 'noises' and fluctuation of background level. Precise evaluation of the electron beam position shift correlated with the motion of the ID is required for estimation of the steering magnet currents in order to suppress the closed orbit distortion (COD). The DWT is a powerful tool for frequency analysis and data processing. The analysis of DWT was applied to the beam position shift correlated with the phase motion of APPLE-2 type undulator (ID23) in SPring-8. The result of the analysis indicated that 'noises' are mainly composed of the components of 50 {approx} 6.25Hz and < 0.1Hz. We carried out the data processing to remove the 'noises' by the matching pursuit algorithm. Then we have succeeded in suppressing the COD within 2 {mu}m by the use of the steering magnet currents calculated from the processed data.
A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform
Yu, Bin; Zhang, Yan
2013-01-01
The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy. PMID:23289014
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.
A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis.
Yang, Lina; Tang, Yuan Yan; Lu, Yang; Luo, Huiwu
2015-01-01
One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts.
A hybrid wavelet transform based short-term wind speed forecasting approach.
Wang, Jujie
2014-01-01
It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.
Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks
Ashok, Vajravelu; Kumar, Nirmal
2013-01-01
Background: Early and non-invasive determination of blood glucose level is of great importance. We aimed to present a new technique to accurately infer the blood glucose concentration in peripheral blood flow using non-invasive optical monitoring system. Methods: The data for the research were obtained from 900 individuals. Of them, 750 people had diabetes mellitus (DM). The system was designed using a helium neon laser source of 632.8 nm wavelength with 5mW power, photo detectors and digital storage oscilloscope. The laser beam was directed through a single optical fiber to the index finger and the scattered beams were collected by the photo detectors placed circumferentially to the transmitting fiber. The received signals were filtered using band pass filter and finally sent to a digital storage oscilloscope. These signals were then decomposed into approximation and detail coefficients using modified Haar Wavelet Transform. Back propagation neural and radial basis functions were employed for the prediction of blood glucose concentration. Results: The data of 450 patients were randomly used for training, 225 for testing and the rest for validation. The data showed that outputs from radial basis function were nearer to the clinical value. Significant variations could be seen from signals obtained from patients with DM and those without DM. Conclusion: The proposed non-invasive optical glucose monitoring system is able to predict the glucose concentration by proving that there is a definite variation in hematological distribution between patients with DM and those without DM. PMID:23645958
Adamczyk, Marek; Genzel, Lisa; Dresler, Martin; Steiger, Axel; Friess, Elisabeth
2015-01-01
Mounting evidence for the role of sleep spindles in neuroplasticity has led to an increased interest in these non-rapid eye movement (NREM) sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here, we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform (CWT) and individual adjustment of slow and fast spindle frequency ranges. Eighteen nap recordings of ten subjects were used for algorithm validation. Our method was compared with both a human scorer and a commercially available SIESTA spindle detector. For the validation set, mean agreement between our detector and human scorer measured during sleep stage 2 using kappa coefficient was 0.45, whereas mean agreement between our detector and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with a SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. We then applied our method to a study in monozygotic (MZ) and dizygotic (DZ) twins, examining the genetic component of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence on variance of all slow spindle parameters, weaker genetic effect on fast spindles, and no effects on fast spindle density and number during stage 2 sleep. PMID:26635577
NASA Astrophysics Data System (ADS)
Tzanis, Andreas
2015-04-01
The Ground Probing Radar (GPR) is a valuable tool for near surface geological, geotechnical, engineering, environmental, archaeological and other work. GPR images of the subsurface frequently contain geometric information (constant or variable-dip reflections) from various structures such as bedding, cracks, fractures etc. Such features are frequently the target of the survey; however, they are usually not good reflectors and they are highly localized in time and in space. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is very sensitive to broadband noise from buried small objects, electromagnetic anthropogenic activity and systemic factors, which frequently blurs the reflections from such targets. The purpose of this paper is to investigate the Curvelet Transform (CT) as a means of S/N enhancement and information retrieval from 2-D GPR sections, with particular emphasis on the recovery of features associated with specific temporal or spatial scales and geometry (orientation/dip). The CT is a multiscale and multidirectional expansion that formulates an optimally sparse representation of bivariate functions with singularities on twice-differentiable (C2-continuous) curves (e.g. edges) and allows for the optimal, whole or partial reconstruction of such objects. The CT can be viewed as a higher dimensional extension of the wavelet transform: whereas discrete wavelets are isotropic and provide sparse representations of functions with point singularities, curvelets are highly anisotropic and provide sparse representations of functions with singularities on curves. A GPR section essentially comprises a spatio-temporal sampling of the transient wavefield which contains different arrivals that correspond to different interactions with wave scatterers in the subsurface (wavefronts). These are generally longitudinally piecewise smooth and transversely oscillatory, i.e. they comprise edges. Curvelets can detect
Some notes on the application of discrete wavelet transform in image processing
Caria, Egydio C. S.; Costa A, Trajano A. de; Rebello, Joao Marcos A.
2011-06-23
Mathematical transforms are used in signal processing in order to extract what is known as 'hidden' information. One of these mathematical tools is the Discrete Wavelet Transform (DWT), which has been increasingly employed in non-destructive testing and, more specifically, in image processing. The main concern in the present work is to employ DWT to suppress noise without losing relevant image features. However, some aspects must be taken into consideration when applying DWT in image processing, mainly in the case of weld radiographs, in order to achieve consistent results. Three topics were selected as representative of these difficulties, as follows: 1) How can image matrix be filled to fit the 2{sup n} lines and 2{sup n} rows requirement? 2) How can the most suitable decomposition level of the DWT function and the correct choice of their coefficient suppression be selected? 3) Is there any influence of the scanning direction and the weld radiograph image, e.g., longitudinal or transversal, on the final processing image? It is known that some artifacts may be present in weld radiograph images. Indeed, the weld surface is frequently rough and rippled, what can be seen as gray level variation on the radiograph, being sometimes mistaken as defective areas. Depending on the position of these artifacts, longitudinal or transversal to the weld bead, they may have different influences on the image processing procedure. This influence is clearly seen in the distribution of the DWT Function coefficients. In the present work, examples of two weld radiographs of quite different image quality were given in order to exemplify it.
Analysis of complex faulting: Wavelet transform, multiple datasets and realistic fault geometry
NASA Astrophysics Data System (ADS)
Ji, Chen
This thesis presents the studies of two recent large and well-recorded earthquakes, the 1999 Hector Mine and Chi-Chi earthquakes. A new procedure for the determination of rupture complexity from a joint inversion of static and seismic data was first developed. This procedure applies a wavelet transform to separate seismic information related to the spatial and temporal slip history, then uses a simulated annealing algorithm to determine the finite-fault model that minimizes the objective function described in terms of wavelet coefficients. This method is then applied to simultaneously invert the slip amplitude, slip direction, rise time and rupture velocity distributions of the Hector Mine and Chi-Chi earthquakes with both seismic and geodetic data. Two slip models are later verified with independent datasets. Results indicate that the seismic moment of the Hector Mine earthquake is 6.28 x 1019 Nm, which is distributed along a "Y" shape fault geometry with three segments. The average slip is 1.5 m with peak amplitudes as high as 7 m. The fault rupture has an average slip duration of 3.5 sec and a slow average rupture velocity of 1.9 km/sec, resulting in a 14 sec rupture propagation history. The rise time appears to be roughly proportional to slip, and the two branches of "Y" shape fault rupture together. The Chi-Chi earthquake is the best-recorded large earthquake so far. Its seismic moment of 2.7 x 1020 Nm is concentrated on the surface of a "wedge shaped" block. The rupture front propagates with a slow rupture velocity of about 2.0 km/sec. The average slip duration is 7.2 sec. Four interesting results are obtained: (1) The sinuous fault plane strongly affects both spatial and temporal variation in slip history; (2) Long-period peak slip velocity increases as the rupture propagates; (3) The peak slip velocity near the surface is in general higher than on the deeper portion of the fault plane as predicted by dynamic modeling [e.g., Oglesby et al., 1998]; and (4
NASA Astrophysics Data System (ADS)
Zhang, Xi; Mi, Chris Chunting; Masrur, Abul; Daniszewski, David
A wavelet-transform-based strategy is proposed for the power management of hybrid electric vehicles (HEV) with multiple on-board energy sources and energy storage systems including a battery, a fuel cell, and an ultra-capacitor. The proposed wavelet-transform algorithm is capable of identifying the high-frequency transient and real time power demand of the HEV, and allocating power components with different frequency contents to corresponding sources to achieve an optimal power management control algorithm. By using the wavelet decomposition algorithm, a proper combination can be achieved with a properly sized ultra-capacitor dealing with the chaotic high-frequency components of the total power demand, while the fuel cell and battery deal with the low and medium frequency power demand. Thus the system efficiency and life expectancy can be greatly extended. Simulation and experimental results validated the effectiveness of wavelet-transform-based power management algorithm.
Perception-based reversible watermarking for 2D vector maps
NASA Astrophysics Data System (ADS)
Men, Chaoguang; Cao, Liujuan; Li, Xiang
2010-07-01
This paper presents an effective and reversible watermarking approach for digital copyright protection of 2D-vector maps. To ensure that the embedded watermark is insensitive for human perception, we only select the noise non-sensitive regions for watermark embedding by estimating vertex density within each polyline. To ensure the exact recovery of original 2D-vector map after watermark extraction, we introduce a new reversible watermarking scheme based on reversible high-frequency wavelet coefficients modification. Within the former-selected non-sensitive regions, our watermarking operates on the lower-order vertex coordinate decimals with integer wavelet transform. Such operation further reduces the visual distortion caused by watermark embedding. We have validated the effectiveness of our scheme on our real-world city river/building 2D-vector maps. We give extensive experimental comparisons with state-of-the-art methods, including embedding capability, invisibility, and robustness over watermark attacking.
Ye, Linlin; Yang, Dan; Wang, Xu
2014-06-01
A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal. PMID:25219236
Ye, Linlin; Yang, Dan; Wang, Xu
2014-06-01
A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.
NASA Astrophysics Data System (ADS)
Qiu, Z.; Lee, C.-M.; Xu, Z. H.; Sui, L. N.
2016-01-01
We have developed a new active control algorithm based on discrete wavelet transform (DWT) for both stationary and non-stationary noise control. First, the Mallat pyramidal algorithm is introduced to implement the DWT, which can decompose the reference signal into several sub-bands with multi-resolution and provides a perfect reconstruction (PR) procedure. To reduce the extra computational complexity introduced by DWT, an efficient strategy is proposed that updates the adaptive filter coefficients in the frequency domainDeepthi B.B using a fast Fourier transform (FFT). Based on the reference noise source, a 'Haar' wavelet is employed and by decomposing the noise signal into two sub-band (3-band), the proposed DWT-FFT-based FXLMS (DWT-FFT-FXLMS) algorithm has greatly reduced complexity and a better convergence performance compared to a time domain filtered-x least mean square (TD-FXLMS) algorithm. As a result of the outstanding time-frequency characteristics of wavelet analysis, the proposed DWT-FFT-FXLMS algorithm can effectively cancel both stationary and non-stationary noise, whereas the frequency domain FXLMS (FD-FXLMS) algorithm cannot approach this point.
NASA Astrophysics Data System (ADS)
Gallego, A.; Moreno-García, P.; Casanova, Cesar F.
2013-06-01
Structural studies to find defects (in particular delaminations) in composite plates have been very prevalent in the Structural Health Monitoring field. The present work develops a new method to detect delaminations in CFRP (Carbon Fiber Reinforced Polymer) plates. In this paper the method is validated with numerical simulations, which come to support its adequacy for use with real acquisition data. This is done firstly through the implementation of a delaminated plate finite element. Using the classical lamination plate theory, delamination is considered in the kinematic equations through jump functions and additional degrees of freedom. The element allows the introduction of nd delaminations through its thickness. Classical QMITC (Quadrilateral Mixed Interpolation Tensorial Components) and DKQ (Discrete Kirchhoff Quadrilateral) elements are used for the membrane and bending FEM (Finite Element Method) formulation. Second, using the vibration modes obtained with the FEM, a damage location technique based on the variational Ritz method and Wavelet Analysis is proposed. The approach has the advantage of requiring only damaged modes and not the healthy ones. Both FEM simulations and Ritz/Wavelet damage detection schemes are applied in an orthotropic CFRP plate with the stacking sequence [0/90]3S. In addition, the influence of delamination thickness position, boundary conditions and added noise (in order to simulate experimental measures) was studied.
Comparison of Solar Wind Speeds Using Wavelet Transform and Fourier Analysis in IPS Data
NASA Astrophysics Data System (ADS)
Aguilar-Rodriguez, E.; Mejia-Ambriz, J. C.; Jackson, B. V.; Buffington, A.; Romero-Hernandez, E.; Gonzalez-Esparza, J. A.; Rodriguez-Martinez, M.; Hick, P.; Tokumaru, M.; Manoharan, P. K.
2015-09-01
The power spectra of intensity fluctuations in interplanetary scintillation (IPS) observations can be used to estimate solar-wind speeds in the inner heliosphere. We obtain and then compare IPS spectra from both wavelet and Fourier analyses for 12 time series of the radio source 3C48; these observations were carried out at Japan's Solar-Terrestrial Environment Laboratory (STEL) facility, at 327 MHz. We show that wavelet and Fourier analyses yield very similar power spectra. Thus, when fitting a model to spectra to determine solar-wind speeds, both yield comparable results. Although spectra from wavelet and Fourier closely match each other for solar-wind speed purposes, those from the wavelet analysis are slightly cleaner, which is reflected in an apparent level of intensity fluctuations that is enhanced, being ≈ 13 % higher. This is potentially useful for records that show a low signal-to-noise ratio.
Climate signal detection using wavelet transform: How to make a time series sing
Lau, K.M.; Weng, H.
1995-12-01
In this paper, the application of the wavelet transform (WT) to climate time series analyses is introduced. A tutorial description of the basic concept of WT, compared with similar concepts used in music, is also provided. Using an analogy between WT representation of a time series and a music score, the authors illustrate the importance of local versus global information in the time-frequency localization of climate signals. Examples of WT applied to climate data analysis are demonstrated using analytic signals as well as real climate time series. Results of WT applied to two climate time series-that is, a proxy paleoclimate time series with a 2.5-Myr deep-sea sediment record of {gamma}{sup 18}O and a 140-yr monthly record of Northern Hemisphere surface temperature-are presented. The former shows the presence of a 40-kyr and a 100-kyr oscillation and an abrupt transition in the oscillation regime at 0.7 Myr before the present, consistent with previous studies. The latter possesses a myriad of oscillatory modes f rom interannual (2-5 yr), interdecadal (10-12 yr, 20-25 yr, and 40-60 yr), and century ({approximately}180 yr) scales at different periods of the data record. In spite of the large difference in timescales, common features in time-frequency characteristics of these two time series have been identified. These features suggest that the variations of the earth`s climate are consistent with those exhibited by a nonlinear dynamical system under external forcings. 32 refs., 9 figs.
NASA Astrophysics Data System (ADS)
Kulesh, M.; Holschneider, M.; Shardakov, I.
2005-12-01
The problem of the surface elastic wave propagation in the half-space within the framework of the Cosserat continuum has been considered. Medium deformation in this model is described not only by the displacement vector, but also by kinematically independent rotation vector. This model can be used for the description of the media with microstructure, for example concrete, sand, sandy-gravel mixture etc. At the same time the applications of these models almost do not exist in praxis, since there are no reliable data about the material properties in nonsymmetrical elasticity theory and in fact there are no experiments which can demonstrate the effects of couple-stress behavior in solid under deformation. The main result of presented work consist in fact, that within the framework of the Cosserat continuum in half-space besides elliptical Rayleigh wave can be in existence the surface shear wave with only transversal component. Geometrically such wave is equal to Love wave, but in classical elasticity theory existence of the Love wave as surface elastic wave is defined by presence of a layer on a half-space, and while a layer thickness vanishing the Love wave proceeds to a plane wave. Thus, in Cosserat medium the new wave mode is found out, and there is no analogue of it in classical elasticity theory. As a second result of presented work the method of the displacement seismogram inversion has been proposed. This method is based on continues wavelet transform and allows to restore the wave number, phase and group velocities. These results can be effectively used in possible experiments which are aimed at the detection of couple-stress effects in medium and further at the identification of material constants of nonsymmetrical elasticity theory. This work was supported by Russian Foundation of Fundamental Research under project 03-01-00561 and by the Deutsche Forschungsgemeinschaft (DFG) within the framework of the priority program SPP 1114, Mathematical methods for time
Functional decomposition of the human ERG based on the discrete wavelet transform.
Gauvin, Mathieu; Little, John M; Lina, Jean-Marc; Lachapelle, Pierre
2015-01-01
The morphology of the electroretinogram (ERG) can be altered as a result of normal and pathological processes of the retina. However, given that the ERG is almost solely assessed in terms of its amplitude and timing, defining the shape of the ERG waveform so that subtle, physiologically driven, morphological changes can be systematically and reproducibly detected remains a challenging problem. We examined if the discrete wavelet transform (DWT) could meet this challenge. Normal human photopic ERGs evoked to a broad range of luminance intensities (to yield waveforms of various shapes, amplitudes, and timings) were analyzed using DWT descriptors of the ERG. Luminance-response curves that were generated using the various DWT descriptors revealed distinct (p < 0.05) luminance-dependence patterns, indicating that the stimulus luminance differently modulates the various time-frequency components of the ERG and thus its morphology. The latter represents the first attempt to study the luminance-dependence of ERG descriptors obtained with the DWT. Analyses of ERGs obtained from patients affected with ON or OFF retinal pathway anomalies were also presented. We show here for the first time that distinct time-frequency descriptors can be specifically associated to the function of the ON and OFF cone pathway. Therefore, in this study, the DWT revealed reproducible, physiologically meaningful and diagnostically relevant descriptors of the ERG over a wide range of signal amplitudes and morphologies. The DWT analysis thus represents a valuable addition to the electrophysiologist's armamentarium that will improve the quantification and interpretation of normal and pathological ERG responses. PMID:26746684
Upscaling of solute transport in disordered porous media by wavelet transformations
NASA Astrophysics Data System (ADS)
Moslehi, Mahsa; de Barros, Felipe P. J.; Ebrahimi, Fatemeh; Sahimi, Muhammad
2016-10-01
Modeling flow and solute transport in large-scale (e.g.) on the order of 103 m heterogeneous porous media involves substantial computational burden. A common approach to alleviate the problem is to utilize an upscaling method that generates models that require less intensive computations. The method must also preserve the important properties of the spatial distribution of the hydraulic conductivity (K) field. We use an upscaling method based on the wavelet transformations (WTs) that coarsens the computational grid based on the spatial distribution of K. The technique is applied to a porous formation with broadly distributed and correlated K values, and the governing equation for solute transport in the formation is solved numerically. The WT upscaling preserves the resolution of the initial highly-resolved computational grid in the high K zones, as well as that of the zones with sharp contrasts between the neighboring K, whereas the low-K zones are averaged out. To demonstrate the accuracy of the method, we simulate fluid flow and nonreactive solute transport in both the high-resolution and upscaled grids, and compare the concentration profiles and the breakthrough times. The results indicate that the WT upscaling of a K field generates non-uniform upscaled grids with a number of grid blocks that on average is about two percent of the number of the blocks in the original high-resolution computational grids, while the concentration profiles, the breakthrough times and the second moment of the concentration distribution, computed for both models, are virtually identical. A systematic parametric study is also carried out in order to investigate the sensitivity of the method to the broadness of the K field, the nature of the correlations in the field (positive versus negative), and the size of the computational grid. As the broadness of the K field and the size of the computational domain increase, better agreement between the results for the high-resolution and
2013-01-01
Background Autism Spectrum Conditions (ASC) are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC. Methods We analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset. Results Results demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8), such differences were only seen for one electrode pair in the ASC group (T7-T8). No significant differences in EEG power spectra were observed between groups. Conclusions Interhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain. Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals. Overall, the results of this research support the value of WTC in examining the time
Ghorbanian, Parham; Devilbiss, David M; Verma, Ajay; Bernstein, Allan; Hess, Terry; Simon, Adam J; Ashrafiuon, Hashem
2013-06-01
Alzheimer's disease (AD) is associated with deficits in a number of cognitive processes and executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power spectrum develop with the progression of AD. These features have been traditionally characterized with montage recordings and conventional spectral analysis during resting eyes-closed and resting eyes-open (EO) conditions. In this study, we introduce a single lead dry electrode EEG device which was employed on AD and control subjects during resting and activated battery of cognitive and sensory tasks such as Paced Auditory Serial Addition Test (PASAT) and auditory stimulations. EEG signals were recorded over the left prefrontal cortex (Fp1) from each subject. EEG signals were decomposed into sub-bands approximately corresponding to the major brain frequency bands using several different discrete wavelet transforms and developed statistical features for each band. Decision tree algorithms along with univariate and multivariate statistical analysis were used to identify the most predictive features across resting and active states, separately and collectively. During resting state recordings, we found that the AD patients exhibited elevated D4 (~4-8 Hz) mean power in EO state as their most distinctive feature. During the active states, however, the majority of AD patients exhibited larger minimum D3 (~8-12 Hz) values during auditory stimulation (18 Hz) combined with increased kurtosis of D5 (~2-4 Hz) during PASAT with 2 s interval. When analyzed using EEG recording data across all tasks, the most predictive AD patient features were a combination of the first two feature sets. However, the dominant discriminating feature for the majority of AD patients were still the same features as the active state analysis. The results from this small sample size pilot study indicate that although EEG recordings during resting conditions are able to differentiate AD from control subjects, EEG activity
Rezvanian, Saba; Lockhart, Thurmon E.
2016-01-01
Injuries associated with fall incidences continue to pose a significant burden to persons with Parkinson’s disease (PD) both in terms of human suffering and economic loss. Freezing of gait (FOG), which is one of the symptoms of PD, is a common cause of falls in this population. Although a significant amount of work has been performed to characterize/detect FOG using both qualitative and quantitative methods, there remains paucity of data regarding real-time detection of FOG, such as the requirements for minimum sensor nodes, sensor placement locations, and appropriate sampling period and update time. Here, the continuous wavelet transform (CWT) is employed to define an index for correctly identifying FOG. Since the CWT method uses both time and frequency components of a waveform in comparison to other methods utilizing only the frequency component, we hypothesized that using this method could lead to a significant improvement in the accuracy of FOG detection. We tested the proposed index on the data of 10 PD patients who experience FOG. Two hundred and thirty seven (237) FOG events were identified by the physiotherapists. The results show that the index could discriminate FOG in the anterior–posterior axis better than other two axes, and is robust to the update time variability. These results suggest that real time detection of FOG may be realized by using CWT of a single shank sensor with window size of 2 s and update time of 1 s (82.1% and 77.1% for the sensitivity and specificity, respectively). Although implicated, future studies should examine the utility of this method in real-time detection of FOG. PMID:27049389
NASA Astrophysics Data System (ADS)
Heo, YongHwa; Kim, Kwang-joon
2015-02-01
While the vibration power for a set of harmonic force and velocity signals is well defined and known, it is not as popular yet for a set of stationary random force and velocity processes, although it can be found in some literatures. In this paper, the definition of the vibration power for a set of non-stationary random force and velocity signals will be derived for the purpose of a time-frequency analysis based on the definitions of the vibration power for the harmonic and stationary random signals. The non-stationary vibration power, defined as the short-time average of the product of the force and velocity over a given frequency range of interest, can be calculated by three methods: the Wigner-Ville distribution, the short-time Fourier transform, and the harmonic wavelet transform. The latter method is selected in this paper because band-pass filtering can be done without phase distortions, and the frequency ranges can be chosen very flexibly for the time-frequency analysis. Three algorithms for the time-frequency analysis of the non-stationary vibration power using the harmonic wavelet transform are discussed. The first is an algorithm for computation according to the full definition, while the others are approximate. Noting that the force and velocity decomposed into frequency ranges of interest by the harmonic wavelet transform are constructed with coefficients and basis functions, for the second algorithm, it is suggested to prepare a table of time integrals of the product of the basis functions in advance, which are independent of the signals under analysis. How to prepare and utilize the integral table are presented. The third algorithm is based on an evolutionary spectrum. Applications of the algorithms to the time-frequency analysis of the vibration power transmitted from an excitation source to a receiver structure in a simple mechanical system consisting of a cantilever beam and a reaction wheel are presented for illustration.
Statistical Analysis of Large Scale Structure by the Discrete Wavelet Transform
NASA Astrophysics Data System (ADS)
Pando, Jesus
1997-10-01
The discrete wavelet transform (DWT) is developed as a general statistical tool for the study of large scale structures (LSS) in astrophysics. The DWT is used in all aspects of structure identification including cluster analysis, spectrum and two-point correlation studies, scale-scale correlation analysis and to measure deviations from Gaussian behavior. The techniques developed are demonstrated on 'academic' signals, on simulated models of the Lymanα (Lyα) forests, and on observational data of the Lyα forests. This technique can detect clustering in the Ly-α clouds where traditional techniques such as the two-point correlation function have failed. The position and strength of these clusters in both real and simulated data is determined and it is shown that clusters exist on scales as large as at least 20 h-1 Mpc at significance levels of 2-4 σ. Furthermore, it is found that the strength distribution of the clusters can be used to distinguish between real data and simulated samples even where other traditional methods have failed to detect differences. Second, a method for measuring the power spectrum of a density field using the DWT is developed. All common features determined by the usual Fourier power spectrum can be calculated by the DWT. These features, such as the index of a power law or typical scales, can be detected even when the samples are geometrically complex, the samples are incomplete, or the mean density on larger scales is not known (the infrared uncertainty). Using this method the spectra of Ly-α forests in both simulated and real samples is calculated. Third, a method for measuring hierarchical clustering is introduced. Because hierarchical evolution is characterized by a set of rules of how larger dark matter halos are formed by the merging of smaller halos, scale-scale correlations of the density field should be one of the most sensitive quantities in determining the merging history. We show that these correlations can be completely
NASA Astrophysics Data System (ADS)
Ge, Xinmin; Fan, Yiren; Li, Jiangtao; Wang, Yang; Deng, Shaogui
2015-02-01
NMR logging and core NMR signals acts as an effective way of pore structure evaluation and fluid discrimination, but it is greatly contaminated by noise for samples with low magnetic resonance intensity. Transversal relaxation time (T2) spectrum obtained by inversion of decay signals intrigued by Carr-Purcell-Meiboom-Gill (CPMG) sequence may deviate from the truth if the signal-to-noise ratio (SNR) is imperfect. A method of combing the improved wavelet thresholding with the EWMA is proposed for noise reduction of decay data. The wavelet basis function and decomposition level are optimized in consideration of information entropy and white noise estimation firstly. Then a hybrid threshold function is developed to avoid drawbacks of hard and soft threshold functions. To achieve the best thresholding values of different levels, a nonlinear objective function based on SNR and mean square error (MSE) is constructed, transforming the problem to a task of finding optimal solutions. Particle swarm optimization (PSO) is used to ensure the stability and global convergence. EWMA is carried out to eliminate unwanted peaks and sawtooths of the wavelet denoised signal. With validations of numerical simulations and experiments, it is demonstrated that the proposed approach can reduce the noise of T2 decay data perfectly.
Perpinan, O.; Lorenzo, E.
2011-01-15
The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent {alpha} is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter. (author)
NASA Astrophysics Data System (ADS)
Wang, Hongchao; Chen, Jin; Dong, Guangming
2014-10-01
When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing’ early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing’ early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained.
Hu, Ying; Mo, Wenqin; Dong, Kaifeng; Jin, Fang; Song, Junlei
2016-06-10
The maximum spectrum of the continuous wavelet transform (MSCWT) is proposed to demodulate the central wavelengths for the overlapped spectrum in a serial fiber Bragg grating (FBG) sensing system. We describe the operation principle of the MSCWT method. Moreover, the influence of the interval gap between two FBG wavelengths, 3 dB bandwidths, and optical powers of the reflected spectra are discussed. The simulation and experimental results indicate that the MSCWT can resolve an overlapped spectrum and decode the central wavelength with high accuracy. More importantly, the proposed peak detection method can enhance the sensing capacity of a wavelength division multiplexing FBG sensor network. PMID:27409024
Unger, Miriam; Pfeifer, Frank; Siesler, Heinz W
2016-07-01
The main objective of this communication is to compare the performance of a miniaturized handheld near-infrared (NIR) spectrometer with a benchtop Fourier transform near-infrared (FT-NIR) spectrometer. Generally, NIR spectroscopy is an extremely powerful analytical tool to study hydrogen-bonding changes of amide functionalities in solid and liquid materials and therefore variable temperature NIR measurements of polyamide II (PAII) have been selected as a case study. The information content of the measurement data has been further enhanced by exploiting the potential of two-dimensional correlation spectroscopy (2D-COS) and the perturbation correlation moving window two-dimensional (PCMW2D) evaluation technique. The data provide valuable insights not only into the changes of the hydrogen-bonding structure and the recrystallization of the hydrocarbon segments of the investigated PAII but also in their sequential order. Furthermore, it has been demonstrated that the 2D-COS and PCMW2D results derived from the spectra measured with the miniaturized NIR instrument are equivalent to the information extracted from the data obtained with the high-performance FT-NIR instrument.
Quantum tomography with wavelet transform in Banach space on homogeneous space
NASA Astrophysics Data System (ADS)
Mirzaee, M.; Rezaei, M.; Jafarizadeh, M. A.
2007-11-01
In this study the intimate connection is established between the Banach space wavelet reconstruction method on homogeneous spaces with both singular and nonsingular vacuum vectors, and some of the well known quantum tomographies, such as: Moyal-representation for a spin, discrete phase space tomography, tomography of a free particle, Homodyne tomography, phase space tomography and SU(1,1) tomography. And both the atomic decomposition and the Banach frame nature of these quantum tomographic examples are also revealed in details. Finally the connection between the wavelet formalism on Banach space and Q-function is discussed.
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation
Mu, Donghui; Chen, Dongju; Fan, Jinwei; Wang, Xiaofeng; Zhang, Feihu
2012-01-01
This paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by the PGI1240 profilometer is represented by a wavelet. Cross-correlation analysis performed to identify the error source of the carriage. The error model is developed based on experimental results on the low frequency components of the signals. With the use of wavelets, the identification precision of test signals is very high. PMID:23012558
Wavelet based free-form deformations for nonrigid registration
NASA Astrophysics Data System (ADS)
Sun, Wei; Niessen, Wiro J.; Klein, Stefan
2014-03-01
In nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have been proposed as a signal representation suitable for multi-scale problems. Wavelet analysis leads to a unique decomposition of a signal into its coarse- and fine-scale components. Potentially, this could therefore be useful for image registration. In this work, we investigate whether a wavelet-based FFD model has advantages for nonrigid image registration. We use a B-splines based wavelet, as defined by Cai and Wang.1 This wavelet is expressed as a linear combination of B-spline basis functions. Derived from the original B-spline function, this wavelet is smooth, differentiable, and compactly supported. The basis functions of this wavelet are orthogonal across scales in Sobolev space. This wavelet was previously used for registration in computer vision, in 2D optical flow problems,2 but it was not compared with the conventional B-spline FFD in medical image registration problems. An advantage of choosing this B-splines based wavelet model is that the space of allowable deformation is exactly equivalent to that of the traditional B-spline. The wavelet transformation is essentially a (linear) reparameterization of the B-spline transformation model. Experiments on 10 CT lung and 18 T1-weighted MRI brain datasets show that wavelet based registration leads to smoother deformation fields than traditional B-splines based registration, while achieving better accuracy.
NASA Astrophysics Data System (ADS)
Saadatinejad, M. R.; Hassani, H.
2013-04-01
The Persian Gulf and its surrounding area are some of the biggest basins and have a very important role in producing huge amounts of hydrocarbon, and this potential was evaluated in order to explore the target for geoscientists and petroleum engineers. Wavelet transform is a useful and applicable technique to reveal frequency contents of various signals in different branches of science and especially in petroleum studies. We applied two major capacities of continuous mode of wavelet transform in seismic investigations. These investigations were operated to detect reservoir geological structures and some anomalies related to hydrocarbon to develop and explore new petroleum reservoirs in at least 4 oilfields in the southwest of Iran. It had been observed that continuous wavelet transform results show some discontinuities in the location of faults and are able to display them more clearly than other seismic methods. Moreover, continuous wavelet transform, utilizing Morlet wavelet, displays low-frequency shadows on 4 different iso-frequency vertical sections to identify reservoirs containing gas. By comparing these different figures, the presence of low-frequency shadows under the reservoir could be seen and we can relate these variations from anomalies at different frequencies as an indicator of the presence of hydrocarbons in the target reservoir.
Connection Between Group Based Quantum Tomography and Wavelet Transform in Banach Spaces
NASA Astrophysics Data System (ADS)
Jafarizadeh, M. A.; Mirzaee, M.; Rezaee, M.
2005-04-01
The intimate connection between the Banach space wavelet reconstruction method for each unitary representation of a given group and some of well-known quantum tomographies, such as, tomography of rotation group, spinor tomography and tomography of unitary group, is established. Also both the atomic decomposition and Banach frame nature of these quantum tomographic examples is revealed in details.
Connection between Group Based Quantum Tomography and Wavelet Transform in Banach Spaces
NASA Astrophysics Data System (ADS)
Mirzaee, M.; Rezaei, M.; Jafarizadeh, M. A.
2007-10-01
The intimate connection between the Banach space wavelet reconstruction method for each unitary representation of a given group and some of well known quantum tomographies, such as: tomography of rotation group, Spinor tomography and tomography of Unitary group, is established. Also both the atomic decomposition and Banach frame nature of these quantum tomographic examples is revealed in details.
Reconstructing Past Admixture Processes from Local Genomic Ancestry Using Wavelet Transformation
Sanderson, Jean; Sudoyo, Herawati; Karafet, Tatiana M.; Hammer, Michael F.; Cox, Murray P.
2015-01-01
Admixture between long-separated populations is a defining feature of the genomes of many species. The mosaic block structure of admixed genomes can provide information about past contact events, including the time and extent of admixture. Here, we describe an improved wavelet-based technique that better characterizes ancestry block structure from observed genomic patterns. principal components analysis is first applied to genomic data to identify the primary population structure, followed by wavelet decomposition to develop a new characterization of local ancestry information along the chromosomes. For testing purposes, this method is applied to human genome-wide genotype data from Indonesia, as well as virtual genetic data generated using genome-scale sequential coalescent simulations under a wide range of admixture scenarios. Time of admixture is inferred using an approximate Bayesian computation framework, providing robust estimates of both admixture times and their associated levels of uncertainty. Crucially, we demonstrate that this revised wavelet approach, which we have released as the R package adwave, provides improved statistical power over existing wavelet-based techniques and can be used to address a broad range of admixture questions. PMID:25852078
Unaldi, Numan; Temel, Samil; Asari, Vijayan K.
2012-01-01
One of the most critical issues of Wireless Sensor Networks (WSNs) is the deployment of a limited number of sensors in order to achieve maximum coverage on a terrain. The optimal sensor deployment which enables one to minimize the consumed energy, communication time and manpower for the maintenance of the network has attracted interest with the increased number of studies conducted on the subject in the last decade. Most of the studies in the literature today are proposed for two dimensional (2D) surfaces; however, real world sensor deployments often arise on three dimensional (3D) environments. In this paper, a guided wavelet transform (WT) based deployment strategy (WTDS) for 3D terrains, in which the sensor movements are carried out within the mutation phase of the genetic algorithms (GAs) is proposed. The proposed algorithm aims to maximize the Quality of Coverage (QoC) of a WSN via deploying a limited number of sensors on a 3D surface by utilizing a probabilistic sensing model and the Bresenham's line of sight (LOS) algorithm. In addition, the method followed in this paper is novel to the literature and the performance of the proposed algorithm is compared with the Delaunay Triangulation (DT) method as well as a standard genetic algorithm based method and the results reveal that the proposed method is a more powerful and more successful method for sensor deployment on 3D terrains. PMID:22666078
NASA Astrophysics Data System (ADS)
Bai, Yongliang; Dong, Dongdong; Wu, Shiguo; Liu, Zhan; Zhang, Guangxu; Xu, Kaijun
2016-05-01
Gravity anomalies detected by different measurement platforms have different characteristics and advantages. There are different kinds of gravity data fusion methods for generating single gravity anomaly map with a rich and accurate spectral content. Former studies using wavelet based gravity fusion method which is a newly developed approach did not pay more attention to the fusion uncertainties. In this paper, we firstly introduce the wavelet based gravity fusion method, and then apply this method to one synthetic model and also to the northern margin of the South China Sea. Wavelet type and the decomposition level are two input parameters for this fusion method, and the uncertainty tests show that fusion results are more sensitive to wavelet type than the decomposition level. The optimal application result of the fusion methodology on the synthetic model is closer to the true anomaly field than either of the simulated shipborne anomaly and altimetry-based anomaly grid. The best fusion result on the northern margin of the South China Sea is based on the 'rbio1.3' wavelet and four-level decomposition. The fusion result contains more accurate short-wavelength anomalies than the altimetry-based gravity anomalies along ship tracks, and it also has more accurate long wavelength characteristics than the shipborne gravity anomalies between ship tracks. The real application case shows that the fusion result has better correspondences to the seafloor topography variations and sub-surface structures than each of the two input gravity anomaly maps (shipborne based gravity anomaly map and altimetry based gravity anomaly map). Therefore, it is possible to map and detect more precise seafloor topography and geologic structures by the new gravity anomaly map.
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
Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering.
Xia, Yong; Han, Junze; Wang, Kuanquan
2015-01-01
Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods. PMID:26405862
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.
Deng, Jun-Min; Yue, Hai-Zhen; Zhuo, Zhi-Zheng; Yan, Hua-Gang; Liu, Di; Li, Hai-Yun
2014-05-01
Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).
Görgel, Pelin; Sertbas, Ahmet; Ucan, Osman N
2013-07-01
The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.
An Introduction to Wavelet Theory and Analysis
Miner, N.E.
1998-10-01
This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides tiormation about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, define common wavelet terminology and describe wavelet amdysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary.
Xie, Hong-Bo; Zheng, Yong-Ping; Guo, Jing-Yi
2009-05-01
Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as a movement artifact are known to cause considerable interference to MMG, compromising the classification accuracy. To solve this noise problem, we proposed a new scheme to extract robust MMG features by the integration of the wavelet packet transform (WPT), singular value decomposition (SVD) and a feature selection technique based on distance evaluation criteria for the classification of hand motions. The WPT was first adopted to provide an effective time-frequency representation of non-stationary MMG signals. Then, the SVD and the distance evaluation technique were utilized to extract and select the optimal feature representing the hand motion patterns from the MMG time-frequency representation matrix. Experimental results of 12 subjects showed that four different motions of the forearm and hand could be reliably differentiated using the proposed method when two channels of MMG signals were used. Compared with three previously reported time-frequency decomposition methods, i.e. short-time Fourier transform, stationary wavelet transform and S-transform, the proposed classification system gave the highest average classification accuracy up to 89.7%. The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.
NASA Astrophysics Data System (ADS)
Huang, Shieh-Kung; Loh, Chin-Hsiung; Chen, Chin-Tsun
2016-04-01
Seismic records collected from earthquake with large magnitude and far distance may contain long period seismic waves which have small amplitude but with dominant period up to 10 sec. For a general situation, the long period seismic waves will not endanger the safety of the structural system or cause any uncomfortable for human activity. On the contrary, for those far distant earthquakes, this type of seismic waves may cause a glitch or, furthermore, breakdown to some important equipments/facilities (such as the high-precision facilities in high-tech Fab) and eventually damage the interests of company if the amplitude becomes significant. The previous study showed that the ground motion features such as time-variant dominant frequencies extracted using moving window singular spectrum analysis (MWSSA) and amplitude characteristics of long-period waves identified from slope change of ground motion Arias Intensity can efficiently indicate the damage severity to the high-precision facilities. However, embedding a large hankel matrix to extract long period seismic waves make the MWSSA become a time-consumed process. In this study, the seismic ground motion data collected from broadband seismometer network located in Taiwan were used (with epicenter distance over 1000 km). To monitor the significant long-period waves, the low frequency components of these seismic ground motion data are extracted using wavelet packet transform (WPT) to obtain wavelet coefficients and the wavelet entropy of coefficients are used to identify the amplitude characteristics of long-period waves. The proposed method is a timesaving process compared to MWSSA and can be easily implemented for real-time detection. Comparison and discussion on this method among these different seismic events and the damage severity to the high-precision facilities in high-tech Fab is made.
Workman, Michael J; Serov, Alexey; Halevi, Barr; Atanassov, Plamen; Artyushkova, Kateryna
2015-05-01
The discrete wavelet transform (DWT) has found significant utility in process monitoring, filtering, and feature isolation of SEM, AFM, and optical images. Current use of the DWT for surface analysis assumes initial knowledge of the sizes of the features of interest in order to effectively isolate and analyze surface components. Current methods do not adequately address complex, heterogeneous surfaces in which features across multiple size ranges are of interest. Further, in situations where structure-to-property relationships are desired, the identification of features relevant for the function of the material is necessary. In this work, the DWT is examined as a tool for quantitative, length-scale specific surface metrology without prior knowledge of relevant features or length-scales. A new method is explored for determination of the best wavelet basis to minimize variation in roughness and skewness measurements with respect to change in position and orientation of surface features. It is observed that the size of the wavelet does not directly correlate with the size of features on the surface, and a method to measure the true length-scale specific roughness of the surface is presented. This method is applied to SEM and AFM images of non-precious metal catalysts, yielding new length-scale specific structure-to-property relationships for chemical speciation and fuel cell performance. The relationship between SEM and AFM length-scale specific roughness is also explored. Evidence is presented that roughness distributions of SEM images, as measured by the DWT, is representative of the true surface roughness distribution obtained from AFM.
Metz, C.E.; Pan, X.
1995-12-01
Exact methods of inverting the two-dimensional (2-D) exponential Radon transform have been proposed by Bellini et al. and by Inouye et al., both of whom worked in the spatial-frequency domain to estimate the 2-D Fourier transform of the unattenuated sinogram; by Hawkins et al., who worked with circularly harmonic Bessel transforms; and by Tretiak and Metz, who followed filtering of appropriately-modified projections by exponentially-weighted backprojection. With perfect sampling, all four of these methods are exact in the absence of projection-data noise, but empirical studies have shown that they propagate noise differently, and no underlying theoretical relationship among the methods has been evident. In this paper, an analysis of the 2-D Fourier transform of the modified sinogram reveals that all previously-proposed linear methods can be interpreted as special cases of a broad class of methods, and that each method in the class can be implemented, in principle, by any one of four distinct techniques. Moreover, the analysis suggests a new member of the class that is predicted to have noise properties better than those of previously proposed members.
Casson, Alexander J
2015-01-01
Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans. PMID:26694414
Casson, Alexander J
2015-12-17
Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.
Casson, Alexander J.
2015-01-01
Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via gmC circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans. PMID:26694414
NASA Astrophysics Data System (ADS)
Gdeisat, Munther A.; Abid, Abdulbasit; Burton, David R.; Lalor, Michael J.; Lilley, Francis; Moore, Chris; Qudeisat, Mohammed
2009-12-01
This paper presents a thorough discussion on the application of the one-dimensional continuous wavelet transform (1D-CWT) in order to retrieve phase information in temporally and spatially tilted fringe patterns and highlights recent progress and challenges. The paper also suggests some possible future developments for this method. The advantages and drawbacks of the one-dimensional continuous wavelet transform technique are discussed here and in this context are compared to the widely used methods of Fourier fringe analysis, phase stepping and the windowed Fourier transform. A description is given of the manner in which the CWT phase gradient and phase estimation methods may be used to extract the phase of fringe patterns, and these two methods are compared and contrasted. Five different ridge extraction algorithms are explained and the performance of three of these is evaluated. To alleviate the distortions that may occur at the image borders and at regions close to holes in fringe patterns, two methods are described and evaluated for extending the image edges and for filling in holes within fringe patterns. A novel mother wavelet is presented which has been designed to improve the ability of the continuous wavelet transform to analyse fringe patterns that contain sudden phase variations. The sampling and structural conditions that are required to obtain 'correct' phase are also discussed.
Wavelet transform analysis of the small-scale X-ray structure of the cluster Abell 1367
NASA Technical Reports Server (NTRS)
Grebeney, S. A.; Forman, W.; Jones, C.; Murray, S.
1995-01-01
We have developed a new technique based on a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies. We apply this technique to the ROSAT position sensitive proportional counter (PSPC) and Einstein high-resolution imager (HRI) images of the central region of the cluster Abell 1367 to detect sources embedded within the diffuse intracluster medium. In addition to detecting sources and determining their fluxes and positions, we show that the wavelet analysis allows a characterization of the sources extents. In particular, the wavelet scale at which a given source achieves a maximum signal-to-noise ratio in the wavelet images provides an estimate of the angular extent of the source. To account for the widely varying point response of the ROSAT PSPC as a function of off-axis angle requires a quantitative measurement of the source size and a comparison to a calibration derived from the analysis of a Deep Survey image. Therefore, we assume that each source could be described as an isotropic two-dimensional Gaussian and used the wavelet amplitudes, at different scales, to determine the equivalent Gaussian Full Width Half-Maximum (FWHM) (and its uncertainty) appropriate for each source. In our analysis of the ROSAT PSPC image, we detect 31 X-ray sources above the diffuse cluster emission (within a radius of 24 min), 16 of which are apparently associated with cluster galaxies and two with serendipitous, background quasars. We find that the angular extents of 11 sources exceed the nominal width of the PSPC point-spread function. Four of these extended sources were previously detected by Bechtold et al. (1983) as 1 sec scale features using the Einstein HRI. The same wavelet analysis technique was applied to the Einstein HRI image. We detect 28 sources in the HRI image, of which nine are extended. Eight of the extended sources correspond to sources previously detected by Bechtold et al. Overall, using both the
NASA Astrophysics Data System (ADS)
Kim, Yooil
2013-06-01
Reliable strength assessment of the Liquefied Natural Gas (LNG) cargo containment system under the sloshing impact load is very difficult task due to the complexity of the physics involved in, both in terms of the hydrodynamics and structural mechanics. Out of all those complexities, the proper selection of the design sloshing load which is applied to the structural model of the LNG cargo containment system, is one of the most challenging one due to its inherent randomness as well as the statistical analysis which is tightly linked to the design sloshing load selection. In this study, the response based strength assessment procedure of LNG cargo containment system has been developed and proposed as an alternative design methodology. Sloshing pressure time history, measured from the model test, is decomposed into wavelet basis function targeting the minimization of the number of the basis function together with the maximization of the numerical efficiency. Then the response of the structure is obtained using the finite element method under each wavelet basis function of different scale. Finally, the response of the structure under entire sloshing impact time history is rapidly calculated by synthesizing the structural response under wavelet basis function. Through this analysis, more realistic response of the system under sloshing impact pressure can be obtained without missing the details of pressure time history such as rising pattern, oscillation due to air entrapment and decay pattern and so on. The strength assessment of the cargo containment system is then performed based on the statistical analysis of the stress peaks selected out of the obtained stress time history.
NASA Astrophysics Data System (ADS)
Son, N. T.; Chen, C. F.; Cru, C. R.
2012-07-01
Agriculture is one of the most important sectors in the economy of Southeast Asia countries, especially Thailand and Vietnam. These two countries have been the largest rice suppliers in the world and played a critical role in global food security. Yearly rice crop monitoring to provide policymakers with information on rice growing areas is thus important to timely devise plans to ensure food security. This study aimed to develop an approach for regional mapping of cropping patterns from time-series MODIS data. Data were processed through three steps: (1) noise filtering of time-series MODIS NDVI data with wavelet transform, (2) image classification of cropping patterns using artificial neural networks (ANNs), and (3) classification accuracy assessment using ground reference data. The results by a comparison between classification map and ground reference data indicated the overall accuracy of 80.3% and Kappa coefficient of 0.76.
Elzanfaly, Eman S; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A
2015-12-01
A comparative study was established between two signal processing techniques showing the theoretical algorithm for each method and making a comparison between them to indicate the advantages and limitations. The methods under study are Numerical Differentiation (ND) and Continuous Wavelet Transform (CWT). These methods were studied as spectrophotometric resolution tools for simultaneous analysis of binary and ternary mixtures. To present the comparison, the two methods were applied for the resolution of Bisoprolol (BIS) and Hydrochlorothiazide (HCT) in their binary mixture and for the analysis of Amlodipine (AML), Aliskiren (ALI) and Hydrochlorothiazide (HCT) as an example for ternary mixtures. By comparing the results in laboratory prepared mixtures, it was proven that CWT technique is more efficient and advantageous in analysis of mixtures with severe overlapped spectra than ND. The CWT was applied for quantitative determination of the drugs in their pharmaceutical formulations and validated according to the ICH guidelines where accuracy, precision, repeatability and robustness were found to be within the acceptable limit.
Lee, Sang-Hong; Lim, Joon S; Kim, Jae-Kwon; Yang, Junggi; Lee, Youngho
2014-08-01
This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.
Taghizadeh-Sarabi, Mitra; Daliri, Mohammad Reza; Niksirat, Kavous Salehzadeh
2015-01-01
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-trial 12 categories of recorded EEG signals. Ten subjects participated in this study. The task was to select target images among 12 basic object categories including animals, flowers, fruits, transportation devices, body organs, clothing, food, stationery, buildings, electronic devices, dolls and jewelry. In order to decode object categories, we have considered several units namely artifact removing, feature extraction, feature selection, and classification. Data were divided into training, validation, and test sets following the artifact removal process. Features were extracted using three different wavelets namely Daubechies4, Haar, and Symlet2. Features were selected among training data and were reduced afterward via scalar feature selection using three criteria including T test, entropy, and Bhattacharyya distance. Selected features were classified by the one-against-one support vector machine (SVM) multi-class classifier. The parameters of SVM were optimized based on training and validation sets. The classification performance (measured by means of accuracy) was obtained approximately 80 % for animal and stationery categories. Moreover, Symlet2 and T test were selected as better wavelet and selection criteria, respectively.
Taghizadeh-Sarabi, Mitra; Daliri, Mohammad Reza; Niksirat, Kavous Salehzadeh
2015-01-01
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-trial 12 categories of recorded EEG signals. Ten subjects participated in this study. The task was to select target images among 12 basic object categories including animals, flowers, fruits, transportation devices, body organs, clothing, food, stationery, buildings, electronic devices, dolls and jewelry. In order to decode object categories, we have considered several units namely artifact removing, feature extraction, feature selection, and classification. Data were divided into training, validation, and test sets following the artifact removal process. Features were extracted using three different wavelets namely Daubechies4, Haar, and Symlet2. Features were selected among training data and were reduced afterward via scalar feature selection using three criteria including T test, entropy, and Bhattacharyya distance. Selected features were classified by the one-against-one support vector machine (SVM) multi-class classifier. The parameters of SVM were optimized based on training and validation sets. The classification performance (measured by means of accuracy) was obtained approximately 80 % for animal and stationery categories. Moreover, Symlet2 and T test were selected as better wavelet and selection criteria, respectively. PMID:24838816
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%).
Hoang, Vu Dang; Hue, Nguyen Thu; Tho, Nguyen Huu; Nguyen, Hue Minh Thi
2015-03-15
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 (R(2)>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%).
Lei, Shengbin; Surin, Mathieu; Tahara, Kazukuni; Adisoejoso, Jinne; Lazzaroni, Roberto; Tobe, Yoshito; De Feyter, Steven
2008-08-01
Recognition and selection are of fundamental importance for the hierarchical assembly of supramolecular systems. Coronene induces the formation of a hydrogen-bonded isophthalic acid supramolecular macrocycle, and this well-defined heterocluster forces, in its turn, DBA1 to form a van der Waals stabilized honeycomb lattice, leading to a three-component 2D crystal containing nine molecules in the unit cell. The recognition and selection events enable efficient error correction and healing in redundant mixtures.
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…
NASA Astrophysics Data System (ADS)
Gutierrez, R. R.; Abad, J. D.; Parsons, D. R.
2011-12-01
The quantification of the variability of bedform geometry is necessary for scientific and practical purposes. For the former purpose, it is necessary for modeling bed roughness cross-strata sets, vertical sorting, sediment transport rates, transition between two-dimensional and three-dimensional dunes, velocity pulsations, flow over bedforms, interaction between flow over bedforms and groundwater, and transport of contaminants. For practical purposes the study of the variability of bedforms is important to predict floods and flow resistance, to predict uplifting of manmade structures underneath a river beds, to track future changes of bedform and biota following dam removal, to estimate the relationship between bedform characteristics and biota, in river restoration, among others. Currently there is not a standard nomenclature and procedure to separate bedform features such as sand waves, dunes and ripples which are commonly present in large rivers. Likewise, there is not a standard definition of the scope for the different scales of such bedform features. The present study proposes a standardization of the nomenclature and symbolic representation of bedform features and elaborates on the combined application of robust spline filter and continuous wavelet transforms to separate the morphodynamic features. A fully automated robust spline procedure for uniformly sampled datasets is used. The algorithm, based on a penalized least squares method, allows fast smoothing of uniformly sampled data elements by means of the discrete cosine transform. The wavelet transforms, which overcome some limitations of the Fourier transforms, are applied to identify the spectrum of bedform wavelengths. The proposed separation method is applied to a 370-m width and 1.028-km length swath bed morphology data of the Parana River, one of the world's largest rivers, located in Argentina. After the separation is carried out, the descriptors (e.g. wavelength, slope, and amplitude for both
Remacha, Clément; Coëtmellec, Sébastien; Brunel, Marc; Lebrun, Denis
2013-02-01
Wavelet analysis provides an efficient tool in numerous signal processing problems and has been implemented in optical processing techniques, such as in-line holography. This paper proposes an improvement of this tool for the case of an elliptical, astigmatic Gaussian (AEG) beam. We show that this mathematical operator allows reconstructing an image of a spherical particle without compression of the reconstructed image, which increases the accuracy of the 3D location of particles and of their size measurement. To validate the performance of this operator we have studied the diffraction pattern produced by a particle illuminated by an AEG beam. This study used mutual intensity propagation, and the particle is defined as a chirped Gaussian sum. The proposed technique was applied and the experimental results are presented.
Taruttis, Adrian; Rosenthal, Amir; Kacprowicz, Marcin; Burton, Neal C; Ntziachristos, Vasilis
2014-05-01
Multispectral optoacoustic tomography (MSOT) utilizes broadband ultrasound detection for imaging biologically-relevant optical absorption features at a range of scales. Due to the multiscale and multispectral features of the technology, MSOT comes with distinct requirements in implementation and data analysis. In this work, we investigate the interplay between scale, which depends on ultrasonic detection frequency, and optical multispectral spectral analysis, two dimensions that are unique to MSOT and represent a previously unexplored challenge. We show that ultrasound frequency-dependent artifacts suppress multispectral features and complicate spectral analysis. In response, we employ a wavelet decomposition to perform spectral unmixing on a per-scale basis (or per ultrasound frequency band) and showcase imaging of fine-scale features otherwise hidden by low frequency components. We explain the proposed algorithm by means of simple simulations and demonstrate improved performance in imaging data of blood vessels in human subjects.
Remacha, Clément; Coëtmellec, Sébastien; Brunel, Marc; Lebrun, Denis
2013-02-01
Wavelet analysis provides an efficient tool in numerous signal processing problems and has been implemented in optical processing techniques, such as in-line holography. This paper proposes an improvement of this tool for the case of an elliptical, astigmatic Gaussian (AEG) beam. We show that this mathematical operator allows reconstructing an image of a spherical particle without compression of the reconstructed image, which increases the accuracy of the 3D location of particles and of their size measurement. To validate the performance of this operator we have studied the diffraction pattern produced by a particle illuminated by an AEG beam. This study used mutual intensity propagation, and the particle is defined as a chirped Gaussian sum. The proposed technique was applied and the experimental results are presented. PMID:23385926
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.
Higher-order wavelet reconstruction/differentiation filters and Gibbs phenomena
NASA Astrophysics Data System (ADS)
Lombardini, Richard; Acevedo, Ramiro; Kuczala, Alexander; Keys, Kerry P.; Goodrich, Carl P.; Johnson, Bruce R.
2016-01-01
An orthogonal wavelet basis is characterized by its approximation order, which relates to the ability of the basis to represent general smooth functions on a given scale. It is known, though perhaps not widely known, that there are ways of exceeding the approximation order, i.e., achieving higher-order error in the discretized wavelet transform and its inverse. The focus here is on the development of a practical formulation to accomplish this first for 1D smooth functions, then for 1D functions with discontinuities and then for multidimensional (here 2D) functions with discontinuities. It is shown how to transcend both the wavelet approximation order and the 2D Gibbs phenomenon in representing electromagnetic fields at discontinuous dielectric interfaces that do not simply follow the wavelet-basis grid.
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
Chen, Hong-Yan; Zhao, Geng-Xing; Li, Xi-Can; Wang, Xiang-Feng; Li, Yu-Ling
2013-11-01
Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content.
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.
Segmentation of blurred objects using wavelet transform: application to x-ray images
NASA Astrophysics Data System (ADS)
Barat, Cecile S.; Ducottet, Christophe; Bilgot, Anne; Desbat, Laurent
2004-02-01
First, we present a wavelet-based algorithm for edge detection and characterization, which is an adaptation of Mallat and Hwang"s method. This algorithm relies on a modelization of contours as smoothed singularities of three particular types (transitions, peaks and lines). On the one hand, it allows to detect and locate edges at an adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure its amplitude and smoothing size. The latter parameters represent respectively the contrast and the smoothness level of the edge point. Second, we explain that this method has been integrated in a 3D bone surface reconstruction algorithm designed for computer-assisted and minimal invasive orthopaedic surgery. In order to decrease the dose to the patient and to obtain rapidly a 3D image, we propose to identify a bone shape from few X-ray projections by using statistical shape models registered to segmented X-ray projections. We apply this approach to pedicle screw insertion (scoliosis, fractures...) where ten to forty percent of the screws are known to be misplaced. In this context, the proposed edge detection algorithm allows to overcome the major problem of vertebrae segmentation in the X-ray images.
NASA Astrophysics Data System (ADS)
Hassani, H.; Saadatinejad, M. R.
2012-04-01
Spectral decomposition provides better methods for quantifying and visualizing subtle seismic features and by decomposing the seismic signal into discrete frequency components, allows the geoscientist to analyze and map features. Through these methods, continuous wavelet transform (CWT) is an effective and widely-applied. It provides a different approach to time-frequency analysis and produces a time-scale map. The application of CWT is extensive and in this paper, we applied two major capacities of CWT in seismic investigations. It operated to detect reservoir structural characteristics and low-frequency shadows below gas reservoirs to develop a producing reservoir and discover a new petroleum reservoir in 2 oilfields in southwestern of Iran successfully. At the first and significant application in reservoir structure study, CWT enabled to providing clear images from kind of structural systems especially to identify hidden structural features such as extensional ruptures and faults for better drilling, injection and recovery operations and be able to increase production of oilfield. According to properties of tectonic events as fault and their effect (velocity diffraction) on seismic signals, it had been observed that CWT results show some discontinuities in location of ruptures and be able to display them more obvious than other spectral results, especially on horizon slices. Then, by picking and interpretation those, we obtain map, kind, strike and deep direction of faults easily. In petroleum exploration case, low-frequency shadows in CWT results appear due to energy attenuation of seismic signal in high frequencies by the presence of gas; this means there are no high frequencies under the gas reservoir. This phenomenon accounts as an indicator and attribute to explore reservoirs containing gas. As the frequency increases, these shadows decrease and finally disappear. The ranges of these shadows are usually between 8 to 20 Hz in gaz and 28 to 35 Hz in oil
NASA Astrophysics Data System (ADS)
Chang, Phang; Isah, Abdulnasir
2016-02-01
In this paper we propose the wavelet operational method based on shifted Legendre polynomial to obtain the numerical solutions of nonlinear fractional-order chaotic system known by fractional-order Brusselator system. The operational matrices of fractional derivative and collocation method turn the nonlinear fractional-order Brusselator system to a system of algebraic equations. Two illustrative examples are given in order to demonstrate the accuracy and simplicity of the proposed techniques.
Fooprateepsiri, Rerkchai; Kurutach, Werasak
2014-03-01
Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression. PMID:24529782
Anion-induced structural transformation of a sulfate-incorporated 2D Cd(II)-organic framework
NASA Astrophysics Data System (ADS)
Lee, Li-Wei; Luo, Tzuoo-Tsair; Wang, Chih-Min; Lee, Gene-Hsiang; Peng, Shie-Ming; Liu, Yen-Hsiang; Lee, Sheng-Long; Lu, Kuang-Lieh
2016-07-01
A Cd(II)-organic framework {[Cd2(tpim)4(SO4)(H2O)2]·(SO4)·21H2O}n (1) was synthesized by reacting CdSO4·8/3H2O and 2,4,5-tri(4-pyridyl)imidazole (tpim) under hydrothermal conditions. A structural analysis showed that compound 1 adopts a layered structure in which the [Cd(tpim)2]n chains are linked by sulfate anions. These 2D layers are further packed into a 3D supramolecular framework via π-π interactions. The structure contains two types of SO42- anions, i.e., bridging SO42- and free SO42- anions, the latter of which are included in the large channels of the framework. Compound 1 exhibits interesting anion exchange behavior. In the presence of SCN- anions, both the bridging and free SO42- anions in 1 were completely exchanged by SCN- ligands to form a 1D species [Cd(tpim)2(SCN)2] (1A), in which the SCN- moieties function as a monodentate ligand. On the other hand, when compound 1 was ion exchanged with N3- anions in aqueous solution, the bridging SO42- moieties remained intact, and only the free guest SO42- were replaced by N3- anions. The gas adsorption behavior of the activated compound 1 was also investigated.
Ultra-Small, High-Frequency, and Substrate-Immune Microtube Inductors Transformed from 2D to 3D
NASA Astrophysics Data System (ADS)
Yu, Xin; Huang, Wen; Li, Moyang; Comberiate, Thomas M.; Gong, Songbin; Schutt-Aine, Jose E.; Li, Xiuling
2015-04-01
Monolithic on-chip inductors are key passive devices in radio frequency integrated circuits (RFICs). Currently, 70-80% of the on-wafer area of most RFIC chips is occupied by the sprawling planar spiral inductors, and its operation frequency is limited to a few GHz. With continuous scaling of the transistor technology, miniaturization and high frequency operation of inductors have become the bottleneck to meet future demands of wireless communication systems. Here we report on-chip self-rolled-up 3D microtube inductors with extremely small footprint, unprecedented high frequency performance and weak dependence on substrate conductivity. The serpentine metal strips are deposited on an oppositely strained silicon nitrides (SiNx) bilayer. After releasing from the sacrificial layer underneath, the metal/SiNx layer is scrolled into a 3D hollow tubular structure by the strain induced unidirectional self-rolled-up technology. Compared to the planar spiral inductors with similar inductances and quality (Q) factors, the footprint of tube inductors is reduced by as much as two orders of magnitude, and the frequency at peak Q factor improves more than 5 times on doped substrates. The self-rolled-up 3D nanotechnology platform employed here, that ``processes in 2D but functions in 3D'', is positioned to serve as a global solution for extreme RFIC miniaturization with improved performance.
Fooprateepsiri, Rerkchai; Kurutach, Werasak
2014-03-01
Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression.
2012-01-01
Background Quantification of leaf movement is an important tool for characterising the effects of environmental signals and the circadian clock on plant development. Analysis of leaf movement is currently restricted by the attachment of sensors to the plant or dependent upon visible light for time-lapse photography. The study of leaf growth movement rhythms in mature plants under biological relevant conditions, e.g. diurnal light and dark conditions, is therefore problematic. Results Here we present OSCILLATOR, an affordable system for the analysis of rhythmic leaf growth movement in mature plants. The system contains three modules: (1) Infrared time-lapse imaging of growing mature plants (2) measurement of projected distances between leaf tip and plant apex (leaf tip tracking growth-curves) and (3) extraction of phase, period and amplitude of leaf growth oscillations using wavelet analysis. A proof-of-principle is provided by characterising parameters of rhythmic leaf growth movement of different Arabidopsis thaliana accessions as well as of Petunia hybrida and Solanum lycopersicum plants under diurnal conditions. The amplitude of leaf oscillations correlated to published data on leaf angles, while amplitude and leaf length did not correlate, suggesting a distinct leaf growth profile for each accession. Arabidopsis mutant accession Landsberg erecta displayed a late phase (timing of peak oscillation) compared to other accessions and this trait appears unrelated to the ERECTA locus. Conclusions OSCILLATOR is a low cost and easy to implement system that can accurately and reproducibly quantify rhythmic growth of mature plants for different species under diurnal light/dark cycling. PMID:22867627
Lenis, Gustavo; Pilia, Nicolas; Oesterlein, Tobias; Luik, Armin; Schmitt, Claus; Dössel, Olaf
2016-02-01
Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32±12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios. PMID:26136298
Lenis, Gustavo; Pilia, Nicolas; Oesterlein, Tobias; Luik, Armin; Schmitt, Claus; Dössel, Olaf
2016-02-01
Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32±12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.
Pramanick, Abhijit; Stoica, Alexandru D.; An, Ke
2016-09-02
In-situ measurement of fine-structure of neutron Bragg diffraction peaks from a relaxor single-crystal using a time-of-flight instrument reveals highly heterogeneous mesoscale domain transformation behavior under applied electric fields. We observed that only 25% of domains undergo reorienta- tion or phase transition contributing to large average strains, while at least 40% remain invariant and exhibit microstrains. Such insights could be central for designing new relaxor materials with better performance and longevity. The current experimental technique can also be applied to resolve com- plex mesoscale phenomena in other functional materials.
High-order wavelet reconstruction/differentiation filters and Gibbs phenomena
NASA Astrophysics Data System (ADS)
Lombardini, Richard; Acevedo, Ramiro; Kuczala, Alexander; Keys, Kerry; Goodrich, Carl; Johnson, Bruce
2016-03-01
We have developed an efficient method to accurately represent 1D or 2D, smooth or discontinuous, solutions to partial differential equations (PDE's), such as Schrodinger or Maxwell's equations, in an orthogonal Daubechies wavelet basis. This is a crucial step in the future development of a wavelet method that solves these PDE's. There are two main developments from this research. First, a reconstruction transform for smooth functions, discovered in previous works [Keinert and Kwon (1997) and Neelov and Goedecker (2006)], is generalized in order to develop a systematic way of tuning its error. This transform converts the wavelet basis representation back to the actual point values of the function. Since this reconstruction can far exceed the wavelet approximation order, it is shown that shorter wavelets can be used while maintaining a high-order accuracy resulting in an increase of computational efficiency. Second, a new ``truncated'' reconstruction transform is developed, using pieces of wavelets, or ``tail functions'', which can be applied to discontinuous functions. Not only does it avoid the wavelet Gibbs phenomenon, but also maintains a tunable accuracy similar to the smooth function case.
Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain
2016-10-01
This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions. PMID:26340785
Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain
2016-10-01
This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.
NASA Astrophysics Data System (ADS)
Qiu, Bingwen; Feng, Min; Tang, Zhenghong
2016-05-01
This study proposed a simple Smoother without any local adjustments based on Continuous Wavelet Transform (SCWT). And then it evaluated its performance together with other commonly applied techniques in phenological estimation. These noise reduction methods included Savitzky-Golay filter (SG), Double Logistic function (DL), Asymmetric Gaussian function (AG), Whittaker Smoother (WS) and Harmonic Analysis of Time-Series (HANTS). They were evaluated based on fidelity and smoothness, and their efficiencies in deriving phenological parameters through the inflexion point-based method with the 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) in 2013 in China. The following conclusions were drawn: (1) The SG method exhibited strong fidelity, but weak smoothness and spatial continuity. (2) The HANTS method had very robust smoothness but weak fidelity. (3) The AG and DL methods performed weakly for vegetation with more than one growth cycle (i.e., multiple crops). (4) The WS and SCWT smoothers outperformed others with combined considerations of fidelity and smoothness, and consistent phenological patterns (correlation coefficients greater than 0.8 except evergreen broadleaf forests (0.68)). (5) Compared with WS methods, the SCWT smoother was capable in preservation of real local minima and maxima with fewer inflexions. (6) Large discrepancy was examined from the estimated phenological dates with SG and HANTS methods, particularly in evergreen forests and multiple cropping regions (the absolute mean deviation rates were 6.2-17.5 days and correlation coefficients less than 0.34 for estimated start dates).
NASA Astrophysics Data System (ADS)
Cai, Gaigai; Chen, Xuefeng; He, Zhengjia
2013-12-01
Localized faults in gearboxes tend to result in periodic shocks and thus arouse periodic responses in vibration signals. Feature extraction has always been a key problem for localized fault diagnosis. This paper proposes a new fault feature extraction technique for gearboxes by using sparsity-enabled signal decomposition method. The sparsity-enabled signal decomposition method separates signals based on the oscillatory behavior of the signal rather than the frequency or scale. Thus, the fault feature can be nonlinearly extracted from vibration signals. During the implementation of the proposed method, tunable Q-factor wavelet transform, for which the Q-factor can be easily specified, is adopted to represent vibration signals in a sparse way, and then morphological component analysis (MCA) is employed to estimate and separate the distinct components. The corresponding optimization problem of MCA is solved by the split augmented Lagrangian shrinkage algorithm (SALSA). With the proposed method, vibration signals of the faulty gearbox can be nonlinearly decomposed into high-oscillatory component and low-oscillatory component which is the fault feature of gearboxes. To evaluate the performance of the proposed method, this paper investigates the effect of two parameters pertinent to MCA and SALSA: the Lagrange multiplier and the penalty parameter. The effectiveness of the proposed method is verified by both the simulated and practical gearbox vibration signals. Results show the proposed method outperforms empirical mode decomposition and spectral kurtosis in extracting fault features of gearboxes.
NASA Astrophysics Data System (ADS)
Esposito, Angelo; Russo, Luigi; Kändler, Christoph; Pianese, Cesare; Ludwig, Bastian; Steiner, Nadia Yousfi
2016-06-01
The on-line diagnostics of Solid Oxide Fuel Cells (SOFCs) is a critical tool to achieve optimal performance and extend the lifetime. The Continuous Wavelet Transform (CWT) methodology was applied to the SOFC voltage signal to detect signatures that reveal the presence of a fault in the cell/stack. The selected fault was anode re-oxidation caused by high Fuel Utilization (FU) (higher then nominal). To experimentally emulate the high FU faults, a standard test procedure was developed, which was used to characterize a μ-CHP system at high FU operation. To complete the analysis, data collected on Single Cells were exploited too. The CWT was applied to the voltage signal for each FU level to verify the qualitative difference (signature) between the signals at different FU's within the same tests as well as the correspondence between the same conditions over different tests. A statistical study was performed to quantify the observed differences and to determine the correspondence between CWT coefficients and operating conditions. The approach proves to be suitable to diagnose high FU in SOFC, showing a successful detection rate above 76%. The results show the good potential of using the CWT methodology as diagnostic tools for SOFCs from cell to stack level.
NASA Astrophysics Data System (ADS)
Wang, Yi; Xu, Guanghua; Liang, Lin; Jiang, Kuosheng
2015-03-01
The kurtogram-based methods have been proved powerful and practical to detect and characterize transient components in a signal. The basic idea of the kurtogram-based methods is to use the kurtosis as a measure to discover the presence of transient impulse components and to indicate the frequency band where these occur. However, the performance of the kurtogram-based methods is poor due to the low signal-to-noise ratio. As the weak transient signal with a wide spread frequency band can be easily masked by noise. Besides, selecting signal just in one frequency band will leave out some transient features. Aiming at these shortcomings, different frequency bands signal fusion is adopted in this paper. Considering that manifold learning aims at discovering the nonlinear intrinsic structure which embedded in high dimensional data, this paper proposes a waveform feature manifold (WFM) method to extract the weak signature from waveform feature space which obtained by binary wavelet packet transform. Minimum permutation entropy is used to select the optimal parameter in a manifold learning algorithm. A simulated bearing fault signal and two real bearing fault signals are used to validate the improved performance of the proposed method through the comparison with the kurtogram-based methods. The results show that the proposed method outperforms the kurtogram-based methods and is effective in weak signature extraction.
Duarte-Galvan, Carlos; Romero-Troncoso, Rene de J; Torres-Pacheco, Irineo; Guevara-Gonzalez, Ramon G; Fernandez-Jaramillo, Arturo A; Contreras-Medina, Luis M; Carrillo-Serrano, Roberto V; Millan-Almaraz, Jesus R
2014-01-01
Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought stress improves specific characteristics. In such cases, a device capable of detecting and quantifying the impact of drought stress in plants is desirable. This article focuses on testing if the monitoring of physiological process through a gas exchange methodology provides enough information to detect drought stress conditions in plants. The experiment consists of using a set of smart sensors based on Field Programmable Gate Arrays (FPGAs) to monitor a group of plants under controlled drought conditions. The main objective was to use different digital signal processing techniques such as the Discrete Wavelet Transform (DWT) to explore the response of plant physiological processes to drought. Also, an index-based methodology was utilized to compensate the spatial variation inside the greenhouse. As a result, differences between treatments were determined to be independent of climate variations inside the greenhouse. Finally, after using the DWT as digital filter, results demonstrated that the proposed system is capable to reject high frequency noise and to detect drought conditions.
Duarte-Galvan, Carlos; de J. Romero-Troncoso, Rene; Torres-Pacheco, Irineo; Guevara-Gonzalez, Ramon G.; Fernandez-Jaramillo, Arturo A.; Contreras-Medina, Luis M.; Carrillo-Serrano, Roberto V.; Millan-Almaraz, Jesus R.
2014-01-01
Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought stress improves specific characteristics. In such cases, a device capable of detecting and quantifying the impact of drought stress in plants is desirable. This article focuses on testing if the monitoring of physiological process through a gas exchange methodology provides enough information to detect drought stress conditions in plants. The experiment consists of using a set of smart sensors based on Field Programmable Gate Arrays (FPGAs) to monitor a group of plants under controlled drought conditions. The main objective was to use different digital signal processing techniques such as the Discrete Wavelet Transform (DWT) to explore the response of plant physiological processes to drought. Also, an index-based methodology was utilized to compensate the spatial variation inside the greenhouse. As a result, differences between treatments were determined to be independent of climate variations inside the greenhouse. Finally, after using the DWT as digital filter, results demonstrated that the proposed system is capable to reject high frequency noise and to detect drought conditions. PMID:25302811
NASA Astrophysics Data System (ADS)
Salem, Hesham; Lotfy, Hayam M.; Hassan, Nagiba Y.; El-Zeiny, Mohamed B.; Saleh, Sarah S.
2015-01-01
This work represents a comparative study of different aspects of manipulating ratio spectra, which are: double divisor ratio spectra derivative (DR-DD), area under curve of derivative ratio (DR-AUC) and its novel approach, namely area under the curve correction method (AUCCM) applied for overlapped spectra; successive derivative of ratio spectra (SDR) and continuous wavelet transform (CWT) methods. The proposed methods represent different aspects of manipulating ratio spectra of the ternary mixture of Ofloxacin (OFX), Prednisolone acetate (PA) and Tetryzoline HCl (TZH) combined in eye drops in the presence of benzalkonium chloride as a preservative. The proposed methods were checked using laboratory-prepared mixtures and were successfully applied for the analysis of pharmaceutical formulation containing the cited drugs. The proposed methods were validated according to the ICH guidelines. A comparative study was conducted between those methods regarding simplicity, limitation and sensitivity. The obtained results were statistically compared with those obtained from the reported HPLC method, showing no significant difference with respect to accuracy and precision.
Peker, Musa
2016-06-01
Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method, which has been developed by sleep specialists, is a time-consuming and difficult process. Generally, electroencephalogram (EEG) signals are used in sleep scoring. In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. In this context, complex-valued methods were utilized in the feature selection and classification stages. In the feature selection stage, features of EEG data were extracted with the help of a dual tree complex wavelet transform (DTCWT). In the next phase, five statistical features were obtained. These features are classified using complex-valued neural network (CVANN) algorithm. The Taguchi method was used in order to determine the effective parameter values in this CVANN. The aim was to develop a stable model involving parameter optimization. Different statistical parameters were utilized in the evaluation phase. Also, results were obtained in terms of two different sleep standards. In the study in which a 2nd level DTCWT and CVANN hybrid model was used, 93.84% accuracy rate was obtained according to the Rechtschaffen & Kales (R&K) standard, while a 95.42% accuracy rate was obtained according to the American Academy of Sleep Medicine (AASM) standard. Complex-valued classifiers were found to be promising in terms of the automatic sleep scoring and EEG data. PMID:26787511
Yassin, Ali A.
2014-01-01
Now, the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image. Furthermore, fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually. In the cloud environment, an adversary has the ability to intercept information and must be secured from eavesdroppers. Unluckily, encryption and decryption functions are slow and they are often hard. Fingerprint techniques required extra hardware and software; it is masqueraded by artificial gummy fingers (spoof attacks). Additionally, when a large number of users are being verified at the same time, the mechanism will become slow. In this paper, we employed each of the partial encryptions of user's fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification. Moreover, our proposed scheme can overcome those problems; it does not require cost, reduces the computational supplies for huge volumes of fingerprint images, and resists well-known attacks. In addition, experimental results illustrate that our proposed scheme has a good performance of user's fingerprint verification. PMID:27355051
Peker, Musa
2016-06-01
Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method, which has been developed by sleep specialists, is a time-consuming and difficult process. Generally, electroencephalogram (EEG) signals are used in sleep scoring. In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. In this context, complex-valued methods were utilized in the feature selection and classification stages. In the feature selection stage, features of EEG data were extracted with the help of a dual tree complex wavelet transform (DTCWT). In the next phase, five statistical features were obtained. These features are classified using complex-valued neural network (CVANN) algorithm. The Taguchi method was used in order to determine the effective parameter values in this CVANN. The aim was to develop a stable model involving parameter optimization. Different statistical parameters were utilized in the evaluation phase. Also, results were obtained in terms of two different sleep standards. In the study in which a 2nd level DTCWT and CVANN hybrid model was used, 93.84% accuracy rate was obtained according to the Rechtschaffen & Kales (R&K) standard, while a 95.42% accuracy rate was obtained according to the American Academy of Sleep Medicine (AASM) standard. Complex-valued classifiers were found to be promising in terms of the automatic sleep scoring and EEG data.
Compression of ECG signals using variable-length classifıed vector sets and wavelet transforms
NASA Astrophysics Data System (ADS)
Gurkan, Hakan
2012-12-01
In this article, an improved and more efficient algorithm for the compression of the electrocardiogram (ECG) signals is presented, which combines the processes of modeling ECG signal by variable-length classified signature and envelope vector sets (VL-CSEVS), and residual error coding via wavelet transform. In particular, we form the VL-CSEVS derived from the ECG signals, which exploits the relationship between energy variation and clinical information. The VL-CSEVS are unique patterns generated from many of thousands of ECG segments of two different lengths obtained by the energy based segmentation method, then they are presented to both the transmitter and the receiver used in our proposed compression system. The proposed algorithm is tested on the MIT-BIH Arrhythmia Database and MIT-BIH Compression Test Database and its performance is evaluated by using some evaluation metrics such as the percentage root-mean-square difference (PRD), modified PRD (MPRD), maximum error, and clinical evaluation. Our experimental results imply that our proposed algorithm achieves high compression ratios with low level reconstruction error while preserving the diagnostic information in the reconstructed ECG signal, which has been supported by the clinical tests that we have carried out.
The relevance of the cross-wavelet transform in the analysis of human interaction – a tutorial
Issartel, Johann; Bardainne, Thomas; Gaillot, Philippe; Marin, Ludovic
2015-01-01
This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiology – neuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it. Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes. PMID:25620949
Salem, Hesham; Lotfy, Hayam M; Hassan, Nagiba Y; El-Zeiny, Mohamed B; Saleh, Sarah S
2015-01-25
This work represents a comparative study of different aspects of manipulating ratio spectra, which are: double divisor ratio spectra derivative (DR-DD), area under curve of derivative ratio (DR-AUC) and its novel approach, namely area under the curve correction method (AUCCM) applied for overlapped spectra; successive derivative of ratio spectra (SDR) and continuous wavelet transform (CWT) methods. The proposed methods represent different aspects of manipulating ratio spectra of the ternary mixture of Ofloxacin (OFX), Prednisolone acetate (PA) and Tetryzoline HCl (TZH) combined in eye drops in the presence of benzalkonium chloride as a preservative. The proposed methods were checked using laboratory-prepared mixtures and were successfully applied for the analysis of pharmaceutical formulation containing the cited drugs. The proposed methods were validated according to the ICH guidelines. A comparative study was conducted between those methods regarding simplicity, limitation and sensitivity. The obtained results were statistically compared with those obtained from the reported HPLC method, showing no significant difference with respect to accuracy and precision.
NASA Astrophysics Data System (ADS)
Amezquita-Sanchez, Juan P.; Adeli, Hojjat
2015-06-01
A new methodology is presented for (a) detecting, (b) locating, and (c) quantifying the damage severity in a smart highrise building structure. The methodology consists of three steps: In step 1, the synchrosqueezed wavelet transform is used to eliminate the noise in the signals. In step 2, a nonlinear dynamics measure based on the chaos theory, fractality dimension (FD), is employed to detect features to be used for damage detection. In step 3, a new structural damage index, based on the estimated FD values, is proposed as a measure of the condition of the structure. Further, the damage location is obtained using the changes of the estimated FD values. Three different FD algorithms for computing the fractality of time series signals are investigated. They are Katz’s FD, Higuchi’s FD, and box dimension. The usefulness and effectiveness of the proposed methodology are validated using the sensed data obtained experimentally for the 1:20 scaled model of a 38-storey concrete building structure.
Bhattacharjee, Debotosh; Seal, Ayan; Ganguly, Suranjan; Nasipuri, Mita; Basu, Dipak Kumar
2012-01-01
Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database. PMID:22924035
Tsanas, Athanasios; Clifford, Gari D
2015-01-01
Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG) signal(s) by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g., Fourier transform-based approaches) which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g., more than one EEG channels, or prior hypnogram assessment). This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means toward probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT) with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11-16 Hz) is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts' sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%), outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts' assessment of detected spindles. PMID:25926784
Tsanas, Athanasios; Clifford, Gari D.
2015-01-01
Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG) signal(s) by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g., Fourier transform-based approaches) which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g., more than one EEG channels, or prior hypnogram assessment). This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means toward probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT) with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11–16 Hz) is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts' sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%), outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts' assessment of detected spindles. PMID:25926784
NASA Astrophysics Data System (ADS)
Roland, E. C.; McGuire, J. J.; Collins, J. A.; Lizarralde, D.
2009-12-01
We perform two 2-D tomographic inversions using data collected as a part of the Quebrada-Discovery-Gofar (QDG) Transform Fault Active/Passive Experiment. The QDG transform faults are located in the southern Pacific Ocean and offset the East Pacific Rise (EPR) at approximately 4° south. In the spring of 2008, two ~100 km refraction profiles were collected, each using 8 short period Ocean Bottom Seismometers (OBS) from OBSIP and over 900 shots from the RV Marcus Langseth, across the easternmost segments of the Quebrada and Gofar transform faults. The two refraction profiles are modeled using a 2-D tomographic code that allows joint inversion of the Pg, PmP, and Pn arrivals (Korenaga et al., 2000). Variations in crustal velocity and thickness, as well as the width and depth extent of a significant low velocity zone within and below the transform valley provide some insight into the material properties of each of the fault-zones. Reduced seismic velocities that are 0.5 to over 1.0 km/s slower than velocities associated with the oceanic crust outside the fault zone may indicate the highly fractured fault zone lithology. The low velocity zone associated with the Quebrada fault also extends to the south of the active fault zone, beneath a fossil fault trace. Because Gofar is offset by an intratransform spreading center, we are able to compare ‘normal’ oceanic crust produced at the EPR to the south of the fault with crust associated with the ~15 km intratransform spreading center to the north. These two high slip rate (14 cm/yr) faults look similar morphologically and demonstrate comparable microseismicity characteristics, however their abilities to generate large earthquakes differ significantly. Gofar generates large earthquakes (Mw ~6) regularly every few years, but in the past 24 years only one large (Mw 5.6) event has been reliably located on Quebrada. The contrasting seismic behavior of these faults represents the range of behavior observed in the global
Spectral Laplace-Beltrami wavelets with applications in medical images.
Tan, Mingzhen; Qiu, Anqi
2015-05-01
The spectral graph wavelet transform (SGWT) has recently been developed to compute wavelet transforms of functions defined on non-Euclidean spaces such as graphs. By capitalizing on the established framework of the SGWT, we adopt a fast and efficient computation of a discretized Laplace-Beltrami (LB) operator that allows its extension from arbitrary graphs to differentiable and closed 2-D manifolds (smooth surfaces embedded in the 3-D Euclidean space). This particular class of manifolds are widely used in bioimaging to characterize the morphology of cells, tissues, and organs. They are often discretized into triangular meshes, providing additional geometric information apart from simple nodes and weighted connections in graphs. In comparison with the SGWT, the wavelet bases constructed with the LB operator are spatially localized with a more uniform "spread" with respect to underlying curvature of the surface. In our experiments, we first use synthetic data to show that traditional applications of wavelets in smoothing and edge detectio can be done using the wavelet bases constructed with the LB operator. Second, we show that multi-resolutional capabilities of the proposed framework are applicable in the classification of Alzheimer's patients with normal subjects using hippocampal shapes. Wavelet transforms of the hippocampal shape deformations at finer resolutions registered higher sensitivity (96%) and specificity (90%) than the classification results obtained from the direct usage of hippocampal shape deformations. In addition, the Laplace-Beltrami method requires consistently a smaller number of principal components (to retain a fixed variance) at higher resolution as compared to the binary and weighted graph Laplacians, demonstrating the potential of the wavelet bases in adapting to the geometry of the underlying manifold.
NASA Astrophysics Data System (ADS)
Balafas, Konstantinos; Kiremidjian, Anne S.
2014-03-01
This paper proposes a series of novel Damage Sensitive Features for earthquake damage estimation. The features take into account input (ground motion) and output acceleration (structure response) measurements. The Continuous Wavelet Transform is applied to both acceleration signals in order to obtain both time domain and frequency domain resolution. An algorithm that has been proposed for Maximum Entropy Deconvolution is applied to the Continuous Wavelet Transforms in order to obtain a matrix that relates the output wavelet coefficients to the input ones. The Damage Sensitive Features are then derived through statistical processing of the resulting matrix. This algorithm has been applied on data acquired from shake table tests where the structures were subjected to progressive damage. The proposed features are compared to response quantities that are indicative of damage (such as the hysteretic energy dissipated) and show high correlation with the extent of damage. The data utilized has not been pre-processed, illustrating the robustness of the algorithm against sensor noise. The proposed algorithm has several advantages: Minimal input and knowledge of the structure is required. More information on the structure's state is extracted through use of both the input and output signals than when only output signal is considered. Only two acceleration measurements are required to obtain a damage forecast utilizing primarily the strong motion recordings, resulting in easier sensor deployment. The use of strong motion recordings allows for information delivery immediately after an earthquake without additional data collection.
Wavelet multiscale processing of remote sensing data
NASA Astrophysics Data System (ADS)
Bagmanov, Valeriy H.; Kharitonov, Svyatoslav V.; Meshkov, Ivan K.; Sultanov, Albert H.
2008-12-01
There is comparative analysis of methods for estimation and definition of Hoerst index (index of self-similarity) and comparative analysis of wavelet types using for image decomposition are offered. Five types of compared wavelets are used for analysis: Haar wavelets, Daubechies wavelets, Discrete Meyer wavelets, symplets and coiflets. Best quality of restored image Meyer and Haar wavelets demonstrate, because of they are characterised by minimal errors of recomposition. But compression index for these types smaller, than for Daubechies wavelets, symplets and coiflets. Contrariwise the latter obtain less precision of decompression. As it is necessary to take into consideration the complexity of realization some wavelet transformation on digital signal processors (DSP), simplest method is Haar wavelet transformation.
NASA Astrophysics Data System (ADS)
Zhang, Pudun; Unger, Miriam; Pfeifer, Frank; Siesler, Heinz W.
2016-11-01
Variable-temperature Fourier-transform infrared (FT-IR) spectra of a predominantly amorphous and a semi-crystalline poly(L-lactic acid) (PLLA) film were measured between 30 °C and 170 °C in order to investigate their temperature-dependent structural changes as a function of the initial state of order. For an in-depth analysis of the spectral variations in the carbonyl stretching band region (1803-1722 cm-1) two-dimensional correlation spectroscopy (2DCOS) and perturbation-correlation moving-window two-dimensional (PCMW2D) analyses were applied. Significant spectral changes were observed during heating of the amorphous PLLA sample whereas the semi-crystalline specimen showed only slight band shifts as a function of the external perturbation. The PCMW2D results suggested that for efficient 2DCOS analyses the heating process should be split up in two temperature intervals. These analyses then provided information on the recrystallization of the amorphous regions, the presence of an intermediate state of order and a sequence scenario for the observed spectral changes.
NASA Astrophysics Data System (ADS)
Sánchez-Úbeda, Juan Pedro; Calvache, María Luisa; Duque, Carlos; López-Chicano, Manuel
2016-11-01
A new methodology has been developed to obtain tidal-filtered time series of groundwater levels in coastal aquifers. Two methods used for oceanography processing and forecasting of sea level data were adapted for this purpose and compared: HA (Harmonic Analysis) and CWT (Continuous Wavelet Transform). The filtering process is generally comprised of two main steps: the detection and fitting of the major tide constituents through the decomposition of the original signal and the subsequent extraction of the complete tidal oscillations. The abilities of the optional HA and CWT methods to decompose and extract the tidal oscillations were assessed by applying them to the data from two piezometers at different depths close to the shoreline of a Mediterranean coastal aquifer (Motril-Salobreña, SE Spain). These methods were applied to three time series of different lengths (one month, one year, and 3.7 years of hourly data) to determine the range of detected frequencies. The different lengths of time series were also used to determine the fit accuracies of the tidal constituents for both the sea level and groundwater heads measurements. The detected tidal constituents were better resolved with increasing depth in the aquifer. The application of these methods yielded a detailed resolution of the tidal components, which enabled the extraction of the major tidal constituents of the sea level measurements from the groundwater heads (e.g., semi-diurnal, diurnal, fortnightly, monthly, semi-annual and annual). In the two wells studied, the CWT method was shown to be a more effective method than HA for extracting the tidal constituents of highest and lowest frequencies from groundwater head measurements.
NASA Astrophysics Data System (ADS)
Ren, Zhiming; Liu, Yang; Zhang, Qunshan
2014-05-01
Full waveform inversion (FWI) has the potential to provide preferable subsurface model parameters. The main barrier of its applications to real seismic data is heavy computational amount. Numerical modelling methods are involved in both forward modelling and backpropagation of wavefield residuals, which spend most of computational time in FWI. We develop a time-space domain finite-difference (FD) method and adaptive variable-length spatial operator scheme in numerical simulation of viscoacoustic equation and extend them into the viscoacoustic FWI. Compared with conventional FD methods, different operator lengths are adopted for different velocities and quality factors, which can reduce the amount of computation without reducing accuracy. Inversion algorithms also play a significant role in FWI. In conventional single-scale methods, it is likely to converge to local minimums especially when the initial model is far from the real model. To tackle the problem, we introduce the second generation wavelet transform to implement the multiscale FWI. Compared to other multiscale methods, our method has advantages of ease of implementation and better time-frequency local analysis ability. The L2 norm is widely used in FWI and gives invalid model estimates when the data is contaminated with strong non-uniform noises. We apply the L1-norm and the Huber-norm criteria in the time-domain FWI to improve its antinoise ability. Our strategies have been successfully applied in synthetic experiments to both onshore and offshore reflection seismic data. The results of the viscoacoustic Marmousi example indicate that our new FWI scheme consumes smaller computer resources. In addition, the viscoacoustic Overthrust example shows its better convergence and more reasonable velocity and quality factor structures. All these results demonstrate that our method can improve inversion accuracy and computational efficiency of FWI.
NASA Astrophysics Data System (ADS)
Sadler, D. A.; Littlejohn, D.; Boulo, P. R.; Soraghan, J. S.
1998-08-01
A procedure to quantify the shape of the absorbance-time profile, obtained during graphite furnace atomic absorption spectrometry, has been used to detect interference effects caused by the presence of a concomitant salt. The quantification of the absorption profile is achieved through the use of the Lipschitz regularity, α0, obtained from the wavelet transform of the absorbance-time profile. The temporal position of certain features and their associated values of α0 provide a unique description of the shape of the absorbance-time profile. Changes to the position or values of α0 between standard and sample atomizations may be indicative of uncorrected interference effects. A weak, but linear, dependence was found of the value of α0 upon the analyte concentration for Cr and Cu. The ability of the Lipschitz regularity to detect interference effects was illustrated for Pb, Se and Cu. For Pb, the lowest concentration of NaCl added, 0.005% m/v, changed both the values of α0 and the peak height absorbance. For Se, no change in the peak height and peak area absorbance signals was detected up to a NaCl concentration of 0.25% m/v. The values of the associated Lipschitz regularities were found to be invariant to NaCl concentration up to this value. For Cu, a concentration of 0.05% m/v NaCl reduced the peak height and peak area absorbance signals by approximately 25% and significantly altered the values of α0.
NASA Astrophysics Data System (ADS)
Sohrabi, Mahmoud Reza; Darabi, Golnaz
2016-01-01
Flavonoids are γ-benzopyrone derivatives, which are highly regarded in these researchers for their antioxidant property. In this study, two new signals processing methods been coupled with UV spectroscopy for spectral resolution and simultaneous quantitative determination of Myricetin, Kaempferol and Quercetin as flavonoids in Laurel, St. John's Wort and Green Tea without the need for any previous separation procedure. The developed methods are continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM) methods integrated with UV spectroscopy individually. Different wavelet families were tested by CWT method and finally the Daubechies wavelet family (Db4) for Myricetin and the Gaussian wavelet families for Kaempferol (Gaus3) and Quercetin (Gaus7) were selected and applied for simultaneous analysis under the optimal conditions. The LS-SVM was applied to build the flavonoids prediction model based on absorption spectra. The root mean square errors for prediction (RMSEP) of Myricetin, Kaempferol and Quercetin were 0.0552, 0.0275 and 0.0374, respectively. The developed methods were validated by the analysis of the various synthetic mixtures associated with a well- known flavonoid contents. Mean recovery values of Myricetin, Kaempferol and Quercetin, in CWT method were 100.123, 100.253, 100.439 and in LS-SVM method were 99.94, 99.81 and 99.682, respectively. The results achieved by analyzing the real samples from the CWT and LS-SVM methods were compared to the HPLC reference method and the results were very close to the reference method. Meanwhile, the obtained results of the one-way ANOVA (analysis of variance) test revealed that there was no significant difference between the suggested methods.
Barbosa, Eduardo; García-Manso, Juan M; Martín-González, Juan M; Sarmiento, Samuel; Calderón, Francisco J; Da Silva-Grigoletto, Marzo E
2010-01-01
This study sought to determine the effects of hyperbaric pressure on heart rate modulation, by analyzing potential changes in heart rate variability (HRV). Ten divers were exposed to pressures of 1, 2, 3, and 4 atmospheres absolute (ATA). The test was performed in a hyperbaric chamber. Heart rate (HR) was recorded in supine subjects for 10 minutes per atmosphere. HRV was analyzed in the frequency mode (fast-Fourier transform and continuous wavelet transform). Results confirmed bradycardia as pressure increased. The drop in HR attained statistical significance after 2, 3, and 4 ATA. Signal energy (normalized TP values) rose progressively, becoming significant at 2 ATA. High frequency and low frequency displayed similar behavior in both cases. Although frequency band peaks did not yield clear results, continuous wave transform analysis showed that the frequency spectrum tended to shift into the high-frequency range as pressure increased. In summary, increased pressure prompted increased bradycardia, and HRV shifted into high-frequency range.
2013-01-01
Background Continuous and discrete wavelet transforms have been established as valid tools to analyze non-stationary and transient signals over Fourier domain methods. Additionally, Fourier transform based coherence methods provide aggregate results but do not provide insights into the changes in coherent behavior over time, hence limiting their utility. Methods Statistical validation of the wavelet transform coherence (WTC) was conducted with simulated data sets. Time frequency maps of signal coherence between calf muscle electromyography (EMG) and blood pressure (BP) were obtained by WTC to provide further insight into their interdependent time-varying behavior via the skeletal muscle pump during quiet stance. Data were collected from healthy young males (n = 5, 19–28 years) during a quiet stance on a balance platform. Waveforms for EMG and BP were acquired and processed for further analysis. Results Low values of bias and standard deviation (< 0.1) were observed and the use of both simulated and real data demonstrated that the WTC method was able to identify time points of significant coherence (> Threshold) and objectively detect existence of interdependent activity between the calf muscle EMG and blood pressure. Conclusions The WTC method effectively identified the presence of linear coupling between the EMG and BP signals during quiet standing. Future studies with more human data are needed to establish the exact characteristics of the identified relationship. PMID:24365103
NASA Astrophysics Data System (ADS)
Inoue, Takumi; Sueoka, Atsuo; Kanemoto, Hiroyuki; Odahara, Satoru; Murakami, Yukitaka
2007-07-01
If a machine in operation has a fault, signs of the fault appear in the monitored signal and are usually synchronised with the operating speed. The signs are very small when the fault is at an early stage. The fast Fourier transform (FFT) is often utilised to detect these signs, but it is very difficult to detect minute signs. In this paper, harmonic wavelet transform is utilised to detect the minute signs of small faults in a monitored signal. The monitored signal of a machine element, in ordinary operation, is regarded as periodic or quasi-periodic. It is important for the effectual detection of the minute signs to reduce the obstructive noise and the end effects in the signal. The end effect is a peculiar phenomenon to wavelet transform and hampers effective detection. Useful methods to reduce the obstructive noise and the end effects are devised herein by the authors. Even if no visible information pertaining to a fault appears in the monitored waveform, the present method is able to detect a minute sign of a small fault. It is demonstrated that the present method is capable of detecting minute signs arising from slight collisions caused by a loose coupling and a fatigue crack.
Rodriguez-Hernandez, Miguel A; Gomez-Sacristan, Angel; Sempere-Payá, Víctor M
2016-04-29
Ultrasound diagnosis is a widely used medical tool. Among the various ultrasound techniques, ultrasonic imaging is particularly relevant. This paper presents an improvement to a two-dimensional (2D) ultrasonic system using measurements taken from perpendicular planes, where digital signal processing techniques are used to combine one-dimensional (1D) A-scans were acquired by individual transducers in arrays located in perpendicular planes. An algorithm used to combine measurements is improved based on the wavelet transform, which includes a denoising step during the 2D representation generation process. The inclusion of this new denoising stage generates higher quality 2D representations with a reduced level of speckling. The paper includes different 2D representations obtained from noisy A-scans and compares the improvements obtained by including the denoising stage.
Rodriguez-Hernandez, Miguel A; Gomez-Sacristan, Angel; Sempere-Payá, Víctor M
2016-04-29
Ultrasound diagnosis is a widely used medical tool. Among the various ultrasound techniques, ultrasonic imaging is particularly relevant. This paper presents an improvement to a two-dimensional (2D) ultrasonic system using measurements taken from perpendicular planes, where digital signal processing techniques are used to combine one-dimensional (1D) A-scans were acquired by individual transducers in arrays located in perpendicular planes. An algorithm used to combine measurements is improved based on the wavelet transform, which includes a denoising step during the 2D representation generation process. The inclusion of this new denoising stage generates higher quality 2D representations with a reduced level of speckling. The paper includes different 2D representations obtained from noisy A-scans and compares the improvements obtained by including the denoising stage. PMID:27163318
Tests for Wavelets as a Basis Set
NASA Astrophysics Data System (ADS)
Baker, Thomas; Evenbly, Glen; White, Steven
A wavelet transformation is a special type of filter usually reserved for image processing and other applications. We develop metrics to evaluate wavelets for general problems on test one-dimensional systems. The goal is to eventually use a wavelet basis in electronic structure calculations. We compare a variety of orthogonal wavelets such as coiflets, symlets, and daubechies wavelets. We also evaluate a new type of orthogonal wavelet with dilation factor three which is both symmetric and compact in real space. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award #DE-SC008696.
Lajnef, Tarek; Chaibi, Sahbi; Eichenlaub, Jean-Baptiste; Ruby, Perrine M.; Aguera, Pierre-Emmanuel; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim
2015-01-01
A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed. PMID
Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit
2015-01-01
Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation. PMID:26435897
Zhou, Xilong; Liu, Xuefeng; Zhang, Zhaobao; Wang, Xumin; Liu, Tao; Liu, Guiming
2014-01-01
Coral reefs occupy a relatively small portion of sea area, yet serve as a crucial source of biodiversity by establishing harmonious ecosystems with marine plants and animals. Previous researches mainly focused on screening several key genes induced by stress. Here we proposed a novel method—correlation analysis after wavelet transform of complex network model, to explore the effect of light on gene expression in the coral Acropora millepora based on microarray data. In this method, wavelet transform and the conception of complex network were adopted, and 50 key genes with large differences were finally captured, including both annotated genes and novel genes without accurate annotation. These results shed light on our understanding of coral's response toward light changes and the genome-wide interaction among genes under the control of biorhythm, and hence help us to better protect the coral reef ecosystems. Further studies are needed to explore how functional connections are related to structural connections, and how connectivity arises from the interactions within and between different systems. The method introduced in this study for analyzing microarray data will allow researchers to explore genome-wide interaction network with their own dataset and understand the relevant biological processes. PMID:24651851
NASA Astrophysics Data System (ADS)
Ahmad, Mirza Naseer; Rowell, Philip; Sriburee, Suchada
2014-06-01
Fluvial sands host excellent oil and gas reservoirs in various fields throughout the world. However, the lateral heterogeneity of reservoir properties within these reservoirs can be significant and determining the distribution of good reservoirs is a challenge. This study attempts to predict sand distribution within fluvial depositional systems by applying the Continuous Wavelet Transformation technique of spectral decomposition along with full spectrum seismic attributes, to a 3D seismic data set in the Pattani Basin, Gulf of Thailand. Full spectrum seismic attributes such as root mean square and coherency help to effectively map fluvial systems down to certain depth below which imaging is difficult in the intervals of interest in this study. However, continuous wavelet transform used in conjunction with other attributes by applying visualization techniques of transparency and RGB can be used at greater depths to extract from 3D seismic data useful information of fluvial depositional elements. This workflow may help to identify different reservoir compartments within the fluvial systems of the Gulf of Thailand.
Jin, Wenying; Wan, Chayan; Cheng, Cungui
2015-01-01
The attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) was employed to acquire the infrared spectra of Radix Bupleuri and its unofficial varieties: the root of Bupleurum smithii Wolff and the root of Bupleurum bicaule Helm. The infrared spectra and spectra of Fourier self-deconvolution (FSD), discrete wavelet transform (DWT), and probability neural network (PNN) of these species were analyzed. By the method of FSD, there were conspicuous differences of the infrared absorption peak intensity of different types between Radix Bupleuri and its unofficial varieties. But it is hard to tell the differences between the root of Bupleurum smithii Wolff and the root of Bupleurum bicaule. The differences could be shown more clearly when the DWT was used. The research result shows that by the DWT technology it is easier to identify Radix Bupleuri from its unofficial varieties the root of Bupleurum smithii Wolff and the root of Bupleurum bicaule. PMID:25784938
Paul, Sabyasachi; Rao, D D; Sarkar, P K
2012-11-01
Measurement of environmental dose in the vicinity of a nuclear power plant site (Tarapur, India) is carried out continuously for the years 2007-2010 and attempts have been made to quantify the additional contributions from nuclear power plants over natural background by segregating the background fluctuations from the events due to plume passage using a non-decimated wavelet approach. A conservative estimate obtained using wavelet based analysis has shown a maximum annual dose of 38 μSv in a year at 1.6 km and 4.8 μSv at 10 km from the installation. The detected events within a year are in good agreement with the month wise wind-rose profile indicating reliability of the algorithm for proper detection of an event from the continuous dose rate measurements. The results were validated with the dispersion model dose predictions using the source term from routine monitoring data and meteorological parameters.
Abbasi Tarighat, Maryam
2016-02-01
Simultaneous spectrophotometric determination of a mixture of overlapped complexes of Fe(3+), Mn(2+), Cu(2+), and Zn(2+) ions with 2-(3-hydroxy-1-phenyl-but-2-enylideneamino) pyridine-3-ol(HPEP) by orthogonal projection approach-feed forward neural network (OPA-FFNN) and continuous wavelet transform-feed forward neural network (CWT-FFNN) is discussed. Ionic complexes HPEP were formulated with varying reagent concentration, pH and time of color formation for completion of complexation reactions. It was found that, at 5.0 × 10(-4) mol L(-1) of HPEP, pH 9.5 and 10 min after mixing the complexation reactions were completed. The spectral data were analyzed using partial response plots, and identified non-linearity modeled using FFNN. Reducing the number of OPA-FFNN and CWT-FFNN inputs were simplified using dissimilarity pure spectra of OPA and selected wavelet coefficients. Once the pure dissimilarity plots ad optimal wavelet coefficients are selected, different ANN models were employed for the calculation of the final calibration models. The performance of these two approaches were tested with regard to root mean square errors of prediction (RMSE %) values, using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of metal ions in different vegetable and foodstuff samples. The results show that, OPA-FFNN and CWT-FFNN were effective in simultaneously determining Fe(3+), Mn(2+), Cu(2+), and Zn(2+) concentration. Also, concentrations of metal ions in the samples were determined by flame atomic absorption spectrometry (FAAS). The amounts of metal ions obtained by the proposed methods were in good agreement with those obtained by FAAS. PMID:26304383
Group-normalized wavelet packet signal processing
NASA Astrophysics Data System (ADS)
Shi, Zhuoer; Bao, Zheng
1997-04-01
Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, group- normalized wavelet packet transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)-frequency masking technique. The extended F-entropy improves the performance of GNWPT. For perception-based image, soft-logic masking is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signal processing, such as images and ECG signal, the compactly supported spline or bi- orthogonal wavelet packets are preferred for perfect de- noising and filtering qualities.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Ochoa Domínguez, Humberto de Jesús; Máynez, Leticia O.; Vergara Villegas, Osslan O.; Mederos, Boris; Mejía, José M.; Cruz Sánchez, Vianey G.
2015-06-01
PET allows functional imaging of the living tissue. However, one of the most serious technical problems affecting the reconstructed data is the noise, particularly in images of small animals. In this paper, a method for high-resolution small animal 3D PET data is proposed with the aim to reduce the noise and preserve details. The method is based on the estimation of the non-subsampled Haar wavelet coefficients by using a linear estimator. The procedure is applied to the volumetric images, reconstructed without correction factors (plane reconstruction). Results show that the method preserves the structures and drastically reduces the noise that contaminates the image.
NASA Astrophysics Data System (ADS)
Singh, Neeraj Kumar; Snoussi, Hichem; Hewson, David; Duchêne, Jacques
The aim of this study was to develop a method to detecting the critical point interval (CPI) when sensory feedback is used as part of a closed-loop postural control strategy. Postural balance was evaluated using centre of pressure (COP) displacements from a force plate for 17 control and 10 elderly subjects under eyes open, eyes closed, and vibration conditions. A modified local-maximum-modulus wavelet transform analysis using the power spectrum of COP signals was used to calculate CPI. Lower CPI values indicate increased closed-loop postural control with a quicker response to sensory input. Such a strategy requires greater energy expenditure due to the repeated muscular interventions to remain stable. The CPI for elderly occurred significantly quicker than for controls, indicating tighter control of posture. Similar results were observed for eyes closed and vibration conditions. The CPI parameter can be used to detect differences in postural control due to ageing.
Li, Chuan; Peng, Juan; Liang, Ming
2014-01-01
Oil debris sensors are effective tools to monitor wear particles in lubricants. For in situ applications, surrounding noise and vibration interferences often distort the oil debris signature of the sensor. Hence extracting oil debris signatures from sensor signals is a challenging task for wear particle monitoring. In this paper we employ the maximal overlap discrete wavelet transform (MODWT) with optimal decomposition depth to enhance the wear particle monitoring capability. The sensor signal is decomposed by the MODWT into different depths for detecting the wear particle existence. To extract the authentic particle signature with minimal distortion, the root mean square deviation of kurtosis value of the segmented signal residue is adopted as a criterion to obtain the optimal decomposition depth for the MODWT. The proposed approach is evaluated using both simulated and experimental wear particles. The results show that the present method can improve the oil debris monitoring capability without structural upgrade requirements. PMID:24686730
Matsui, T; Ohki, M; Nakamura, T; Takagi, Y
2014-06-01
Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosed with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.
Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen
2016-09-01
With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein-protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual "Random Forest" classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties. Cross-validation tests indicate that the new predictor is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions. The web server of iPPBS-PseAAC is accessible at http://www.jci-bioinfo.cn/iPPBS-PseAAC , by which users can easily acquire their desired results without the need to follow the complicated mathematical equations involved.
NASA Astrophysics Data System (ADS)
Conde, Vladimir; Bredemeyer, Stefan; Saballos, J. Armando; Galle, Bo; Hansteen, Thor H.
2016-07-01
San Cristóbal volcano is the highest and one of the most active volcanoes in Nicaragua. Its persistently high activity during the past decade is characterized by strong degassing and almost annual VEI 1-2 explosions, which present a threat to the local communities. Following an eruption on 8 September 2012, the intervals between eruptions decreased significantly, which we interpret as the start of a new eruptive phase. We present here the results of semi-continuous SO2 flux measurements covering a period of 18 months, obtained by two scanning UV-DOAS instruments installed as a part of the network for observation of volcanic and atmospheric change project, and the results of real-time seismic amplitude measurements (RSAM) data. Our data comprise a series of small to moderately explosive events in December 2012, June 2013 and February 2014, which were accompanied by increased gas emissions and seismicity. In order to approach an early warning strategy, we present a statistical method for the joint analysis of gas flux and seismic data, by using continuous wavelet transform and cross-wavelet transform (XWT) methods. This analysis shows that the XWT coefficients of SO2 flux and RSAM are in good agreement with the occurrence of eruptive events and thus may be used to indicate magma ascent into the volcano edifice. Such multi-parameter surveillance efforts can be useful for the interpretation and surveillance of possible eruptive events and could thus be used by local institutions for the prediction of upcoming volcanic unrest.
Parallel object-oriented, denoising system using wavelet multiresolution analysis
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.
NASA Astrophysics Data System (ADS)
Karacan, C. Özgen; Olea, Ricardo A.
2014-06-01
Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile. For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs. Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal-Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths. Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas
NASA Astrophysics Data System (ADS)
Brown, P.; Wong, D. K.; Weryk, R. J.; Wiegert, P.
2010-05-01
A 7 year survey using the Canadian Meteor Orbit Radar (CMOR), a specular backscattering orbital radar, has produced three million individually measured meteoroid orbits for particles with mean mass near 10 -7 kg. We apply a 3D wavelet transform to our measured velocity vectors, partitioning them into 1° solar longitude bins while stacking all 7 years of data into a single "virtual" year to search for showers which show annual activity and last for at least 3 days. Our automated stream search algorithm has identified 117 meteor showers. We have recovered 42 of the 45 previously described streams from our first reconnaissance survey (Brown, P., Weryk, R.J., Wong, D.K., Jones, J. [2008]. Icarus 195, 317-339). Removing possible duplicate showers from the automated results leaves 109 total streams. These include 42 identified in survey I and at least 62 newly identified streams. Our large data sample and the enhanced sensitivity of the 3D wavelet search compared to our earlier survey have allowed us to extend the period of activity for several major showers. This includes detection of the Geminid shower from early November to late December and the Quadrantids from early November to mid-January. Among our newly identified streams are the Theta Serpentids which appears to be derived from 2008 KP and the Canum Venaticids which have a similar orbit to C/1975 X1 (Sato). We also find evidence that nearly 60% of all our streams are part of seven major stream complexes, linked via secular invariants.
A new wavelet-based thin plate element using B-spline wavelet on the interval
NASA Astrophysics Data System (ADS)
Jiawei, Xiang; Xuefeng, Chen; Zhengjia, He; Yinghong, Zhang
2008-01-01
By interacting and synchronizing wavelet theory in mathematics and variational principle in finite element method, a class of wavelet-based plate element is constructed. In the construction of wavelet-based plate element, the element displacement field represented by the coefficients of wavelet expansions in wavelet space is transformed into the physical degree of freedoms in finite element space via the corresponding two-dimensional C1 type transformation matrix. Then, based on the associated generalized function of potential energy of thin plate bending and vibration problems, the scaling functions of B-spline wavelet on the interval (BSWI) at different scale are employed directly to form the multi-scale finite element approximation basis so as to construct BSWI plate element via variational principle. BSWI plate element combines the accuracy of B-spline functions approximation and various wavelet-based elements for structural analysis. Some static and dynamic numerical examples are studied to demonstrate the performances of the present element.
NASA Astrophysics Data System (ADS)
Luk, B. L.; Liu, K. P.; Tong, F.; Man, K. F.
2010-05-01
The impact-acoustics method utilizes different information contained in the acoustic signals generated by tapping a structure with a small metal object. It offers a convenient and cost-efficient way to