Nguyen-Ky, T. and Wen, Peng and Li, Yan (2009) Monitoring the depth of anesthesia using discrete wavelet transform and power spectral density. In: 4th International Conference on Rough Sets and Knowledge Technology (RSKT 2009), 14-16 Jul 2009, Gold Coast, Australia. ...
A rapid, simple, precise, accurate, and environmentally friendly spectrophotometric method was developed and validated for simultaneous determination of Sacubitril and Valsartan in their combined dosage form, using continuous wavelet transform (CWT) and zero-crossing techniques without using organic solvents and the time-consuming extraction step.Initially, UV spectra of two pure components in water were processed via various mother wavelet families. Then, applying zero-crossing technique, the optimum points were found to obtain appropriate calibration curves for each point. The calibration curves were linear for both Sacubitril and Valsartan. The validation of these methods was investigated by analyzing several synthetic mixtures with known concentrations. Applying one-way analysis of variance (ANOVA) test and Fisher pairwise comparisons, the following were found to yield the best results: Discrete Meyer (dmey) wavelet functions with scaling factor of 61 at 232 nm and Symlet5 (sym5) with 48 at 232 nm
Kingsbury, NG and Zymnis, A (2004) 3D diffusion tensor magnetic resonance imaging data visualisation using the dual tree complex wavelet transform. In: The EURASIP Conference Biosignal; Analysis of Biomedical Signals and Images, 2004-6- to -- pp. 261-266... Full text not available from this repository ...
TY - JOUR. T1 - Wavelet analysis of a quasiperiodic tiling with fivefold symmetry. AU - Antoine, J.-P.. AU - Jacques, L.. AU - Twarock, Reidun. PY - 1999/10/18. Y1 - 1999/10/18. N2 - We determine all (statistical) rotation-dilation symmetries of a planar quasiperiodic tiling with fivefold symmetry, with a two-dimensional continuous wavelet transform, using a modified Cauchy wavelet and the scale-angle measure. The tiling is constructed via an affine extension of the Coxeter group H and its statistical symmetries were unknown.. AB - We determine all (statistical) rotation-dilation symmetries of a planar quasiperiodic tiling with fivefold symmetry, with a two-dimensional continuous wavelet transform, using a modified Cauchy wavelet and the scale-angle measure. The tiling is constructed via an affine extension of the Coxeter group H and its statistical symmetries were unknown.. UR - http://www.scopus.com/inward/record.url?scp=0345984490&partnerID=8YFLogxK. U2 - 10.1016/S0375-9601(99)00634-9. DO - ...
9783540242598 Abstract Harmonic Analysis of Continuous Wavelet Transforms (Lecture Notes in Mathematics),books, textbooks, text book
Spectrum sensing is one of the crucial aspects in Cognitive Radio (CR). Fast and accurate spectrum opportunity detection provides interference avoidance to other/licensed users. At the same time, it offers more efficient spectrum utilization by providing accurate sensing information as an input to the intelligent dynamic resource allocation process. Wideband spectrum sensing has been introduced due to the higher bandwidth demand and increasing spectrum scarcity since it provides better chance of detecting spectrum opportunity. In this paper, the application of wavelet transform techniques for wideband spectrum opportunity detection in CRs is documented. Wavelet analysis is used in two-step process detection or multi-resolution opportunity detection proposed here. Edge detection using wavelet analysis is employed in the first step to indentify possibly available subband(s). The fine analysis is done in the second step for each chosen subband(s) using wavelet transform in order to detect any ...
Improving efficiency of disease diagnosis based on phenotype ontology is a critical yet challenging research area. Recently, Human Phenotype Ontology (HPO)-based semantic similarity has been affectively and widely used to identify causative genes and diseases. However, current phenotype similarity measurements just consider the annotations and hierarchy structure of HPO, neglecting the definition description of phenotype terms. In this paper, we propose a novel phenotype similarity measurement, termed as DisPheno, which adequately incorporates the definition of phenotype terms in addition to HPO structure and annotations to measure the similarity between phenotype terms. DisPheno also integrates phenotype term associations into phenotype-set similarity measurement using gene and disease annotations of phenotype terms. Compared with five existing state-of-the-art methods, DisPheno shows great performance in HPO-based phenotype semantic similarity measurement and improves the efficiency of disease
A one-dimensional wavelet system and method. In various embodiments, computation engines are set forth for forward and inverse transforms in a wavelet system. The computation engine includes a plurality of register banks having input ports arranged to receive input sample values and a multiplexer coupled to the output ports of the register banks. A processing unit is configured to perform the forward or inverse wavelet transform for data values that are sequenced through the register banks and multiplexer by a control unit. The computation unit is adaptable to implement discrete wavelet transform, discrete wavelet packet, and custom wavelet trees.
It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of
Wavelet-domain hidden Markov models (HMMs), in particular hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the 2-D discrete wavelet transform (DWT), i.e. HL, LH, and HH, are independent. In this paper, we study wavelet-based texture analysis and synthesis using HMMs. Particularly, we develop a new HMM, called HMT-3S, for statistical texture characterization in the wavelet-domain. In addition to the joint statistics captured by HMT, the new HMT-3S can also exploit the crosscorrelation across DWT subbands. Meanwhile, HMT-3S can be characterized by using the graphical grouping technique, and has the same tree structure as HMT. The proposed HMT-3S is applied to texture analysis, including classification and segmentation, and texture synthesis with improved performance over HMT. Specifically, for texture classification, we study four wavelet-based methods, and experimental results show that HMT-3S provides the highest
An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals: 10.4018/jalr.2010040101: In this study, the authors propose an approach for detecting R-wave of electrocardiogram (ECG) signals. A statistical process control chart is successfully
Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.
Heart rate variability (HRV) can be quantified, among others, in the frequency domain using digital signal processing (DSP) techniques. The wavelet transform is an alternative tool for the analysis of non-stationary signals. The implementation of perfect reconstruction digital filter banks leads to multi resolution wavelet analysis. Software was developed in LabVIEW. In this study, the average power was compared at each decomposition level of a tachogram, containing the consecutive RR-intervals of two groups of subjects: aerobic athletes and a control group. Compared to the controls, the aerobic athletes showed an increased power in all frequency bands. These results are in accordance with values obtained by spectral analysis using the Fourier transform, suggesting that wavelet analysis could be an appropriate tool to evaluate oscillating components in HRV, but in addition to classic methods, it also gives a time resolution ...
We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: first, it needs to model and respect the statistics of natural images in order to reconstruct natural looking images; second, it needs to be able to perform well in the presence of noise. To facilitate an objective assessment of current methods we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1. the model is trained to directly optimize a user-specified performance ...
Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature
We introduce a new class of wavelets that behave like a given differential operator L. Our construction is inspired by the derivative-like behavior of classical wavelets. Within our framework, the wavelet coefficients of a signal y are the samples of a smoothed version of L{y}. For a linear system characterized by an operator equation L{y} = x, the operator-like wavelet transform essentially deconvolves the system output y and extracts the innovation signal x. The main contributions of the thesis include: Exponential-spline wavelets. We consider the system L described by a linear differential equation and build wavelets that mimic the behavior of L. The link between the wavelet and the operator is an exponential B-spline function; its integer shifts span the multiresolution space. The construction that we obtain is non-stationary in the sense that the wavelets and the scaling functions depend on the scale. We propose a generalized version of Mallats fast filterbank algorithm with ...
This is a list of transforms in mathematics. Abel transform Bateman transform Bracewell transform Fourier transform Short-time Fourier transform Hankel transform Hartley transform Hermite transform Hilbert transform Hilbert-Schmidt integral operator Jacobi transform Laguerre transform Laplace transform Inverse Laplace transform Two-sided Laplace transform Inverse two-sided Laplace transform Laplace-Carson transform Laplace-Stieltjes transform Legendre transform Linear canonical transform Mellin transform Inverse Mellin transform Poisson-Mellin-Newton cycle N-transform Radon transform Stieltjes transformation Sumudu transform Wavelet transform (integral) Weierstrass transform Binomial transform Discrete Fourier transform, DFT Fast Fourier transform, a popular implementation of the DFT Discrete cosine transform Modified discrete cosine transform Discrete Hartley transform Discrete sine transform Discrete wavelet transform Hadamard transform (or, Walsh-Hadamard transform) Fast wavelet transform ...
Abstract: One of the most promising non-invasive markers of the activity of the autonomic nervous system is Heart Rate Variability (HRV). HRV analysis toolkits often provide spectral analysis techniques using the Fourier transform, which assumes that the heart rate series is stationary. To overcome this issue, the Short Time Fourier Transform is often used (STFT). However, the wavelet transform is thought to be a more suitable tool for analyzing non-stationary signals than the STFT. Given the lack of support for wavelet-based analysis in HRV toolkits, such analysis must be implemented by the researcher. This has made this technique underutilized. This paper presents a new algorithm to perform HRV power spectrum analysis based on the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The algorithm calculates the power in any spectral band with a given tolerance for the bands boundaries. The MODWPT decomposition tree is pruned to avoid calculating unnecessary wavelet coefficients, ...
In this paper, an image fusion method is proposed based on the non-separable wavelet frame (NWF) for merging a high-resolution panchromatic image and a low-resolution multispectral image. The lowfrequency part of the panchromatic image is directly substituted by multispectral image. As a result, the multispectral information of the multispectral image can be preserved effectively in the fused image. Due to multiscale method for enhancing the high-frequency parts of the panchromatic image, spatial information of the fused image can be improved. Experimental results indicate that the proposed method outperforms the intensity-hue-saturation (IHS) transform, discrete wavelet transform and separable wavelet frame inpreserving spectral and spatial information.. © 2005 Chinese Optics Letters. PDF Article ...
TY - JOUR. T1 - Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response. AU - Zhang, R. AU - McAllister, G. AU - Scotney, BW. AU - McClean, SI. AU - Houston, G. N1 - Other Details ------------------------------------ This paper was selected (with re-review) for journal publication from papers presented at the 18th IEEE International Symposium on Computer-Based Medical Systems, June 2005. The paper develops an algorithm that enables hearing response to be assessed using less than one-tenth of the data (from auditory brainstem responses to stimuli) typically required by techniques currently used by audiologists. Thus the length of assessment sessions may be reduced, with corresponding reductions in patient discomfort and audiologists time. Hearing classification accuracy has been evaluated in collaboration with (and using data provided by) an Audiological Scientist at the Royal Hospitals, Belfast (Glen Houston).. PY - 2006/7/1. Y1 - 2006/7/1. N2 - ...
Read "Quantum - coherent dynamics in photosynthetic charge separation revealed by wavelet analysis, Scientific Reports" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
This paper describes two approaches for accomplishing interactive feature analysis by overcomplete multiresolution representations. We show quantitatively that transform coefficients, modified by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a filter bank representing a steerable dyadic wavelet transform that can be used for multiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicion) areas (lesions). Features
Dr. Cuculich introduced new concepts in the use of ECGI (at least to this author)-phase mapping and the importance of wavefronts or wavelet transformation in A-Fib signals. "ECGI uses wavelet transform looking at pure activation time." He asked, "how does…phase mapping affect the result?" He related phase mapping to the CONFIRM concept of "phase lock" where a simple 12-lead ECG analysis can classify A-Fib mechanistically.3. Doctors (and we patients) are still struggling to understand what phase mapping and wavelet transformation actually mean. Dr. Cuculichs studies of phase mapping techniques (Hilbert transform) in A-Fib show that phase mapping highlights and accentuates the curvature of a wavefront and thus indicates a rotor is present. According to Dr. Cuculich, phase mapping is highly dependent on the chosen cycle length. He concluded that "while published ECGI data used wavelet transform to identify activation patterns, phase mapping techniques (when performed carefully and correctly) may ...
The change in firing activity caused by the tail pinch was determined by comparing the firing rate in a 10 s window before the pinch with the firing rate during the pinch (Wilcoxon signed rank test).. Slow oscillation periods were detected as data segments with theta (3-6 Hz) to delta (2-3 Hz) ratios ,2 derived from recordings in the stratum oriens or pyramidale of the dorsal CA1 region. Theta-to-delta ratios were smoothed by a sliding average with a length of 5 s and 50% overlap. The minimal duration of analyzed data segments was 10 s. Slow oscillations in the medial prefrontal cortex were recorded by the same glass electrode that was used for juxtacellular labeling. The discrimination of DOWN- and UP-states was done by extracting the shape information below 2.5 Hz after a continuous wavelet transform using a Daubechies (3) wavelet function and by setting the threshold to cos(θ) = 0. This wavelet transform was confirmed not to cause a phase shift, and similar results were obtained by using a ...
To present and evaluate a new algorithm, based on the wavelet transform, for the automatic measurement of motor unit action potential (MUAP) duration. A total of 240 MUAPs were studied. The waveform of each MUAP was wavelet-transformed, and the start and end points were estimated by regarding the maxima and minima points in a particular scale of the wavelet transform. The results of the new method were compared with the gold standard of duration marker positions obtained by manual measurement. The new method was also compared with a conventional algorithm, which we had found to be best in a previous comparative study. To evaluate the new method against manual measurements, the dispersion of automatic and manual duration markers were analyzed in a set of 19 repeatedly recorded MUAPs. The differences between the new algorithms marker positions and the gold standard of duration marker positions were smaller than those observed with the conventional method. The dispersion of the new algorithms ...
Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via
A Wavelet is a mathematical function used to write down a function or signal in terms of other functions that are simpler to study. Many signal processing tasks can be seen in terms of a wavelet transform. Informally speaking, the signal can be seen under the lens with a magnification given by the scale of the wavelet. In doing so, we can see only the information that is determined by the shape of the wavelet used. The English term "wavelet" was introduced in the early 1980s by French physicists Jean Morlet and Alex Grossman. They used the French word "ondelette" (which means "small wave"). Later, this word was brought into English by translating "onde" into "wave" giving "wavelet". Wavelet is (complex) function from the Hilbert space ...
ANNOUNCEMENT: Dear Colleague: In the upcoming International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) in Montreal, September 20-23, 1995, a 1 1/2 days workshop on NEW ADVANCES IN BIOMEDICAL SIGNAL AND IMAGE PROCESSING will be held before the conference to discuss and present new advances in biomedical signal and image processing methods and recent applications of these emerging technologies including Time-Frequency, Wavelet Transform, and Wavelet Packets. I am pleased to invite you to join us for these exciting presentations by prominent experts in engineering, medicine, computer science and applied mathematics. The cost for each attendee will be: 1. $100 for IEEE student member 2. $200 IEEE members, 3. $300 non IEEE members. If you are interested in joining us at Montreal, and have any questions about the workshop and registrations, please contact me at Rutgers (908-445-4096) or e-mail (akay at gandalf.rutgers.edu). Sincerely, Metin Akay, Ph.D. Workshop Chair ...
In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP) image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA) is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face
Introduction: Signal processing and machine learning (ML) paradigms may serve as an automated tool for analyzing and discovering the hidden changes in the ECG in order to predict the presence and severity of hemorrhage. In this study a method based on discrete wavelet transform (DWT) and ML is used to analyze raw ECG, extract significant ECG features, and then use them to identify severe volume loss.. Methods: The algorithm detects and extracts time-interval and amplitude features from the ECG that describe ECG changes due to volume loss. These features include PQ, PR, QT, and ST duration, amplitude of T over amplitude of R, and amplitude of P over amplitude of R from each ECG cycle. The system was tested on volunteers undergoing lower body negative pressure (LBNP) as a hemorrhage mimetic. The LBNP protocol consisted of a 5 min rest period (0 mm Hg) followed by 5 min of chamber decompression of the lower body to −15, −30, −45, and −60 mm Hg and additional increments of −10 mm Hg every ...
Wavelet transforms are used to explore the dynamic evolution of granular material by post-processing data from DEM simulations. Data sets generated from two different physical scenarios were analysed: particles in vibration cells and samples under compression. It is shown how the wavelet multiresolution analysis (MRA) helps to reveal the particle motions within a vibration box subject to applied vibrations. Two combinations of amplitude and frequency vibration were applied in this work: low frequency with high amplitude and high frequency with low amplitude. Both cases give the same acceleration amplitude of approximately 7.8g where g is the acceleration due to gravity and both mono-sized particles and binary mixture were considered. The root mean square of the fluctuating velocity and the packing fraction of the two data sets are also consistent with the analysis results of the wavelets technique. Furthermore, it is shown how the MRA techniques were used to capture the position of ...
A method and an apparatus for compressing or decompressing two-dimensional electronic data are provided. The method for compressing the two-dimensional electronic data set includes dividing the data set into data arrays, performing a wavelet transformation on each array to provide a plurality of wavelet coefficients, and encoding at least some of the wavelet coefficients using an entropy encoding scheme. Each data array preferably relates to a separate and continuous area of an image.
Advance in computer vision (CV) have led to an increasing market for biometric recognition systems. However, as more users are registered in a system, its expanding dataset will increase the systems response time and lower its recognition stability. As mentioned above, we propose a new high-performance algorithm suitable for embedded finger-vein recognition systems. First, the semantic segmentation based on DeepLabv3+ to filter out the background noise and enhance processing stability. The adaptive symmetric mask-based discrete wavelet transform (A-SMDWT) and adaptive image contrast enhancement were used in the pre-processing of images, and feature extraction was performed through the repeated line tracking (RLT) method. Next, the histogram of oriented gradient (HOG) of the image is computed, after which a support vector machine (SVM) was then used to train a classifier. Finally, a self-established finger-vein image dataset as well as a public dataset was implemented in the Raspberry Pi ...
Bronchiolar obstruction is commonly manifested in computed tomography (CT) images as areas of decreased attenuation relative to adjacent normal lung parenchyma. The certain identification of such areas is difficult in practice, particularly if they are poorly marginated. This paper presents a novel approach to the enhancement of feature differences between normal and diseased lung parenchyma so that reliable visual assessment can be made. The method relies on a hybrid structural filtering technique which removes pulmonary vessels appearing in the CT cross-sectional images without affecting intrinsic subtle intensity details of the lung parenchyma. In order to restore possible structural distortions introduced by the hybrid filter, a feature localization process based on wavelet reconstruction of feature extrema is used. After contrast enhancement the resultant images are used to delineate region borders of the diseased areas and quantification is made with regard to the extent of the disease ...
TY - CONF. T1 - Testing for public debt sustainability using band spectrum regression analysis ". AU - Cipollini, Andrea. AU - Lo Cascio, Iolanda. PY - 2014. Y1 - 2014. N2 - In this note we focus on the response of the primary surplus to debt (ratios to GDP)over a low frequency band (associated with cycles with period between eight and sixteen years) to filter out business cycle effects. For this purpose, we use band spectrum regression, using both the Fourier Transform and the Discrete Wavelet transform, fitted to pooled panel dataset of 18 EMU countries. The empirical findings give evidence of fiscal fatigue within Eurozone:the response of primary surplus to debt will decrease over a finite debt limit.. AB - In this note we focus on the response of the primary surplus to debt (ratios to GDP)over a low frequency band (associated with cycles with period between eight and sixteen years) to filter out business cycle effects. For this purpose, we use band spectrum regression, using both the Fourier ...
Investigation of changes in rainfall and runoff patterns in various regions and determining their relationship in the sense of hydrology and climatology are of great importance, considering those patterns efficiently reveal the human and natural factors in this variability. One of the mathematical methods to recognise and model these fluctuations is Wavelet Analysis. This is a spectral method used in multivariateanalysis and also tracing fluctuations in temporal series. In this study, continuous wavelet transformation is used to identify temporal changes in rainfall-runoff patterns. The hydrological and rain gauge data were collected from in situ measurements of Kermanshah province located in the western border of Iran.Precipitation anomalies were reconsidered in a number of stations, including Kermanshah, for a period of 55 years (1955-2010) and discharge of Gamasiab River in Polchehr station, discharge of Khoram Rood River in Aran-Gharb station and discharge of Gharasoo River in Polekohne ...
The complexity of bioprocess data generated from the manufacture of recombinant monoclonal antibodies cell line sparks the chemometrics study of the datasets. The ultimate goal is to form a link between the data and its underlying biological patterns. A concatenated protocol, specifically discrete wavelet transform and multiway principal component analysis techniques, is recommended to successfully extract meaningful information from the complex datasets.. ...
Continuous wavelet transform (CWT) is a useful technique to analyse time-varying signals. Direct computation of CWT via FFT requires O(Nlog/sub 2/N) operat
Summary Seismic inversion is the process of removing the effects of propagated wavelet to recover the values of acoustic impedance. To estimate a reliable mixed-phase wavelet for a viscoelastic medium, we utilized higher-order statistics method. Non-minimum phase wavelet estimation based on cumulant matching is known as statistical wavelet estimation approach. To apply this approach, we windowed the seismic data in such a way that two consecutive windows were only one sample apart. No fixed wavelet was considered for any windows and we let the phase of each wavelet to rotate at each sample in the window. Comparison of the fourth-order cumulant of the whitened trace with fourth-order moment of the all-pass operator in each window yields a cost function which should be minimized. In this regard, we chose simulated annealing method. The proposed method was applied to synthetic and real data set from one of hydrocarbon fields of Iran. The results showed that the proposed method can estimate non-stationary
Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications,
Wavelet algorithms are being developed as an alternative way to determine gene locations in genomic DNA sequences. The preliminary results from the development are presented. The data show the wavelet approach is feasible and better than knowledgedependent approach based on a sample of sequences.
OBJECTIVE: Based on the theoretical basis of Gabor wavelet transformation, the application effects of feature extraction algorithm in Magnetic Resonance Imaging (MRI) and the role of feature extraction algorithm in the diagnosis of lumbar vertebra degenerative diseases were explored. METHOD: The structure of lumbar vertebra and degenerative changes were respectively introduced to clarify the onset mechanism and pathological changes of lumbar vertebra degenerative changes. Most importantly, the theoretical basis of Gabor wavelet transformation and the extraction effect of feature information in lumbar vertebra MRI images were introduced. The differentiation effects of feature information extraction algorithm on annulus fibrosus and nucleus pulposus were analyzed. In this study, the data of lumbar spine MRI was randomly selected from the Wenzhou Lumbar Spine Research Database as research objects. A total of 130 discs were successfully fitted, and 109 images were graded by a doctor after ...
Three relationships, surface electromyography (sEMG) amplitude-force relationship, mean frequency (MF) and force relationship, and the relationship between frequency parameters and signal energy distribution (MF-power relationship) are first investigated. These relationships are nonlinear from both time-domain method and time-frequency analysis method with high regression correlation coefficients R2 values. Based on these relationships, muscle force estimation methods are proposed and evaluated using sEMG signals from healthy subjects and stroke patients. Another force prediction method is developed based on continuous wavelet transform (CWT) and artificial neural networks (ANN) for stroke patients especially. A novel muscle fatigue detection approach is explored using a time-frequency analysis method from sEMG signals during various muscle contraction conditions for both the healthy and stroke subjects. Quantified fatigue levels are obtained indicating the fatigue changes in muscles. These ...
In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. In this paper a new approach based on the threshold value of ECG signal determination is proposed using Wavelet Transform coefficients. Electrocardiography has had a profound influence on the practice of medicine. The electrocardiogram signal contains an important amount of information that can be exploited in different manners. The ECG signal allows for the analysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals are used to verify the proposed method using MATLAB software. Method presented in this paper is compared with the Donohos method for signal denoising meanwhile better results are obtained for ECG signals by the proposed algorithm.
We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.. © 2002 Optical Society of America. Full Article , PDF Article ...
Package with tools for classification of electroencephalography (EEG) data. Feature extraction techniques such as Fourier Transform and Continuous Wavelet Transform (CWT) are available. Support Vector Machines (SVM) can be used to classify the extracted features. An algorithm using Analysis of Variance (ANOVA), False Discovery Rate (FDR), and SVM is available to feature selection. Additionally, the package contains functions to plot data and features.
An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Students two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with without enhanced active segment selection, several potential features and ...
Analytical methods of eye-blinking artefact removal using wavelet transform and Kalman filter are considered. A method for implementing an algorithm for the removal of an eye-blinking artefact using a Matlab software package was presented. The comparison of the operation of algorithms is given.
Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes ...
Noise reduction is an essential step of cDNA microarray image analysis for obtaining better-quality gene expression measurements. Wavelet-based denoising m