WorldWideScience

Sample records for wavelet transform based

  1. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

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

    2016-10-01

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

  2. Electrocardiogram de-noising based on forward wavelet transform ...

    Indian Academy of Sciences (India)

    noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application ...

  3. Research on ghost imaging method based on wavelet transform

    Science.gov (United States)

    Li, Mengying; He, Ruiqing; Chen, Qian; Gu, Guohua; Zhang, Wenwen

    2017-09-01

    We present an algorithm of extracting the wavelet coefficients of object based on ghost imaging (GI) system. Through modification of the projected random patterns by using a series of templates, wavelet transform GI (WTGI) can directly measure the high frequency components of wavelet coefficients without needing the original image. In this study, we theoretically and experimentally perform the high frequency components of wavelet coefficients detection with an arrow and a letter A based on GI and WTGI. Comparing with the traditional method, the use of the algorithm proposed in this paper can significantly improve the quality of the image of wavelet coefficients in both cases. The special advantages of GI will make the wavelet coefficient detection based on WTGI very valuable in real applications.

  4. Electrocardiogram de-noising based on forward wavelet transform ...

    Indian Academy of Sciences (India)

    cation of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each. Bionic Wavelet ... wavelet coefficients obtained from the application of the Bionic Wavelet Transform (BWT) to the noisy ECG signal. ...... Han J Y, Lee S K and Park H B 2009 Denoising ECG using Translation Invariant Multiwavelet. Int. J. Electr.

  5. Color graph based wavelet transform with perceptual information

    Science.gov (United States)

    Malek, Mohamed; Helbert, David; Carré, Philippe

    2015-09-01

    We propose a numerical strategy to define a multiscale analysis for color and multicomponent images based on the representation of data on a graph. Our approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We suggest introducing color dimension into the computation of the weights of the graph and using the geodesic distance as a mean of distance measurement. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. This new representation is illustrated with denoising and inpainting applications. Overall, by introducing psychovisual information in the graph computation for the graph wavelet transform, we obtain very promising results. Thus, results in image restoration highlight the interest of the appropriate use of color information.

  6. Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

    Directory of Open Access Journals (Sweden)

    Mousa Kadhim Wali

    2013-01-01

    Full Text Available We classify the driver distraction level (neutral, low, medium, and high based on different wavelets and classifiers using wireless electroencephalogram (EEG signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS, music player, short message service (SMS, and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5. The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.

  7. Towards discrete wavelet transform-based human activity recognition

    Science.gov (United States)

    Khare, Manish; Jeon, Moongu

    2017-06-01

    Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

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

    Science.gov (United States)

    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.

  9. IMAGE SPLICING DETECTION BASED ON DEMOSAICKING AND WAVELET TRANSFORMATION

    Directory of Open Access Journals (Sweden)

    Endina Putri Purwandari

    2015-03-01

    Full Text Available Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1 demosaicking can be used as a comparison image in forgery detection; (2 the application of demosaicking and wavelet transformation can improve the quality of the input image (3 demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image.

  10. Adaptive Wavelet Transforms

    Energy Technology Data Exchange (ETDEWEB)

    Szu, H.; Hsu, C. [Univ. of Southwestern Louisiana, Lafayette, LA (United States)

    1996-12-31

    Human sensors systems (HSS) may be approximately described as an adaptive or self-learning version of the Wavelet Transforms (WT) that are capable to learn from several input-output associative pairs of suitable transform mother wavelets. Such an Adaptive WT (AWT) is a redundant combination of mother wavelets to either represent or classify inputs.

  11. Identification Method of Mud Shale Fractures Base on Wavelet Transform

    Science.gov (United States)

    Xia, Weixu; Lai, Fuqiang; Luo, Han

    2018-01-01

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

  12. Oil metal particles Detection Algorithm Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Shang Wei

    2017-01-01

    Full Text Available In order to observe the real-time abrasion status of the aero-engine, we need to monitor the lubrication system online. As the aero-engine operating time and running state changes, the concentration, composition, size and other parameters of the metal debris can show different changes. They can be used as an important indicator to reflect the state of the aero-engine fault. However, due to the influence of electromagnetic, vibration disturbance and random noise signal introduced by the processing unit itself, the metal particles signal tend to comprise noise. Oil metal particles detection algorithm based on wavelet transform, utilizes the optimized localized nature in time domain and frequency domain of wavelet transform and the characteristics of multi-resolution analysis, combined with the signal characteristics in actual aero-engine condition to realize noise reduction and detection, while validating the algorithm using real experimental data. The result shows that noise can be effectively decreased and signal characteristics can be detected correctly.

  13. Psychoacoustic Music Analysis Based on the Discrete Wavelet Packet Transform

    Directory of Open Access Journals (Sweden)

    Xing He

    2008-01-01

    Full Text Available Psychoacoustical computational models are necessary for the perceptual processing of acoustic signals and have contributed significantly in the development of highly efficient audio analysis and coding. In this paper, we present an approach for the psychoacoustic analysis of musical signals based on the discrete wavelet packet transform. The proposed method mimics the multiresolution properties of the human ear closer than other techniques and it includes simultaneous and temporal auditory masking. Experimental results show that this method provides better masking capabilities and it reduces the signal-to-masking ratio substantially more than other approaches, without introducing audible distortion. This model can lead to greater audio compression by permitting further bit rate reduction and more secure watermarking by providing greater signal space for information hiding.

  14. A New Quantum Watermarking Based on Quantum Wavelet Transforms

    Science.gov (United States)

    Heidari, Shahrokh; Naseri, Mosayeb; Gheibi, Reza; Baghfalaki, Masoud; Rasoul Pourarian, Mohammad; Farouk, Ahmed

    2017-06-01

    Quantum watermarking is a technique to embed specific information, usually the owner’s identification, into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique. The invisibility of the scheme is examined by the peak-signal-to-noise ratio (PSNR) and the histogram calculation. Furthermore the robustness of the scheme is analyzed by the Bit Error Rate (BER) and the Correlation Two-Dimensional (Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack. Supported by Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

  15. AN EFFICIENT HILBERT AND INTEGER WAVELET TRANSFORM BASED VIDEO WATERMARKING

    Directory of Open Access Journals (Sweden)

    AGILANDEESWARI L.

    2016-03-01

    Full Text Available In this paper, an efficient, highly imperceptible, robust, and secure digital video watermarking technique for content authentication based on Hilbert transform in the Integer Wavelet Transform (IWT domain has been introduced. The Hilbert coefficients of gray watermark image are embedded into the cover video frames Hilbert coefficients on the 2-level IWT decomposed selected block on sub-bands using Principal Component Analysis (PCA technique. The authentication is achieved by using the digital signature mechanism. This mechanism is used to generate and embed a digital signature after embedding the watermarks. Since, the embedding process is done in Hilbert transform domain, the imperceptibility and the robustness of the watermark is greatly improved. At the receiver end, prior to the extraction of watermark, the originality of the content is verified through the authentication test. If the generated and received signature matches, it proves that the received content is original and performs the extraction process, otherwise deny the extraction process due to unauthenticated received content. The proposed method avoids typical degradations in the imperceptibility level of watermarked video in terms of Average Peak Signal – to – Noise Ratio (PSNR value of about 48db, while it is still providing better robustness against common video distortions such as frame dropping, averaging, and various image processing attacks such as noise addition, median filtering, contrast adjustment, and geometrical attacks such as, rotation and cropping in terms of Normalized Correlation Coefficient (NCC value of about nearly 1.

  16. Wavelet sparse transform optimization in image reconstruction based on compressed sensing

    Science.gov (United States)

    Ziran, Wei; Huachuang, Wang; Jianlin, Zhang

    2017-06-01

    The high image sparsity is very important to improve the accuracy of compressed sensing reconstruction image, and the wavelet transform can make the image sparse obviously. This paper is the optimization method based on wavelet sparse transform in image reconstruction based on compressed sensing, and we have designed a restraining matrix to optimize the wavelet sparse transform. Firstly, the wavelet coefficients are obtained by wavelet transform of the original signal data, and the wavelet coefficients have a tendency of decreasing gradually. The restraining matrix is used to restrain the small coefficients and is a part of image sparse transform, so as to make the wavelet coefficients more sparse. When the sampling rate is between 0. 15 and 0. 45, the simulation results show that the quality promotion of the reconstructed image is the best, and the peak signal to noise ratio (PSNR) is increased by about 0.5dB to 1dB. At the same time, it is more obvious to improve the reconstruction accuracy of the fingerprint texture image, which to some extent makes up for the shortcomings that reconstruction of texture image by compressed sensing based on the wavelet transform has the low accuracy.

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

    Science.gov (United States)

    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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  18. Adaptive boxcar/wavelet transform

    Science.gov (United States)

    Sezer, Osman G.; Altunbasak, Yucel

    2009-01-01

    This paper presents a new adaptive Boxcar/Wavelet transform for image compression. Boxcar/Wavelet decomposition emphasizes the idea of average-interpolation representation which uses dyadic averages and their interpolation to explain a special case of biorthogonal wavelet transforms (BWT). This perspective for image compression together with lifting scheme offers the ability to train an optimum 2-D filter set for nonlinear prediction (interpolation) that will adapt to the context around the low-pass wavelet coefficients for reducing energy in the high-pass bands. Moreover, the filters obtained after training is observed to posses directional information with some textural clues that can provide better prediction performance. This work addresses a firrst step towards obtaining this new set of training-based fillters in the context of Boxcar/Wavelet transform. Initial experimental results show better subjective quality performance compared to popular 9/7-tap and 5/3-tap BWTs with comparable results in objective quality.

  19. Region-based image denoising through wavelet and fast discrete curvelet transform

    Science.gov (United States)

    Gu, Yanfeng; Guo, Yan; Liu, Xing; Zhang, Ye

    2008-10-01

    Image denoising always is one of important research topics in the image processing field. In this paper, fast discrete curvelet transform (FDCT) and undecimated wavelet transform (UDWT) are proposed for image denoising. A noisy image is first denoised by FDCT and UDWT separately. The whole image space is then divided into edge region and non-edge regions. After that, wavelet transform is performed on the images denoised by FDCT and UDWT respectively. Finally, the resultant image is fused through using both of edge region wavelet cofficients of the image denoised by FDCT and non-edge region wavelet cofficients of the image denoised by UDWT. The proposed method is validated through numerical experiments conducted on standard test images. The experimental results show that the proposed algorithm outperforms wavelet-based and curvelet-based image denoising methods and preserve linear features well.

  20. Analytic discrete cosine harmonic wavelet transform based OFDM ...

    Indian Academy of Sciences (India)

    Abstract. An OFDM based on Analytic Discrete Cosine Harmonic Wavelet Trans- form (ADCHWT_OFDM) has been proposed in this paper. Analytic DCHWT has been realized by applying DCHWT to the original signal and to its Hilbert trans- form. ADCHWT has been found to be computationally efficient and very effective.

  1. Detection of K-complexes based on the wavelet transform

    DEFF Research Database (Denmark)

    Krohne, Lærke K.; Hansen, Rie B.; Christensen, Julie Anja Engelhard

    2014-01-01

    Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives....... The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability. When testing on data from subjects from the DREAMS© database, a mean true positive rate of 74 % and a positive predictive value of 65 % were achieved. After adjusting a few thresholds to adapt...

  2. Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

    OpenAIRE

    Mousa Kadhim Wali; Murugappan Murugappan; Badlishah Ahmmad

    2013-01-01

    We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS), music player, short message service (SMS), and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was ba...

  3. Wavelet Transform -50 ...

    Indian Academy of Sciences (India)

    University of Hyderabad. His current research interests are in the areas of quantum information ..... The enterprising reader can perform a multi-level decomposition and reconstruction to discover that the problems of overshoots and undershoots plaguing the Fourier transform are absent in discrete wavelet transform. 1kn&(ff.

  4. Super-resolution image restoration algorithms based on orthogonal discrete wavelet transform

    Science.gov (United States)

    Liu, Yang-yang; Jin, Wei-qi

    2005-02-01

    Several new super-resolution image restoration algorithms based on orthogonal discrete wavelet transform are proposed, by using orthogonal discrete wavelet transform and generalized cross validation ,and combining with Luck-Richardson super-resolution image restoration algorithm (LR) and Luck-Richardson algorithm based on Poisson-Markov model (MPML). Orthogonal discrete wavelet transform analyzed in both space and frequency domain has the capability of indicating local features of a signal, and concentrating the signal power to a few coefficients in wavelet transform domain. After an original image is "Symlets" orthogonal discrete wavelet transformed, an asymptotically optimal threshold is determined by minimizing generalized cross validation, and high frequency subbands in each decomposition level are denoised with soft threshold processes to converge respectively to those with maximum signal-noise-ratio, when the method is incorporated with existed super-resolution image algorithms, details of original image, especially of those with low signal-noise-ratio, could be well recovered. Single operation wavelet LR algorithm(SWLR),single operation wavelet MPML algorithm(SW-MPML) and MPML algorithm based on single operation and wavelet transform (MPML- SW) are some operative algorithms proposed based on the method. According to the processing results to simulating and practical images , because of the only one operation, under the guarantee of rapid and effective restoration processing, in comparison with LR and MPML, all the proposed algorithms could retain image details better, and be more suitable to low signal-noise-ratio images, They could also reduce operation time for up to hundreds times of iteratives, as well as, avoid the iterative operation of self-adaptive parameters in MPML, improve operating speed and precision. They are practical and instantaneous to some extent in the field of low signal-noise-ratio image restoration.

  5. Wavelet transforms and their applications

    CERN Document Server

    Debnath, Lokenath

    2015-01-01

    This textbook is an introduction to wavelet transforms and accessible to a larger audience with diverse backgrounds and interests in mathematics, science, and engineering. Emphasis is placed on the logical development of fundamental ideas and systematic treatment of wavelet analysis and its applications to a wide variety of problems as encountered in various interdisciplinary areas. Numerous standard and challenging topics, applications, and exercises are included in this edition, which will stimulate research interest among senior undergraduate and graduate students. The book contains a large number of examples, which are either directly associated with applications or formulated in terms of the mathematical, physical, and engineering context in which wavelet theory arises. Topics and Features of the Second Edition: ·         Expanded and revised the historical introduction by including many new topics such as the fractional Fourier transform, and the construction of wavelet bases in various spaces ...

  6. Multisource remote sensing image fusion based on curvelet and wavelet transform

    Science.gov (United States)

    Xiao, Moyan; He, Zhibiao

    2011-12-01

    Aiming at limitations of existing multiresolution analysis (MRA) fusion methods, this paper proposes a new fusion method which combines curvelet and wavelet transform. Curvelet transform processes edges better than wavelet transform does. While wavelet transform handles smooth area better than curvelet transform does. As an image often includes more than one feature, the proposed method is conducted on the basis of region segmentation and use Àtrous wavelet transform (ATWT) to fuse smooth areas and fast discrete curvelet transform (FDCT) to fuse areas with edges. Furthermore, an optimal objective function defined based on a balance between spectral preservation and spatial resolution improvement is put forward to search optimal segmentation threshold. The optimal fusion result can be obtained by fusion processing through the optimal segmentation threshold. Landsat TM multispectral (MS) images and SPOT Panchromatic (Pan) image covering a region of Wuhan in Hubei province are tested to assess this proposed method. Visual evaluation and statistics analysis are employed to assess the quality of fused images of different methods. The proposed method demonstrates best results among methods being tested in this study. So by combining attributes of both transforms, it is possible to get better image fusion result than by using wavelet and curvelet individually.

  7. De-noising methods for NMR logging echo signals based on wavelet transform

    Science.gov (United States)

    Xie, Ranhong; Wu, Youbin; Liu, Kang; Liu, Mi; Xiao, Lizhi

    2014-06-01

    The signal-to-noise ratio (SNR) of the echo signals in nuclear magnetic resonance (NMR) is one of the most important factors that affect the effective application of NMR logging. Wavelet transform can be used to remove the noise and improve the SNR of echo signals in NMR logging. This paper uses three de-noising methods to treat the NMR echo signals: modulus maxima, spatial correlation and wavelet threshold based on wavelet transform. The effects of the three methods in the noise reduction of NMR echo signals were compared by numerical simulation, core experiment and NMR logging data. The results show that while these three methods can all effectively improve the SNR of NMR echo signals and the NMR T2 inversion results, the most effective among them is the wavelet threshold method, which can obtain a higher SNR and provides more accurate formation porosity.

  8. Target recognition by wavelet transform

    CERN Document Server

    Li Zheng Dong; He Wu Liang; Pei Chun Lan; Peng Wen; SongChen; Zheng Xiao Dong

    2002-01-01

    Wavelet transform has an important character of multi-resolution power, which presents pyramid structure, and this character coincides the way by which people distinguish object from coarse to fineness and from large to tiny. In addition to it, wavelet transform benefits to reducing image noise, simplifying calculation, and embodying target image characteristic point. A method of target recognition by wavelet transform is provided

  9. Sensor fault diagnosis based on discrete wavelet transform and BP neural network

    Science.gov (United States)

    Liu, Quan; Jiang, Xuemei

    2005-11-01

    Sensor technology is one of three major pillars of the modern information technology. With the extensive application of sensor, the dependability of the sensor is paid more and more attention. The development of sensor faults diagnose technology offers strong guarantee for using the sensor reliably. In this paper, the application of combining the wavelet and BP neural networks to sensors failure detection is studied, and a novel diagnosis method based on discrete wavelet transform and BP neural network was proposed to detect and identify sensor abrupt fault. Since wavelet transform can accurately localize sensor signal characteristics both in time and frequency domain, it is very suitable for non-stationary signal analysis. After discrete wavelet transform analysis for sensor output, eigenvector of energy changing rate was extracted, and classification of sensor fault was conducted by using BP neural network. The proposed method does not need construction of sensor model and measurement of sensor input. Hence redundant data can be reduced by omitting some wavelet coefficients and the capability of fault detection can be improved. Sensor fault diagnosis is simulated by the computer. Through a large amount of simulated examples it indicates that the sensors fault diagnosis method based on the theory of wavelet has characteristic such as good sensitivity, high accuracy rate and robust ability to overcome noise. Simulation results proved the effectiveness of this method.

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

    Science.gov (United States)

    Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

    2017-08-01

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

  11. A mathematical model of fluctuation noise based on the wavelet transform

    Directory of Open Access Journals (Sweden)

    Ivan D. Lobanov

    2016-03-01

    Full Text Available A new model of white noise based on the wavelet transform has been proposed. This model is more adequate for solving some radiophysical tasks, such as the problem of electromagnetic waves reflecting from the ionosphere. Moreover, it was shown that in terms of probabilistic description of the random-process trajectories, the wavelet implementation of this random process is more likely (using the probability density functional offered by Amiantov. The wavelet properties and the famous theorems of mathematical analysis and theory of chances were used to develop our model: the mean value theorem and Lyapunov's central limit theorem. Our study resulted in obtaining a theorem on random-process expansion in terms of wavelet basis. It was also shown that the obtained results were in agreement with those of Kotelnikov.

  12. Lecture notes on wavelet transforms

    CERN Document Server

    Debnath, Lokenath

    2017-01-01

    This book provides a systematic exposition of the basic ideas and results of wavelet analysis suitable for mathematicians, scientists, and engineers alike. The primary goal of this text is to show how different types of wavelets can be constructed, illustrate why they are such powerful tools in mathematical analysis, and demonstrate their use in applications. It also develops the required analytical knowledge and skills on the part of the reader, rather than focus on the importance of more abstract formulation with full mathematical rigor.  These notes differs from many textbooks with similar titles in that a major emphasis is placed on the thorough development of the underlying theory before introducing applications and modern topics such as fractional Fourier transforms, windowed canonical transforms, fractional wavelet transforms, fast wavelet transforms, spline wavelets, Daubechies wavelets, harmonic wavelets and non-uniform wavelets. The selection, arrangement, and presentation of the material in these ...

  13. A Time Delay Estimation Method Based on Wavelet Transform and Speech Envelope for Distributed Microphone Arrays

    Directory of Open Access Journals (Sweden)

    YIN, F.

    2013-08-01

    Full Text Available A time delay estimation method based on wavelet transform and speech envelope is proposed for distributed microphone arrays. This method first extracts the speech envelopes of the signals processed with multi-level discrete wavelet transform, and then makes use of the speech envelopes to estimate a coarse time delay. Finally it searches for the accurate time delay near the coarse time delay by the cross-correlation function calculated in time domain. The simulation results illustrate that the proposed method can accurately estimate the time delay between two distributed microphone array signals.

  14. Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Shanshan Chen

    2017-01-01

    Full Text Available Compressed sensing (CS has been applied to accelerate magnetic resonance imaging (MRI for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT with fast iterative shrinkage/soft thresholding algorithm (FISTA to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR, larger signal-to-noise ratio (SNR, better structural similarity index (SSIM, and lower relative error.

  15. Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

    Science.gov (United States)

    Syahputra, M. F.; Situmeang, S. I. G.; Rahmat, R. F.; Budiarto, R.

    2017-04-01

    The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.

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

    Science.gov (United States)

    Wang, Yi; Li, Cunbin; Zhang, Liang

    2017-09-01

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

  17. Study on serum fluorescence spectra based on wavelet transform ...

    African Journals Online (AJOL)

    Serum fluorescence emission intensity is closely related with the excitation wavelength; when the excitation wavelength is 230 nm, the blood lipid concentration and fluorescence intensity was significantly correlated. On the contrary, blood sugar was almost with no effect on the strength. Wavelet analysis was used in signal ...

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

    Science.gov (United States)

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

    2017-01-01

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

  19. Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

    Science.gov (United States)

    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

  20. 3D Scan-Based Wavelet Transform and Quality Control for Video Coding

    Directory of Open Access Journals (Sweden)

    Parisot Christophe

    2003-01-01

    Full Text Available Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during video playback. In this paper, we propose a new temporal scan-based wavelet transform method for video coding combining the advantages of wavelet coding (performance, scalability with acceptable reduced memory requirements, no additional CPU complexity, and avoiding jerks. We also propose an efficient quality allocation procedure to ensure a constant quality over time.

  1. 3D Scan-Based Wavelet Transform and Quality Control for Video Coding

    Science.gov (United States)

    Parisot, Christophe; Antonini, Marc; Barlaud, Michel

    2003-12-01

    Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during video playback. In this paper, we propose a new temporal scan-based wavelet transform method for video coding combining the advantages of wavelet coding (performance, scalability) with acceptable reduced memory requirements, no additional CPU complexity, and avoiding jerks. We also propose an efficient quality allocation procedure to ensure a constant quality over time.

  2. Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG.

    Science.gov (United States)

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

    2017-05-01

    Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important problems have not yet been benefitted from DWT, including epileptic focus localization, a key task in epilepsy diagnosis and treatment. Additionally, the parameters and settings for DWT are chosen empirically or arbitrarily in previous work. In this work, we propose a framework to use DWT and support vector machine (SVM) for epileptic focus localization problem based on EEG. To provide a guideline in selecting the best settings for DWT, we decompose the EEG segments in seven commonly used wavelet families to their maximum theoretical levels. The wavelet and its level of decomposition providing the highest accuracy in each wavelet family are then used in a grid search for obtaining the optimal frequency bands and wavelet coefficient features. Our approach achieves promising performance on two widely-recognized intrancranial EEG datasets that are also seizure-free, with an accuracy of 83.07% on the Bern-Barcelona dataset and an accuracy of 88.00% on the UBonn dataset. Compared with existing DWT-based approaches in epileptic EEG analysis, the proposed approach leads to more accurate and robust results. A guideline for DWT parameter setting is provided at the end of the paper.

  3. A modified undecimated discrete wavelet transform based approach to mammographic image denoising.

    Science.gov (United States)

    Matsuyama, Eri; Tsai, Du-Yih; Lee, Yongbum; Tsurumaki, Masaki; Takahashi, Noriyuki; Watanabe, Haruyuki; Chen, Hsian-Min

    2013-08-01

    In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.

  4. Research on preprocessing method of tractor wheel speed signal based on wavelet transform

    Science.gov (United States)

    Zhili, Zhou; Yao, Liu; Liyou, Xu

    2017-09-01

    As one of the most important parameters in the measurement of tractor slip ratio, wheel speed signal must ensure its accuracy in order to accurately measure the tractor slip ratio. Noises make tractor wheel speed signal a significant fluctuation, which may cause control system failure. Fault points elimination method suitable for tractor wheel speed signal was determined based on the characteristics of tractor wheel speed signal during working process. Meanwhile, soft-hard compromise threshold function wavelet denoising method is designed according to the characteristics of tractor wheel speed signal and trials. We use Carsim simulation software to get actual tractor wheel speed signal, and add white noise which SNR (signal to noise ratio) is 60 to the original signal. From the results of several wavelet denoising methods we can conclude that the soft-hard compromise threshold function wavelet denoising method is better than any other ordinary wavelet denoising methods. The SNR of denoised signal is 56.440 and the MSE (mean square error) is 0.0042. The wavelet transform denoising method is feasible to remove noise from tractor driving wheel speed signal.

  5. Ground roll wave suppression based on wavelet frequency division and radial trace transform

    Science.gov (United States)

    Wang, Wan-Li; Yang, Wu-Yang; Wei, Xin-Jian; He, Xin

    2017-03-01

    Ground roll waves interfere with seismic data. The suppression of ground roll waves based on the division of wavelet frequencies considers the low-frequency characteristics of ground roll waves. However, this method will not be effective when the ground roll wave and the effective signal have the same frequency bands because of overlapping. The radial trace transform (RTT) considers the apparent velocity difference between the effective signal and the ground roll wave to suppress the latter, but affects the low-frequency components of the former. This study proposes a ground roll wave suppression method by combining the wavelet frequency division and the RTT based on the difference between the ground roll wave velocity and the effective signal and their energy difference in the wavelet domain, thus making full use of the advantages of both methods. First, we decompose the seismic data into different frequency bands through wavelet transform. Second, the RTT and low-cut filtering are applied to the low-frequency band, where the ground roll waves are appearing. Third, we reconstruct the seismic record without ground roll waves by using the inverse RTT and the remaining frequency bands. The proposed method not only improves the ground roll wave suppression, but also protects the signal integrity. The numerical simulation and real seismic data processing results suggest that the proposed method has a strong ability to denoise while preserving the amplitude.

  6. Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

    Science.gov (United States)

    Zheng, Yang; Chen, Xihao; Zhu, Rui

    2017-07-01

    Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

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

    Science.gov (United States)

    Li, Hong; Ding, Xue

    2017-03-01

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

  8. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  9. Block-based wavelet transform coding of mammograms with region-adaptive quantization

    Science.gov (United States)

    Moon, Nam Su; Song, Jun S.; Kwon, Musik; Kim, JongHyo; Lee, ChoongWoong

    1998-06-01

    To achieve both high compression ratio and information preserving, it is an efficient way to combine segmentation and lossy compression scheme. Microcalcification in mammogram is one of the most significant sign of early stage of breast cancer. Therefore in coding, detection and segmentation of microcalcification enable us to preserve it well by allocating more bits to it than to other regions. Segmentation of microcalcification is performed both in spatial domain and in wavelet transform domain. Peak error controllable quantization step, which is off-line designed, is suitable for medical image compression. For region-adaptive quantization, block- based wavelet transform coding is adopted and different peak- error-constrained quantizers are applied to blocks according to the segmentation result. In view of preservation of microcalcification, the proposed coding scheme shows better performance than JPEG.

  10. A DNA Structure-Based Bionic Wavelet Transform and Its Application to DNA Sequence Analysis

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2003-01-01

    Full Text Available DNA sequence analysis is of great significance for increasing our understanding of genomic functions. An important task facing us is the exploration of hidden structural information stored in the DNA sequence. This paper introduces a DNA structure-based adaptive wavelet transform (WT – the bionic wavelet transform (BWT – for DNA sequence analysis. The symbolic DNA sequence can be separated into four channels of indicator sequences. An adaptive symbol-to-number mapping, determined from the structural feature of the DNA sequence, was introduced into WT. It can adjust the weight value of each channel to maximise the useful energy distribution of the whole BWT output. The performance of the proposed BWT was examined by analysing synthetic and real DNA sequences. Results show that BWT performs better than traditional WT in presenting greater energy distribution. This new BWT method should be useful for the detection of the latent structural features in future DNA sequence analysis.

  11. EXPERIMENTAL INVESTIGATION FOR FAULT DI AGNOSIS BASED ON FFT AND WAVELET TRANSFORM

    National Research Council Canada - National Science Library

    MIHAIL PRICOP

    2016-01-01

    .... In this paper Fast Fourier Transform (FFT) and Wavelet transform complementary methods are used for fault monitoring of drive belts, analyzing in this way the limitations and advantages of using these methods...

  12. EXPERIMENTAL INVESTIGATION FOR FAULT DIAGNOSIS BASED ON FFT AND WAVELET TRANSFORM

    National Research Council Canada - National Science Library

    Mihail Pricop; Ionut-Cristian Scurtu; Tiberiu Pazara; Codruta Pricop; Dinu Atodiresei

    2016-01-01

    .... In this paper Fast Fourier Transform (FFT) and Wavelet transform complementary methods are used for fault monitoring of drive belts, analyzing in this way the limitations and advantages of using these methods...

  13. Mouse EEG spike detection based on the adapted continuous wavelet transform

    Science.gov (United States)

    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.

  14. [Epileptic EEG signal classification based on wavelet packet transform and multivariate multiscale entropy].

    Science.gov (United States)

    Xu, Yonghong; Li, Xingxing; Zhao, Yong

    2013-10-01

    In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory.

  15. Image Registration Using Redundant Wavelet Transforms

    National Research Council Canada - National Science Library

    Brown, Richard

    2001-01-01

    .... In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency...

  16. Discrete frequency slice wavelet transform

    Science.gov (United States)

    Yan, Zhonghong; Tao, Ting; Jiang, Zhongwei; Wang, Haibin

    2017-11-01

    This paper introduces a new kind of Time-Frequency Representation (TFR) method called Discrete Frequency Slice Wavelet Transform (DFSWT). It is an improved version of Frequency Slice Wavelet Transform (FSWT). The previous researches on FSWT show that it is a new efficient TFR in an easy way without strict limitation as traditional wavelet theory. DFSWT as well as FSWT are defined directly in frequency domain, and still keep its properties in time-frequency domain as FSWT decomposition, reconstruction and filter design, etc. However, the original signal is decomposed and reconstructed on a Chosen Frequency Domains (CFD) as need of application. CFD means that the decomposition and reconstruction are not completed on all frequency components. At first, it is important to discuss the necessary condition of CFD to reconstruct the original signal. And then based on norm l2, an optimization algorithm is introduced to reconstruct the original signal even accurately. Finally, for a test example, the TFR analysis of a real life signal is shown. Some conclusions are drawn that the concept of CFD is very useful to application, and the DFSWT can become a simple and easy tool of TFR method, and also provide a new idea of low speed sampling of high frequency signal in applications.

  17. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

    Science.gov (United States)

    Hu, Qiao; He, Zhengjia; Zhang, Zhousuo; Zi, Yanyang

    2007-02-01

    This paper presents a novel method for fault diagnosis based on an improved wavelet package transform (IWPT), a distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, with investigating the feature of impact fault in vibration signals, a biorthogonal wavelet with impact property is constructed via lifting scheme, and the IWPT is carried out to extract salient frequency-band features from raw vibration signals. Then, the faulty features can be detected by envelope spectrum analysis of wavelet package coefficients of the most salient frequency band. Secondly, with the distance evaluation technique, the optimal features are selected from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band. Finally, the optimal features are input into the SVMs ensemble with AdaBoost algorithm to identify the different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.

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

    Science.gov (United States)

    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.

  19. A new fractional wavelet transform

    Science.gov (United States)

    Dai, Hongzhe; Zheng, Zhibao; Wang, Wei

    2017-03-01

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

  20. A novel algorithm based on wavelet transform for ship target detection in optical remote sensing images

    Science.gov (United States)

    Huang, Bo; Xu, Tingfa; Chen, Sining; Huang, Tingting

    2017-07-01

    The rapid development of the satellite observation technology provides a very rich source of data for sea reconnaissance and ships surveillance. In the face of such a vast sea remote sensing data, it is urgent need to realize the automatic ship detection in optical remote sensing images, but the optical remote sensing images are easily affected by meteorological conditions, such as clouds, waves, which results in larger false alarm; and the weak contrast between optical remote sensing image target and background is easy to cause missing alarm. In this paper, a novel algorithm based on wavelet transform for ship target detection in optical remote sensing images is proposed, which can effectively remove these noise and interference. The segmentation of sea and land background is first applied to the image preprocessing to achieve more accurate detection results, and then discrete wavelet transform is used to deal with the part of sea background. The results show that almost all of the offshore ships can be detected, and through the comparison of the results of four different wavelet basis functions, the accuracy of ship detection is further improved.

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

    Science.gov (United States)

    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.

  2. Wavelet packet transform-based robust video watermarking technique

    Indian Academy of Sciences (India)

    imperceptibility, they used Watson's DCT-based visual model for video watermarking. Ge et al. (2003) have presented a ... accordance with the human visual system and signal characteristics. Hsu & Wu (1998) have proposed a ...... Shannon C E 1949 Communication theory of secrecy system. Bell System Technical Journal ...

  3. Applications of a fast, continuous wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    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.

  4. Wavelet transforms as solutions of partial differential equations

    Energy Technology Data Exchange (ETDEWEB)

    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.

  5. Evaluation of human hand thermal images using wavelet transform based local spatial features - biomed 2013.

    Science.gov (United States)

    Suganthi, S S; Ramakrishnan, S

    2013-01-01

    Transform-based spatial analyses of medical Infrared (IR) images are found to be useful to extract local information, which can be used to identify the abnormalities associated with in region of interest. In this work, human hand infrared images are analyzed by extracting local spatial features using wavelet transform method. The images for this study were acquired using uncooled micro bolometer with focal plane array technology based medical IR camera with dedicated software having high array resolution and spectral response under controlled protocol. The acquired images were decomposed into Intrinsic Mode Functions (IMFs) using bidimensional empirical mode decomposition. Extrema points were detected using eight connected neighbor window method and interpolated using thin plate spline interpolation technique to generate IMFs. The edge information were extracted from local phase of the first IMF. Edges were detected using phase congruency measure by applying Gabor function based wavelet transform. The results showed that it was possible to detect edges from only the first IMF without being influenced by other IMFs. It was further observed that the edge intermittence that arises due to noise component was reduced by treating images with local phase distributions. Hence, it appears that the edge information extraction could enhance the diagnostic relevance of thermal image analysis.

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

    Science.gov (United States)

    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.

  7. EEG classification approach based on the extreme learning machine and wavelet transform.

    Science.gov (United States)

    Yuan, Qi; Zhou, Weidong; Zhang, Jing; Li, Shufang; Cai, Dongmei; Zeng, Yanjun

    2012-04-01

    Automatic detection and classification of electroencephalogram (EEG) epileptic activity aid diagnosis and relieve the heavy workload of doctors. This article presents a new EEG classification approach based on the extreme learning machine (ELM) and wavelet transform (WT). First, the WT is used to extract useful features when certain scales cover abnormal components of the EEG. Second, the ELM algorithm is used to train a single hidden layer of feedforward neural network (SLFN) features. Finally, the SLFN is tested with interictal and ictal EEGs. The experiments demonstrated that the proposed approach achieved a satisfactory classification rate of 99.25% for interictal and ictal EEGs.

  8. A WAVELET TRANSFORM BASED WATERMARKING ALGORITHM FOR PROTECTING COPYRIGHTS OF DIGITAL IMAGES

    Directory of Open Access Journals (Sweden)

    Divya A

    2013-08-01

    Full Text Available This paper proposes an algorithm of Digital Watermarking based on Biorthogonal Wavelet Transform. Digital Watermarking is a technique to protect the copyright of the multimedia data. The position of the watermark can be detected without using the original image by utilizing the correlation between the neighbours of wave co-efficient. The strength of Digital watermark is obtained according to the edge intensities resulting in good robust and Imperceptible. Results show that the proposed watermark algorithm is invisible and has good robustness against common image processing operations.

  9. A novel method for SAR image denoising based on HMT in complex wavelet pocket transform domain

    Science.gov (United States)

    Yan, He; Li, Gang; Fu, You-jia

    2007-12-01

    A novel SAR image denoising scheme based on hidden Markov tree (HMT) in the quad-tree complex wavelet packet transform (QCWPT) domain was presented to achieve the tradeoff between details retainment and noise removal. A neighborhood coefficient differential window was used to compute intra-scale correlations of complex wavelet coefficients in high frequency detail subimage, and intra-scale correlational state was identified according to the smallest error rate Bayesian decision-making rules. A HMT was fitted to describe the correlations between the complex wavelet coefficients across decomposition scales and mark inter-scale correlational state. The product results of corresponding positional intra-scale and inter-scale correlational state were looked as a new hidden state transition probability. A set of iterative equations was developed using the expectation-maximization(EM) algorithm to estimate the model parameters and produce denoising images. Experimental results show that the proposed denoising algorithm is superior to the traditional filtering methods and possible to achieve an excellent balance between suppress speckle noise effectively and preserve as many image details and edges as possible.

  10. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

    Full Text Available 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.

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

    Science.gov (United States)

    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

  12. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Science.gov (United States)

    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 (0detection 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.

  13. Image analysis in unmanned aerial vehicle on-board system for objects detection and recognition with the help of energy characteristics based on wavelet transform

    Science.gov (United States)

    Shleymovich, M. P.; Medvedev, M. V.; Lyasheva, S. A.

    2017-04-01

    In this article the problem of image analysis in unmanned aerial vehicle on-board system for objects detection and recognition with the help of energy characteristics based on wavelet transform is described. The approach of salient points extraction based on wavelet transform is proposed. The salience of the points is substantiated with the energy estimates of their weights. On the basis of wavelet transform salient points extraction the method of image contour segmentation is proposed. For further image recognition the salient points descriptors constructed with the help of wavelet transform are used. The objects detection and recognition system for unmanned aerial vehicles based on proposed methods is simulated using the simulation platform.

  14. Noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage.

    Science.gov (United States)

    Fierro, Massimo; Ha, Ho-Gun; Ha, Yeong-Ho

    2013-05-01

    This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach.

  15. Research on Methods of Infrared and Color Image Fusion Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Zhao Rentao

    2014-06-01

    Full Text Available There is significant difference in the imaging features of infrared image and color image, but their fusion images also have very good complementary information. In this paper, based on the characteristics of infrared image and color image, first of all, wavelet transform is applied to the luminance component of the infrared image and color image. In multi resolution the relevant regional variance is regarded as the activity measure, relevant regional variance ratio as the matching measure, and the fusion image is enhanced in the process of integration, thus getting the fused images by final synthesis module and multi-resolution inverse transform. The experimental results show that the fusion image obtained by the method proposed in this paper is better than the other methods in keeping the useful information of the original infrared image and the color information of the original color image. In addition, the fusion image has stronger adaptability and better visual effect.

  16. Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

    Science.gov (United States)

    Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

    2018-01-25

    Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for

  17. Transformer Protection Using the Wavelet Transform

    OpenAIRE

    ÖZGÖNENEL, Okan; ÖNBİLGİN, Güven; KOCAMAN, Çağrı

    2014-01-01

    This paper introduces a novel approach for power transformer protection algorithm. Power system signals such as current and voltage have traditionally been analysed by the Fast Fourier Transform. This paper aims to prove that the Wavelet Transform is a reliable and computationally efficient tool for distinguishing between the inrush currents and fault currents. The simulated results presented clearly show that the proposed technique for power transformer protection facilitates the a...

  18. Shannon Entropy-Based Wavelet Transform Method for Autonomous Coherent Structure Identification in Fluid Flow Field Data

    Directory of Open Access Journals (Sweden)

    Kartik V. Bulusu

    2015-09-01

    Full Text Available The coherent secondary flow structures (i.e., swirling motions in a curved artery model possess a variety of spatio-temporal morphologies and can be encoded over an infinitely-wide range of wavelet scales. Wavelet analysis was applied to the following vorticity fields: (i a numerically-generated system of Oseen-type vortices for which the theoretical solution is known, used for bench marking and evaluation of the technique; and (ii experimental two-dimensional, particle image velocimetry data. The mother wavelet, a two-dimensional Ricker wavelet, can be dilated to infinitely large or infinitesimally small scales. We approached the problem of coherent structure detection by means of continuous wavelet transform (CWT and decomposition (or Shannon entropy. The main conclusion of this study is that the encoding of coherent secondary flow structures can be achieved by an optimal number of binary digits (or bits corresponding to an optimal wavelet scale. The optimal wavelet-scale search was driven by a decomposition entropy-based algorithmic approach and led to a threshold-free coherent structure detection method. The method presented in this paper was successfully utilized in the detection of secondary flow structures in three clinically-relevant blood flow scenarios involving the curved artery model under a carotid artery-inspired, pulsatile inflow condition. These scenarios were: (i a clean curved artery; (ii stent-implanted curved artery; and (iii an idealized Type IV stent fracture within the curved artery.

  19. New Algorithm For Calculating Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Piotr Lipinski

    2009-04-01

    Full Text Available In this article we introduce a new algorithm for computing Discrete Wavelet Transforms (DWT. The algorithm aims at reducing the number of multiplications, required to compute a DWT. The algorithm is general and can be used to compute a variety of wavelet transform (Daubechies and CDF. Here we focus on CDF 9/7 filters, which are used in JPEG2000 compression standard. We show that the algorithm outperforms convolution-based and lifting-based algorithms in terms of number of multiplications.

  20. The two-dimensional code image recognition based on wavelet transform

    Science.gov (United States)

    Wan, Hao; Peng, Cheng

    2017-01-01

    With the development of information technology, two-dimensional code is more and more widely used. In the technology of two-dimensional code recognition, the noise reduction of the two-dimensional code image is very important. Wavelet transform is applied to the noise reduction of two-dimensional code, and the corresponding Matlab experiment and simulation are made. The results show that the wavelet transform is simple and fast in the noise reduction of two-dimensional code. And it can commendably protect the details of the two-dimensional code image.

  1. Ambiguity attacks on robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2017-12-01

    Full Text Available Among emergent applications of digital watermarking are copyright protection and proof of ownership. Recently, Makbol and Khoo (2013 have proposed for these applications a new robust blind image watermarking scheme based on the redundant discrete wavelet transform (RDWT and the singular value decomposition (SVD. In this paper, we present two ambiguity attacks on this algorithm that have shown that this algorithm fails when used to provide robustness applications like owner identification, proof of ownership, and transaction tracking. Keywords: Ambiguity attack, Image watermarking, Singular value decomposition, Redundant discrete wavelet transform

  2. Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables.

    Science.gov (United States)

    Shekarrizfard, Maryam; Karimi-Jashni, A; Hadad, K

    2012-01-01

    In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN). First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.

  3. Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wu Xiang

    2014-01-01

    Full Text Available 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 multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

  4. Fault detection and analysis of electric generator based on wavelet transform and fuzzy logic technology

    Science.gov (United States)

    Ding, Guangbin; Pang, Peilin

    2008-10-01

    A new method combining wavelet transform with fuzzy theory is proposed to improve the limitation of traditional fault diagnosis technology of electric generator. In order to determine the threshold of each order of wavelet space and the decomposition level adaptively, the statistic rule is brought forward to increase the signal-noise-ratio. The wavelet transform is used to acquire the effective feature components and the proposed fuzzy diagnosis equation is used to complete classify fault pattern. The fault diagnosis model of electric generator is established and the network parameters training are fulfilled by the improved least squares algorithm. The input nodes include the information representing the fault characters. On basis of experiments data to train the fault diagnosis mode, the accurate classification results can be achieved in accordance with expert experience. In view of actual applications, the proposed method can effectively diagnose the fault pattern of electric generator.

  5. An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms.

    Science.gov (United States)

    Madeiro, João P V; Cortez, Paulo C; Marques, João A L; Seisdedos, Carlos R V; Sobrinho, Carlos R M R

    2012-11-01

    The QRS detection and segmentation processes constitute the first stages of a greater process, e.g., electrocardiogram (ECG) feature extraction. Their accuracy is a prerequisite to a satisfactory performance of the P and T wave segmentation, and also to the reliability of the heart rate variability analysis. This work presents an innovative approach of QRS detection and segmentation and the detailed results of the proposed algorithm based on First-Derivative, Hilbert and Wavelet Transforms, adaptive threshold and an approach of surface indicator. The method combines the adaptive threshold, Hilbert and Wavelet Transforms techniques, avoiding the whole ECG signal preprocessing. After each QRS detection, the computation of an indicator related to the area covered by the QRS complex envelope provides the detection of the QRS onset and offset. The QRS detection proposed technique is evaluated based on the well-known MIT-BIH Arrhythmia and QT databases, obtaining the average sensitivity of 99.15% and the positive predictability of 99.18% for the first database, and 99.75% and 99.65%, respectively, for the second one. The QRS segmentation approach is evaluated on the annotated QT database with the average segmentation errors of 2.85±9.90ms and 2.83±12.26ms for QRS onset and offset, respectively. Those results demonstrate the accuracy of the developed algorithm for a wide variety of QRS morphology and the adaptation of the algorithm parameters to the existing QRS morphological variations within a single record. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  7. Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform

    Directory of Open Access Journals (Sweden)

    Maryam Rahmati Garousi

    2016-03-01

    Full Text Available In this paper, a new method for the detection and classification of all types of power quality disturbances is presented. In addition to separating the disturbance signals, the proposed method is able to determine the type of disturbances. Disturbance waveforms are generated based on IEEE 1159 standard and they are de-noised using discrete wavelet transform. To detect the sinusoidal signals from disturbance signals, new criteria have been proposed. By introducing these new criteria, the classification algorithm is not active for non-disturbance signals. Therefore, the computation time is reduced. If a signal has disturbance, to extract the required information, it is analyzed using discrete wavelet transform. Using this information, the appropriate feature vectors are introduced. Parallel neural networks structures are proposed for the classification of disturbances. The inputs of these networks are the introduced feature vectors. The proposed method is done for all power quality disturbances including DC offset, flicker, interrupt, sag, swell, harmonic, notching, impulsive transient, oscillatory transient and eight combinations of these including the harmonics with transient, harmonic with flicker, harmonic with sag, harmonic with swell, sag with flicker, swell with flicker, transient with sag and transient with swell. The performance of this algorithm is compared with a single neural network structure. The results indicate using the parallel neural networks structure, computational time is much reduced and the accuracy of classification of power quality disturbances is significantly increased. Comparison the obtained results by the method with other methods, represents very high performance of the proposed method with precision %99.53.

  8. Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

    Directory of Open Access Journals (Sweden)

    R. Hajiabadi

    2016-10-01

    Full Text Available Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated. Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part and second, high-frequency part (detail will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of

  9. Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

    Directory of Open Access Journals (Sweden)

    Shuo-Tsung Chen

    2015-01-01

    Full Text Available Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  10. A fast method for the detection of vascular structure in images, based on the continuous wavelet transform with the Morlet wavelet having a low central frequency

    Science.gov (United States)

    Postnikov, Eugene B.; Tsoy, Maria O.; Kurochkin, Maxim A.; Postnov, Dmitry E.

    2017-04-01

    A manual measurement of blood vessels diameter is a conventional component of routine visual assessment of microcirculation, say, during optical capillaroscopy. However, many modern optical methods for blood flow measurements demand the reliable procedure for a fully automated detection of vessels and estimation of their diameter that is a challenging task. Specifically, if one measure the velocity of red blood cells by means of laser speckle imaging, then visual measurements become impossible, while the velocity-based estimation has their own limitations. One of promising approaches is based on fast switching of illumination type, but it drastically reduces the observation time, and hence, the achievable quality of images. In the present work we address this problem proposing an alternative method for the processing of noisy images of vascular structure, which extracts the mask denoting locations of vessels, based on the application of the continuous wavelet transform with the Morlet wavelet having small central frequencies. Such a method combines a reasonable accuracy with the possibility of fast direct implementation to images. Discussing the latter, we describe in details a new MATLAB program code realization for the CWT with the Morlet wavelet, which does not use loops completely replaced with element-by-element operations that drastically reduces the computation time.

  11. A Multi-Valued Diagnostic Model Synthesis Based on Descrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Borowczyk Henryk

    2015-01-01

    Full Text Available The method of a multi-valued diagnostic model synthesis using discrete wavelet transform is presented. The method's algorithm consists of three stages: (1 - signal decomposition into low- and high frequency parts - approximations and details, (2 - approximations and details parameterization, (3 - multi-valued encoding parameters obtained in stage 2. The method is illustrated with vibroacoustic signal in real life experiment. The multi-valued diagnostic model is the final result.

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

    Science.gov (United States)

    Das, Sukanta; Ranjan, Vikash

    2017-12-01

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

  13. Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

    Science.gov (United States)

    Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

    2015-01-01

    Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

  14. Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform

    Directory of Open Access Journals (Sweden)

    Xi Qiao

    2017-09-01

    Full Text Available Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable, which cause the underwater image of sea cucumbers to be distorted, blurred, and severely attenuated. Therefore, the valuable information from such an image cannot be fully extracted for further processing. To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers, pre-processing of a sea cucumber image is attracting increasing interest. This paper presents a new method based on contrast limited adaptive histogram equalization and wavelet transform (CLAHE-WT to enhance the sea cucumber image quality. CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution, and WT was used for de-noising based on a soft threshold. Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details. For quantitative analysis, the test with 120 underwater images showed that for the proposed method, the mean square error (MSE, peak signal to noise ratio (PSNR, and entropy were 49.2098, 13.3909, and 6.6815, respectively. The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.

  15. Distinction between myocardial infarction patients with and withouthistory of ventricular tachycardia based on wavelet transformed signal-averaged electrocardiogram

    Directory of Open Access Journals (Sweden)

    Ahmad Keshtkar

    2013-12-01

    Full Text Available Background: There are varieties of electrocardiogram-based methods to predict the risk of Ventricular tachycardia in patients. New extracted features from the signal averaged electrocardiogram and its wavelet coefficient as a distinction’s index are used in this study. Methods: Signals of orthogonal leads from 60 myocardial infarction patients (MI with or without the history of ventricular tachycardia were selected from the national metrology institute of Germany (PTB diagnostic database. They were filtered and the discrete transformed wavelet was exerted on them. New and conventional features introduced in this study were extracted from signal averaged electrocardiogram and its wavelet decompositions. Results: Extracted features: QRS-d, Entropy-w, Maxhist and ZeroC has acceptable statistically criteria (p-value <0.05 for mentioned groups, comparing QRS duration ,in MI patients which is longer than MI + VT, and for other features it is Vice versa. In wavelet decomposition analysis, the entropy feature has higher precision for detection and diagnosing MI and MI+VT. Conclusions: Entropy of wavelet coefficients is a useful feature in distinguishing myocardial infarction patients with or without ventricular tachycardia.

  16. Spatial Verification Using Wavelet Transforms: A Review

    CERN Document Server

    Weniger, Michael; Friederichs, Petra

    2016-01-01

    Due to the emergence of new high resolution numerical weather prediction (NWP) models and the availability of new or more reliable remote sensing data, the importance of efficient spatial verification techniques is growing. Wavelet transforms offer an effective framework to decompose spatial data into separate (and possibly orthogonal) scales and directions. Most wavelet based spatial verification techniques have been developed or refined in the last decade and concentrate on assessing forecast performance (i.e. forecast skill or forecast error) on distinct physical scales. Particularly during the last five years, a significant growth in meteorological applications could be observed. However, a comparison with other scientific fields such as feature detection, image fusion, texture analysis, or facial and biometric recognition, shows that there is still a considerable, currently unused potential to derive useful diagnostic information. In order to tab the full potential of wavelet analysis, we revise the stat...

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

    Science.gov (United States)

    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.

  18. Circadian variation of scalp EEG: a novel measure based on wavelet packet transform and differential entropy.

    Science.gov (United States)

    Zandi, Ali Shahidi; Boudreau, Philippe; Boivin, Diane B; Dumont, Guy A

    2013-01-01

    We propose a novel entropy-based measure to quantify the circadian variations of scalp electroencephalogram (EEG) by analyzing waking epochs of nap opportunities under an ultradian sleep-wake cycle (USW) protocol. To compute this circadian measure for a nap opportunity, each waking epoch (~1 sec) is decomposed using wavelet packet transform and the relative energy for the desired frequency band (here, 10-12 Hz) is calculated. Then, in a bootstrapping procedure, a shape statistic (skewness or kurtosis) of the relative energy distribution, after each resampling, is computed. Finally, the probability density function of this statistic is estimated, and the corresponding differential entropy is considered as the circadian measure. This measure was evaluated using EEG recordings from 4 healthy subjects during a 72-h USW procedure. According to the results, the proposed measure showed a significant circadian variation both for individual and group data, with peak values occurring near the core body temperature minimum. The performance of the entropy-based measure was also compared with that of two other measures, namely mean energy logarithm and mean energy ratio, revealing the superiority of this measure.

  19. Audio Watermarking Scheme Robust against Desynchronization Based on the Dyadic Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Jiwu Huang

    2010-01-01

    Full Text Available Digital watermarking is a technique used to embed an extra piece of information into multimedia signals without degrading the signal quality. For robust audio watermarking, geometrical modifications are common operations and present many challenges because they severely alter the tempo or spectral structures of the audio and thus cause watermark desynchronization. However, most of the existing audio watermarking algorithms lack resynchronization ability due to the nongeometrically-invariant nature of the watermarking domain. In this paper, we consider the dyadic wavelet transform (DYWT for its geometrical invariants which can help resynchronize the watermark. We then design a novel embedding method based on shape modulation which is demonstrated to be robust against many kinds of attack. Based on the knowledge of the insertion, deletion, and substitution (IDS channel, we carefully design a novel error correction coding (ECC with the ability of bit-resynchronization to correct the IDS errors in the watermark. Compared with existing algorithms, our algorithm achieves greater robustness to geometrical modifications and other common operations.

  20. An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients.

    Science.gov (United States)

    Al-Ajlouni, A F; Abo-Zahhad, M; Ahmed, S M; Schilling, R J

    2008-01-01

    Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively.

  1. An Evaluation of the Effects of Wavelet Coefficient Quantization in Transform Based EEG Compression

    Science.gov (United States)

    Higgins, Garry; McGinley, Brian; Jones, Edward; Glavin, Martin

    2016-01-01

    In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantization is an easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, noninvertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantization level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantization and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone. PMID:23668341

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

    Directory of Open Access Journals (Sweden)

    Seyed Mortaza Mousavi

    2014-01-01

    Full Text Available 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.

  3. Electric Equipment Diagnosis based on Wavelet Analysis

    Science.gov (United States)

    Stavitsky, Sergey A.; Palukhin, Nikolay E.; Kobenko, Juri V.; Riabova, Elena S.

    2016-02-01

    Due to electric equipment development and complication it is necessary to have a precise and intense diagnosis. Nowadays there are two basic ways of diagnosis: analog signal processing and digital signal processing. The latter is more preferable. The basic ways of digital signal processing (Fourier transform and Fast Fourier transform) include one of the modern methods based on wavelet transform. This research is dedicated to analyzing characteristic features and advantages of wavelet transform. This article shows the ways of using wavelet analysis and the process of test signal converting. In order to carry out this analysis, computer software Mathcad was used and 2D wavelet spectrum for a complex function was created.

  4. Color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2017-06-01

    Full Text Available This paper puts forward a new color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform for the purpose of removing uncertainty when choosing sub-band coefficients in the smooth regions. Luminance component is the weighted average of the three color channels in the IHS color space and it is not sensitive to noise. According to the characteristics, luminance component was chosen as the measurement to calculate the focus degree. After separating the luminance component and spectrum component, Fisher classification and fuzzy theory were chosen as the fusion rules to conduct the choice of the coefficients after the dual-tree complex wavelet transform. So fusion color image could keep the natural color information as much as possible. This method could solve the problem of color distortion in the traditional algorithms. According to the simulation results, the proposed algorithm obtained better visual effects and objective quantitative indicators.

  5. Study of combined filter based on wavelet transform to denoise stripe images of electronic speckle shearography pattern interferometry

    Science.gov (United States)

    Liu, Zhongling; Jing, Chao; Zhang, Yimo

    2011-11-01

    Stripe images of electronic speckle shearography pattern interferometry, in which stripe distribution are correlated with vertical micro distortion or micro vibration of objects, are severely disturbed by noises, and so denoising stripe images of electronic speckle shearography pattern interferometry is necessary to extract useful stripe distribution information. Denoising methods and flow for stripe images of electronic speckle shearography pattern interferometry are analyzed in this paper to get the stripe distribution correlated with vertical micro distortion or micro vibration of objects. The noises in the stripe images of electronic speckle shearography pattern interferometry are comprised of speckle noise and other random noises induced by environmental disturb and instrumental performance, so it's difficult to use familiar filters, such as mean-value filter, medium-value filter and adaptive filter, etc, to remove all noises in the stripe images. The combined filter composed of mean-value filter and wavelet filter is designed to denoise stripe images. The aim of mean-value filter is to remove random noises induced by environmental disturb and instrumental performance, and then the wavelet filter, in which the Meyer wavelet is adopted, is designed to remove speckle noise in the stripe images. The final stripe distribution images after denoising and binarization are listed to prove the denoising validity of combined filter based on wavelet transform.

  6. An Improved Negative Pressure Wave Method for Natural Gas Pipeline Leak Location Using FBG Based Strain Sensor and Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Qingmin Hou

    2013-01-01

    Full Text Available Methods that more quickly locate leakages in natural gas pipelines are urgently required. In this paper, an improved negative pressure wave method based on FBG based strain sensors and wavelet analysis is proposed. This method takes into account the variation in the negative pressure wave propagation velocity and the gas velocity variation, uses the traditional leak location formula, and employs Compound Simpson and Dichotomy Searching for solving this formula. In addition, a FBG based strain sensor instead of a traditional pressure sensor was developed for detecting the negative pressure wave signal produced by leakage. Unlike traditional sensors, FBG sensors can be installed anywhere along the pipeline, thus leading to high positioning accuracy through more frequent installment of the sensors. Finally, a wavelet transform method was employed to locate the pressure drop points within the FBG signals. Experiment results show good positioning accuracy for natural gas pipeline leakage, using this new method.

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

    Science.gov (United States)

    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.

  8. Image Retrieval Based on Wavelet Features

    Science.gov (United States)

    Murtagh, F.

    2006-04-01

    A dominant (additive, stationary) Gaussian noise component in image data will ensure that wavelet coefficients are of Gaussian distribution, and in such a case Shannon entropy quantifies the wavelet transformed data well. But we find that both Gaussian and long tailed distributions may well hold in practice for wavelet coefficients. We investigate entropy-related features based on different wavelet transforms and the newly developed curvelet transform. Using a materials grading case study, we find that second, third, fourth order moments allow 100% successful test set discrimination.

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

    Science.gov (United States)

    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.

  10. EXPERIMENTAL INVESTIGATION FOR FAULT DI AGNOSIS BASED ON FFT AND WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    MIHAIL PRICOP

    2016-06-01

    Full Text Available Belts are components of the mechanical systems of rotation commonly used for mechanical power transmission and changes in rotational speeds in the shafts. Various failures of the drive belts (foot shear, tooth wear, hollowed teeth, back cracks are common in rotating machines and can cause economic losses. To increase efficiency, reliability and safety of the machines the use of new fault diagnosis techniques of belts, identification and classification is required. In this paper Fast Fourier Transform (FFT and Wavelet transform complementary methods are used for fault monitoring of drive belts, analyzing in this way the limitations and advantages of using these methods. Experimental investigations for the fault diagnosis of drive belts are made using experimental platform and Bruel & Kjaer equipment for measuring vibration and PULSE and MATLAB software for recorded signal processing. The results were analyzed and presented.

  11. QIM blind video watermarking scheme based on Wavelet transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Nisreen I. Yassin

    2014-12-01

    Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.

  12. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine

    Science.gov (United States)

    Li, Ning; Zhou, Rui; Hu, Qinghua; Liu, Xiaohang

    2012-04-01

    This paper investigates the application of the redundant second generation wavelet package transform (RSGWPT), neighborhood rough set (NRS) and support vector machine (SVM) on faulty detection, attribute reduction and pattern classification. On this basis, a novel method for mechanical faulty diagnosis based on RSGWPT, NRS and SVM is presented, which utilizes the RSGWPT to extract faulty feature parameters from the statistical characteristics of wavelet package coefficients to constitute feature vectors, and then makes the attribute reduction by NRS method to obtain the key features, lastly these key features are input into SVM to accomplish faulty pattern classification. The experimental results of the proposed method to fault diagnosis of the gearbox and gasoline engine valve trains show that this method can extract the faulty features, which have better classification ability and at the same time reduce a lot of redundant features in case of assuring the classification accuracy, accordingly improve the classifier efficiency and achieve a better classification performance.

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

    Science.gov (United States)

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

    2017-08-01

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

  14. Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques.

    Science.gov (United States)

    Nguyen-Ky, Tai; Wen, Peng; Li, Yan; Malan, Mel

    2012-06-01

    This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT). The best Daubechies wavelet and their ranking value p are optimally determined to identify different states of anaesthesia. A statistic normalisation process is then carried out to re-scale data and compute the hypnotic depth of anaesthesia. Finally, a new function ZDoA is proposed to compute a DoA index which corresponds one of the five depths of anaesthesia states to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Simulation results based on real anaesthetised EEGs demonstrate that the new index generally parallels the BIS index. In particular, the ZDoA index is often faster than the BIS index to react to the transition period between consciousness and unconsciousness for this data set. A Bland-Altman plot indicates a 95.23% agreement between the ZDoA and BIS indices. The ZDoA trend is responsive, and its movement is consistent with the clinically observed and recorded changes of the patients. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Microbinary element for optical wavelet transform

    Science.gov (United States)

    Huang, Gaogui; Feng, Wenyi; Yan, Yingbai; Jin, Guofan

    1997-09-01

    In order to simplify an opto-electronic hybrid system for texture segmentation based on the multi-channel filtering framework in the human visual theory, a micro-binary optical element (BOE) is designed and fabricated. The BOE has the functions of splitting, filtering and imaging simultaneously. The focal length of the BOE is 150mm and the diameter is 4mm. It contains sixteen Gabor wavelet filters with scales decreased by 2 orders and with our orientations separated every 45 degree, which can be used to perform a nearly complete decomposition with wavelet transform. The relief surface structure with minimum feature scale of 1.5micrometers is fabricated by using the photolithography and ion etching technique. In this paper, the functions of the BOE and the simulation of the filtering are described in detail, the experimental results and improvement of the element are given.

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

    Science.gov (United States)

    Habib Huseni, Gulamhusenwala; Balaji, Ramakrishnan

    2017-10-01

    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.

  17. Fratricide Avoidance Using Transform Domain Techniques: A New Spectral Estimation Method Based on the Evolutionary Wavelet Spectrum Concept

    National Research Council Canada - National Science Library

    Andrian, Jean

    2006-01-01

    ... approach as a natural extension of the Wiener-Kintchine theorem. Moreover, we have the flexibility to replace the wavelets with any time-frequency atoms such as local trigonometric functions or any lapped orthogonal transform (LOT...

  18. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform.

    Science.gov (United States)

    Zandi, Ali Shahidi; Javidan, Manouchehr; Dumont, Guy A; Tafreshi, Reza

    2010-07-01

    A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling approximately 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h(-1) and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

  19. 3D Scan-Based Wavelet Transform and Quality Control for Video Coding

    OpenAIRE

    Parisot Christophe; Antonini Marc; Barlaud Michel

    2003-01-01

    Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during v...

  20. Wavelet transform with fuzzy tuning based indirect field oriented speed control of three-phase induction motor drive

    DEFF Research Database (Denmark)

    Sanjeevikumar, P.; Daya, J.L. Febin; Wheeler, Patrick

    2015-01-01

    This manuscript presents the details about the novel controller using wavelet transform and fuzzy logic tuning for speed control of an induction motor drive. The conventional proportional-integral (PI) speed controller in an indirect vector control of induction motor drive has been replaced...... by the proposed controller for an improved transient and steady state performances. The discrete wavelet transform has been used to decompose the error speed into different frequency components and the fuzzy logic is used to generate the scaling gains of the wavelet controller. The complete model of the proposed...

  1. Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Ying-Yi Hong

    2014-04-01

    Full Text Available Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC. The static switch should have a reliable module implemented in a chip to detect/locate the fault and activate the breaker to open the circuit immediately. This paper proposes a novel approach to design this module in a static switch using the discrete wavelet transform (DWT and adaptive network-based fuzzy inference system (ANFIS. The wavelet coefficient of the fault voltage and the inference results of ANFIS with the wavelet energy of the fault current at the secondary side of the main transformer determine the control action (open or close of a static switch. The ANFIS identifies the faulty zones inside or outside the microgrid. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. This microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. (Montreal, QC, Canada. The proposed method based on DWT and ANFIS is implemented in a field programmable gate array (FPGA by using the Xilinx System Generator. Simulation results reveal that the proposed method is efficient and applicable in the real-time control environment of a power system.

  2. Discretization of quaternionic continuous wavelet transforms

    Science.gov (United States)

    Askari Hemmat, A.; Thirulogasanthar, K.; Krzyżak, A.

    2017-07-01

    A scheme to form a basis and a frame for a Hilbert space of quaternion valued square integrable function from a basis and a frame, respectively, of a Hilbert space of complex valued square integrable functions is introduced. Using the discretization techniques for 2D-continuous wavelet transform of the SIM(2) group, the quaternionic continuous wavelet transform, living in a complex valued Hilbert space of square integrable functions, of the quaternion wavelet group is discretized, and thereby, a discrete frame for quaternion valued Hilbert space of square integrable functions is obtained.

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

    Science.gov (United States)

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

    2017-04-21

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

  4. A Secret Image Sharing Method Using Integer Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Li Ching-Chung

    2007-01-01

    Full Text Available A new image sharing method, based on the reversible integer-to-integer (ITI wavelet transform and Shamir's 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 shadows, and allows recovery of the complete secret image by using any or more shadows . 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.

  5. Tree-structured wavelet transform signature for classification of melanoma

    Science.gov (United States)

    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.

  6. Detection of single-phase CTA occult vessel occlusions in acute ischemic stroke using CT perfusion-based wavelet-transformed angiography

    Energy Technology Data Exchange (ETDEWEB)

    Kunz, Wolfgang G.; Sommer, Wieland H.; Meinel, Felix G.; Ertl-Wagner, Birgit; Thierfelder, Kolja M. [Ludwig-Maximilian-University Hospital Munich, Institute for Clinical Radiology, Munich (Germany); Havla, Lukas; Dietrich, Olaf [Ludwig-Maximilian-University Hospital Munich, Josef Lissner Laboratory for Biomedical Imaging of the Institute for Clinical Radiology, Munich (Germany); Dorn, Franziska [Ludwig-Maximilian-University Hospital Munich, Department of Neuroradiology, Munich (Germany); Buchholz, Grete [Ludwig-Maximilian-University Hospital Munich, Department of Neurology, Munich (Germany)

    2017-06-15

    To determine the detection rate of intracranial vessel occlusions using CT perfusion-based wavelet-transformed angiography (waveletCTA) in acute ischemic stroke patients, in whom single-phase CTA (spCTA) failed to detect an occlusion. Subjects were selected from a cohort of 791 consecutive patients who underwent multiparametric CT including whole-brain CT perfusion. Inclusion criteria were (1) significant cerebral blood flow (CBF) deficit, (2) no evidence of vessel occlusion on spCTA and (3) follow-up-confirmed acute ischemic infarction. waveletCTA was independently analysed by two readers regarding presence and location of vessel occlusions. Logistic regression analysis was performed to identify predictors of waveletCTA-detected occlusions. Fifty-nine patients fulfilled the inclusion criteria. Overall, an occlusion was identified using waveletCTA in 31 (52.5 %) patients with negative spCTA. Out of 47 patients with middle cerebral artery infarction, 27 occlusions (57.4 %) were detected by waveletCTA, mainly located in the M2 (15) and M3 segments (8). The presence of waveletCTA-detected occlusions was associated with larger CBF deficit volumes (odds ratio (OR) = 1.335, p = 0.010) and shorter times from symptom onset (OR = 0.306, p = 0.041). waveletCTA is able to detect spCTA occult vessel occlusions in about half of acute ischemic stroke patients and may potentially identify more patients eligible for endovascular therapy. (orig.)

  7. Applications of a fast continuous wavelet transform

    Science.gov (United States)

    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

  8. Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

    Science.gov (United States)

    Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

    2017-11-01

    Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Recursive Pyramid Algorithm-Based Discrete Wavelet Transform for Reactive Power Measurement in Smart Meters

    Directory of Open Access Journals (Sweden)

    Mahin K. Atiq

    2013-09-01

    Full Text Available Measurement of the active, reactive, and apparent power is one of the most fundamental tasks of smart meters in energy systems. Recently, a number of studies have employed the discrete wavelet transform (DWT for power measurement in smart meters. The most common way to implement DWT is the pyramid algorithm; however, this is not feasible for practical DWT computation because it requires either a log N cascaded filter or O (N word size memory storage for an input signal of the N-point. Both solutions are too expensive for practical applications of smart meters. It is proposed that the recursive pyramid algorithm is more suitable for smart meter implementation because it requires only word size storage of L × Log (N-L, where L is the length of filter. We also investigated the effect of varying different system parameters, such as the sampling rate, dc offset, phase offset, linearity error in current and voltage sensors, analog to digital converter resolution, and number of harmonics in a non-sinusoidal system, on the reactive energy measurement using DWT. The error analysis is depicted in the form of the absolute difference between the measured and the true value of the reactive energy.

  10. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    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.

  11. A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

    Directory of Open Access Journals (Sweden)

    Jujie Wang

    2014-01-01

    Full Text Available 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.

  12. Modified wavelet transform for unbiased frequency representation

    Science.gov (United States)

    Telfer, Brian A.; Szu, Harold H.

    1992-10-01

    A new wavelet transform normalization procedure is proposed for the construction of a weighted bank of matched filters. The standard normalization results in higher input frequencies producing larger wavelet transform magnitudes if the amplitude of the frequencies is held constant, while the new normalization produces equal responses. This is illustrated with an example of Gibb's overshooting phenomenon, and connections to neural networks are discussed. Another example is presented which illustrates a cocktail party effect. A derivation is given to show that an inverse transform still exists when using the new normalization.

  13. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good...... for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  14. Research on Marine Photovoltaic Power Forecasting Based on Wavelet Transform and Echo State Network

    Directory of Open Access Journals (Sweden)

    Xinhui Du

    2017-08-01

    Full Text Available With the rapid development of photovoltaic power generation technology, photovoltaic power generation system has gradually become an important component of the integrated energy system of marine. High precision short-term photovoltaic power generation forecasting is becoming one of the key technologies in ship energy saving and ship energy efficiency improving. Aiming at the characteristics of marine photovoltaic power generation system, we designed a highprecision power forecasting model (WT+ESN for marine photovoltaic power generation system with anti-marine environmental interference. In this model, the information mining of the photovoltaic system in marine environment is carried out based on wavelet theory, then the forecasting model basing on echo state network is construct ed. Lastly, three kinds of error metrics are compared with the three traditional models by Matlab, the result shows that the model has high forecasting accuracy and strong robustness to marine environmental factors, which is of great significance to save fuel for ships, improve the energy utilization rate and assist the power dispatching and fuel dispatching of the marine power generation system.

  15. Electric Equipment Diagnosis based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Stavitsky Sergey A.

    2016-01-01

    Full Text Available Due to electric equipment development and complication it is necessary to have a precise and intense diagnosis. Nowadays there are two basic ways of diagnosis: analog signal processing and digital signal processing. The latter is more preferable. The basic ways of digital signal processing (Fourier transform and Fast Fourier transform include one of the modern methods based on wavelet transform. This research is dedicated to analyzing characteristic features and advantages of wavelet transform. This article shows the ways of using wavelet analysis and the process of test signal converting. In order to carry out this analysis, computer software Mathcad was used and 2D wavelet spectrum for a complex function was created.

  16. Multiresolution signal decomposition transforms, subbands, and wavelets

    CERN Document Server

    Akansu, Ali N; Haddad, Paul R

    2001-01-01

    The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course

  17. A method for surface topography measurement using a new focus function based on dual-tree complex wavelet transform

    Science.gov (United States)

    Li, Shimiao; Guo, Tong; Yuan, Lin; Chen, Jinping

    2018-01-01

    Surface topography measurement is an important tool widely used in many fields to determine the characteristics and functionality of a part or material. Among existing methods for this purpose, the focus variation method has proved high performance particularly in large slope scenarios. However, its performance depends largely on the effectiveness of focus function. This paper presents a method for surface topography measurement using a new focus measurement function based on dual-tree complex wavelet transform. Experiments are conducted on simulated defocused images to prove its high performance in comparison with other traditional approaches. The results showed that the new algorithm has better unimodality and sharpness. The method was also verified by measuring a MEMS micro resonator structure.

  18. A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    AKTAS, M.

    2012-11-01

    Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

  19. Inertial Sensor Signals Denoising with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Ioana-Raluca EDU

    2015-03-01

    Full Text Available In the current paper we propose a new software procedure for processing data from an inertial navigation system boarded on a moving vehicle, in order to achieve accurate navigation information on the displacement of the vehicle in terms of position, speed, acceleration and direction. We divided our research in three phases. In the first phase of our research, we implemented a real-time evaluation criterion with the intention of achieving real-time data from an accelerometer. It is well-known that most errors in the detection of position, velocity and attitude in inertial navigation occur due to difficult numerical integration of noise. In the second phase, we were interested in achieving a better estimation and compensation of the gyro sensor angular speed measurements. The errors of these sensors occur because of their miniaturization, they cannot be eliminated but can be modelled by applying specific signal processing methods. The objective of both studies was to propose a signal processing algorithm, based on Wavelet filter, along with a criterion for evaluating and updating the optimal decomposition level of Wavelet transform for achieving accurate information from inertial sensors. In the third phase of our work we are suggesting the utility of a new complex algorithm for processing data from an inertial measurement unit, containing both miniaturized accelerometers and gyros, after undergoing a series of numerical simulations and after obtaining accurate information on vehicle displacement

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

    Science.gov (United States)

    Huang, Yan; Wang, Zhihui

    2015-12-01

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

  1. Object-oriented Markov random model for classification of high resolution satellite imagery based on wavelet transform

    Science.gov (United States)

    Hong, Liang; Liu, Cun; Yang, Kun; Deng, Ming

    2013-07-01

    The high resolution satellite imagery (HRSI) have higher spatial resolution and less spectrum number, so there are some "object with different spectra, different objects with same spectrum" phenomena. The objective of this paper is to utilize the extracted features of high resolution satellite imagery (HRSI) obtained by the wavelet transform(WT) for segmentation. WT provides the spatial and spectral characteristics of a pixel along with its neighbors. The object-oriented Markov random Model in the wavelet domain is proposed in order to segment high resolution satellite imagery (HRSI). The proposed method is made up of three blocks: (1) WT-based feature extrcation.the aim of extraction of feature using WT for original spectral bands is to exploit the spatial and frequency information of the pixels; (2) over-segmentation object generation. Mean-Shift algorithm is employed to obtain over-segmentation objects; (3) classification based on Object-oriented Markov Random Model. Firstly the object adjacent graph (OAG) can be constructed on the over-segmentation objects. Secondly MRF model is easily defined on the OAG, in which WT-based feature of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. Experiments are conducted on one HRSI dataset-QuickBird images. We evaluate and compare the proposed approach with the well-known commercial software eCognition(object-based analysis approach) and Maximum Likelihood(ML) based pixels. Experimental results show that the proposed the method in this paper obviously outperforms the other methods.

  2. Wavelet Transform-A New Mathematical Microscope

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 3. Wavelet Transform - A New Mathematical Microscope. Sachin P Nanavati Prasanta K Panigrahi. General Article Volume 9 Issue 3 March 2004 pp 50-64. Fulltext. Click here to view fulltext PDF. Permanent link:

  3. Application of wavelet transform in γ-ray spectra analysis

    Science.gov (United States)

    Yu, GuoLiang; Gu, JianZhong; Hou, Long; Li, ZhenYu; Wang, YanZhao; Zhang, YiYun

    2013-09-01

    The frequency distribution of different ingredients in γ-ray spectra, e.g., photo-peak, fluctuations of counts and Compton region, is separately analyzed. After wavelet transform of γ-ray spectra, the wavelet coefficients of a photo-peak increase with transforming scales and these coefficients show direct proportion with intensity of peak at determinate scale. A novel algorithm based on wavelet transform is proposed and studied. The results indicate that most of the photo-peaks in multi-spectra can be determined accurately, the γ-rays energy and intensity of the peak can also be determined. This method has the prospect of being applied in on-line multi-spectra analysis in such fields as radioprotection and nuclear safety monitoring.

  4. ANALYSIS OF EVENT-RELATED POTENTIALS OF EEG SIGNAL USING DISCRETE WAVELET TRANSFORM

    OpenAIRE

    Krotkikh, S. S.; Kirichenko, L. O.

    2012-01-01

    In this work we use discrete wavelet transform for analyzes the frequency structure of EEG signal with evoked potentials after effect of stimulus. The method for determining the response time to a stimulus, based on the evaluation of the wavelet entropy and relative wavelet entropy EEG, has been implemented.

  5. Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform.

    Science.gov (United States)

    Zhou, Jian; Senhadji, Lotfi; Coatrieux, Jean-Louis; Luo, Limin

    2009-02-01

    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.

  6. Nuclear data compression and reconstruction via discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

    1997-12-31

    Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)

  7. Improvement of electrocardiogram by empirical wavelet transform

    Science.gov (United States)

    Chanchang, Vikanda; Kumchaiseemak, Nakorn; Sutthiopad, Malee; Luengviriya, Chaiya

    2017-09-01

    Electrocardiogram (ECG) is a crucial tool in the detection of cardiac arrhythmia. It is also often used in a routine physical exam, especially, for elderly people. This graphical representation of electrical activity of heart is obtained by a measurement of voltage at the skin; therefore, the signal is always contaminated by noise from various sources. For a proper interpretation, the quality of the ECG should be improved by a noise reduction. In this article, we present a study of a noise filtration in the ECG by using an empirical wavelet transform (EWT). Unlike the traditional wavelet method, EWT is adaptive since the frequency spectrum of the ECG is taken into account in the construction of the wavelet basis. We show that the signal-to-noise ratio increases after the noise filtration for different noise artefacts.

  8. Application of Modal Parameter Estimation Methods for Continuous Wavelet Transform-Based Damage Detection for Beam-Like Structures

    Directory of Open Access Journals (Sweden)

    Zhi Qiu

    2015-02-01

    Full Text Available This paper presents a hybrid damage detection method based on continuous wavelet transform (CWT and modal parameter identification techniques for beam-like structures. First, two kinds of mode shape estimation methods, herein referred to as the quadrature peaks picking (QPP and rational fraction polynomial (RFP methods, are used to identify the first four mode shapes of an intact beam-like structure based on the hammer/accelerometer modal experiment. The results are compared and validated using a numerical simulation with ABAQUS software. In order to determine the damage detection effectiveness between the QPP-based method and the RFP-based method when applying the CWT technique, the first two mode shapes calculated by the QPP and RFP methods are analyzed using CWT. The experiment, performed on different damage scenarios involving beam-like structures, shows that, due to the outstanding advantage of the denoising characteristic of the RFP-based (RFP-CWT technique, the RFP-CWT method gives a clearer indication of the damage location than the conventionally used QPP-based (QPP-CWT method. Finally, an overall evaluation of the damage detection is outlined, as the identification results suggest that the newly proposed RFP-CWT method is accurate and reliable in terms of detection of damage locations on beam-like structures.

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

    Science.gov (United States)

    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.

  10. Structural Health Monitoring approach for detecting ice accretion on bridge cable using the Haar Wavelet Transform

    DEFF Research Database (Denmark)

    Andre, Julia; Kiremidjian, Anne; Liao, Yizheng

    2016-01-01

    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 de fined 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...

  11. Application of the wavelet transform for speech processing

    Science.gov (United States)

    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.

  12. Optimization of the Continuous Wavelet Transform for DSP Processor Implementation.

    Science.gov (United States)

    Patil, Sunil; Abel, E

    2005-01-01

    The redundant wavelet transform is an effective tool when emphasis is on the analysis of non-stationary signals and on localization and characterization of singularities. Here we describe an optimized method to implement a B-spline based redundant wavelet transform (RWT) on a Digital Signal Processor (DSP) for integer scales. Expressions are derived to give an exact operation count at any integer scale m for any B-spline of order n. Finally experimental results are given using cubic b-spline as scaling function and first-and second-order derivative of B-splines as wavelets. It has been shown that optimized method improves the execution speed over the standard method by 20-28%.

  13. A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Li Zhang

    2017-12-01

    Full Text Available Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

  14. ECG signal denoising via empirical wavelet transform.

    Science.gov (United States)

    Singh, Omkar; Sunkaria, Ramesh Kumar

    2017-03-01

    This paper presents new methods for baseline wander correction and powerline interference reduction in electrocardiogram (ECG) signals using empirical wavelet transform (EWT). During data acquisition of ECG signal, various noise sources such as powerline interference, baseline wander and muscle artifacts contaminate the information bearing ECG signal. For better analysis and interpretation, the ECG signal must be free of noise. In the present work, a new approach is used to filter baseline wander and power line interference from the ECG signal. The technique utilized is the empirical wavelet transform, which is a new method used to compute the building modes of a given signal. Its performance as a filter is compared to the standard linear filters and empirical mode decomposition.The results show that EWT delivers a better performance.

  15. Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

    Science.gov (United States)

    Eslamizadeh, Gholamhossein; Barati, Ramin

    2017-05-01

    Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Compression of digital hologram for three-dimensional object using Wavelet-Bandelets transform.

    Science.gov (United States)

    Bang, Le Thanh; Ali, Zulfiqar; Quang, Pham Duc; Park, Jae-Hyeung; Kim, Nam

    2011-04-25

    In the transformation based compression algorithms of digital hologram for three-dimensional object, the balance between compression ratio and normalized root mean square (NRMS) error is always the core of algorithm development. The Wavelet transform method is efficient to achieve high compression ratio but NRMS error is also high. In order to solve this issue, we propose a hologram compression method using Wavelet-Bandelets transform. Our simulation and experimental results show that the Wavelet-Bandelets method has a higher compression ratio than Wavelet methods and all the other methods investigated in this paper, while it still maintains low NRMS error.

  17. Wavelet based approach for facial expression recognition

    Directory of Open Access Journals (Sweden)

    Zaenal Abidin

    2015-03-01

    Full Text Available Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4 wavelet and Coiflet (1 wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database.

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

    Science.gov (United States)

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

    2014-03-01

    This paper proposes a novel phase based approach for computing disparity as the optical flow from the given pair of consecutive images. A new dual tree fractional quaternion wavelet transform (FrQWT) is proposed by defining the 2D Fourier spectrum upto a single quadrant. In the proposed FrQWT, each quaternion wavelet consists of a real part (a real DWT wavelet) and three imaginary parts that are organized according to the quaternion algebra. First two FrQWT phases encode the shifts of image features in the absolute horizontal and vertical coordinate system, while the third phase has the texture information. The FrQWT allowed a multi-scale framework for calculating and adjusting local disparities and executing phase unwrapping from coarse to fine scales with linear computational efficiency. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  19. EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform

    National Research Council Canada - National Science Library

    Bhatti, Muhammad

    2001-01-01

    EEG (Electroencephalograph), as a noninvasive testing method, plays a key role in the diagnosing diseases, and is useful for both physiological research and medical applications. Wavelet transform (WT...

  20. Discrete Wavelet Transform-Partial Least Squares Versus Derivative ...

    African Journals Online (AJOL)

    Discrete Wavelet Transform-Partial Least Squares Versus Derivative Ratio Spectrophotometry for Simultaneous Determination of Chlorpheniramine Maleate and Dexamethasone in the Presence of Parabens in Pharmaceutical Dosage Form.

  1. The De-Noising of Sonic Echo Test Data through Wavelet Transform Reconstruction

    Directory of Open Access Journals (Sweden)

    J.N. Watson

    1999-01-01

    Full Text Available This paper presents the results of feasibility study into the application of the wavelet transform signal processing method to sonic based non-destructive testing techniques. Finite element generated data from cast in situ foundation piles were collated and processed using both continuous and discrete wavelet transform techniques. Results were compared with conventional Fourier based methods. The discrete Daubechies wavelets and the continuous Mexican hat wavelet were used and their relative merits investigated. It was found that both the continuous Mexican hat and discrete Daubechies D8 wavelets were significantly better at locating the pile toe compared than the Fourier filtered case. The wavelet transform method was then applied to field test data and found to be successful in facilitating the detection of the pile toe.

  2. Wavelet Transforms of Flickering Light Curves in Cataclysmic Variables

    Science.gov (United States)

    Fritz, T.; Bruch, A.

    The flickering in CVs is composed of a stochastic superposition of flares, causing continuous erratic magnitude variations between a few times $0.01$ mag up to $>1$ mag, depending on the observed system (Bruch 1992). Power spectra based on Fourier transformations rise continuously from high to low frequencies (red noise). However, Fourier techniques -- based on sinusoids as fundamental functions -- are not ideally suited to analyse flickering. Since flickering flares are not sinusoidal, frequencies are smeared out over a large range, making it difficult to detect deviations from smooth distribution functions. Rather than sinusoidal the flare shape is triangular with a symmetrical rise and decline (albeit occasionally with strong deviations; Bruch 1992). Therefore a triangular base function would be more appropriate for analysing the collective properties of the flickering. This suggests that a wavelet-transformation should be well suited for this purpose since its base functions -- the wavelets -- can be chosen to enhance particular structures in the investigated signal. Some known wavelets have shapes well resembling flickering flares. We have applied a discrete wavelet transformation to 843 light curves of 75 CVs. Minimization of the information entropy showed the $C12$ coiflet to be the best suited wavelet for the study of flickering. The use of orthonormal bases enables a mathematically rigorous treatment of noise sources such as Poisson noise and atmospheric scintillation. While the wavelet transformation is a representation of the signal in frequency {\\it and\\/} time, we are not interested in exactly when a flare occurs in a light curve. Therefore, we use the scalegram (Scargle et al.\\ 1993), which is the mean square sum of the wavelet coefficients at a given time scale -- basically the strength of the signal -- as a function of the time scale, to characterize the flickering. The normalized scalegram measures the power of the flickering in relation to the

  3. Detection and characterization of lightning-based sources using continuous wavelet transform: application to audio-magnetotellurics

    Science.gov (United States)

    Larnier, H.; Sailhac, P.; Chambodut, A.

    2018-01-01

    Atmospheric electromagnetic waves created by global lightning activity contain information about electrical processes of the inner and the outer Earth. Large signal-to-noise ratio events are particularly interesting because they convey information about electromagnetic properties along their path. We introduce a new methodology to automatically detect and characterize lightning-based waves using a time-frequency decomposition obtained through the application of continuous wavelet transform. We focus specifically on three types of sources, namely, atmospherics, slow tails and whistlers, that cover the frequency range 10 Hz to 10 kHz. Each wave has distinguishable characteristics in the time-frequency domain due to source shape and dispersion processes. Our methodology allows automatic detection of each type of event in the time-frequency decomposition thanks to their specific signature. Horizontal polarization attributes are also recovered in the time-frequency domain. This procedure is first applied to synthetic extremely low frequency time-series with different signal-to-noise ratios to test for robustness. We then apply it on real data: three stations of audio-magnetotelluric data acquired in Guadeloupe, oversea French territories. Most of analysed atmospherics and slow tails display linear polarization, whereas analysed whistlers are elliptically polarized. The diversity of lightning activity is finally analysed in an audio-magnetotelluric data processing framework, as used in subsurface prospecting, through estimation of the impedance response functions. We show that audio-magnetotelluric processing results depend mainly on the frequency content of electromagnetic waves observed in processed time-series, with an emphasis on the difference between morning and afternoon acquisition. Our new methodology based on the time-frequency signature of lightning-induced electromagnetic waves allows automatic detection and characterization of events in audio

  4. FPGA implement method for two-dimensional integer wavelet transform in the space-based on-orbit image compression system

    Science.gov (United States)

    Wang, Jie; Tian, Yan; Meng, Xiangsheng; Liu, Tong

    2017-02-01

    The image obtained from space-based vision system has increasingly high frame frequency and resolution, and field of view is also growing. Due to the dramatic increase of data scale and the restriction of channel bandwidth between satellite and ground, on-orbit data compression becomes the core of on-satellite data processing. The paper analyzes the new generation static image compression standard JPEG2000 and the key two-dimensional (2D) discrete wavelet transform (DWT) technology. Then an FPGA (Field Programmable Gate Array)implement method for 2D integer wavelet transform is designed. It adopts the spatial combinative lifting algorithm (SCLA), which realizes the simultaneous transformation on rows and columns. On this basis, the paper realizes wavelet decomposition for images with a resolution of 6576*4384 (which is divided into 1024*1024) on the FPGA platform. In particular, the test platform is built in ISE14.7 simulation software, and the device model is xc5vfx100t. The design has passed the FPGA verification. In order to verify the correctness of the algorithm, the results are compared with that obtained by running matlab code. The experimental results show that the design is correct and the resource occupancy rate is low.

  5. Discrete wavelet transform-based investigation into the variability of standardized precipitation index in Northwest China during 1960-2014

    Science.gov (United States)

    Yang, Peng; Xia, Jun; Zhan, Chesheng; Zhang, Yongyong; Hu, Sheng

    2017-02-01

    In this study, the temporal variations of the standard precipitation index (SPI) were analyzed at different scales in Northwest China (NWC). Discrete wavelet transform (DWT) was used in conjunction with the Mann-Kendall (MK) test in this study. This study also investigated the relationships between original precipitation and different periodic components of SPI series with datasets spanning 55 years (1960-2014). The results showed that with the exception of the annual and summer SPI in the Inner Mongolia Inland Rivers Basin (IMIRB), spring SPI in the Qinghai Lake Rivers Basin (QLRB), and spring SPI in the Central Asia Rivers Basin (CARB), it had an increasing trend in other regions for other time series. In the spring, summer, and autumn series, though the MK trends test in most areas was at the insignificant level, they showed an increasing trend in precipitation. Meanwhile, the SPI series in most subbasins of NWC displayed a turning point in 1980-1990, with the significant increasing levels after 2000. Additionally, there was a significant difference between the trend of the original SPI series and the largest approximations. The annual and seasonal SPI series were composed of the short periodicities, which were less than a decade. The MK value would increase by adding the multiple D components (and approximations), and the MK value of the combined series was in harmony with that of the original series. Additionally, the major trend of the annual SPI in NWC was based on the four kinds of climate indices (e.g., Atlantic Oscillation [AO], North Atlantic Oscillation [NAO], Pacific Decadal Oscillation [PDO], and El Nino-Southern Oscillation index [ENSO/NINO]), especially the ENSO.

  6. Wavelet based multicarrier code division multiple access ...

    African Journals Online (AJOL)

    This paper presents the study on Wavelet transform based Multicarrier Code Division Multiple Access (MC-CDMA) system for a downlink wireless channel. The performance of the system is studied for Additive White Gaussian Noise Channel (AWGN) and slowly varying multipath channels. The bit error rate (BER) versus ...

  7. Noise reduction in ultrasonic NDT using undecimated wavelet transforms.

    Science.gov (United States)

    Pardo, E; San Emeterio, J L; Rodriguez, M A; Ramos, A

    2006-12-22

    Translation-invariant wavelet processing is applied to grain noise reduction in ultrasonic non-destructive testing of materials. In particular, the undecimated wavelet transform (UWT), which is essentially a discrete wavelet transform (DWT) that avoids decimation, is used. Two different UWT processors have been specifically developed for that purpose, based on two UWT implementation schemes: the "à trous" algorithm and the cycle-spinning scheme. The performance of these two UWT processors is compared with that of a classical DWT processor, by using synthetic grain noise registers and experimental pulse-echo NDT traces. The synthetic ultrasonic traces have been generated by an own-developed frequency-domain model that includes frequency dependence in both material attenuation and scattering. The experimental ultrasonic traces have been obtained by inspecting a piece of carbon-fiber reinforced plastic composite in which we have mechanized artificial flaws. Decomposition level-dependent thresholds, which are suitable for correlated noise, are specifically determined in all cases. Soft thresholding, Daubechies db6 mother wavelet and the three well-known threshold selection rules, Universal, Minimax and SURE, are applied to the different decomposition levels. The performance of the different de-noising procedures for single echo detection has been comparatively evaluated in terms of signal-to-noise ratio enhancement.

  8. On transforms between Gabor frames and wavelet frames

    DEFF Research Database (Denmark)

    Christensen, Ole; Goh, Say Song

    2013-01-01

    We describe a procedure that enables us to construct dual pairs of wavelet frames from certain dual pairs of Gabor frames. Applying the construction to Gabor frames generated by appropriate exponential Bsplines gives wavelet frames generated by functions whose Fourier transforms are compactly...... supported splines with geometrically distributed knot sequences. There is also a reverse transform, which yields pairs of dual Gabor frames when applied to certain wavelet frames....

  9. Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals

    OpenAIRE

    Mohamed Rizon

    2010-01-01

    Problem statement: The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG). Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral) on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International 10-10 system. We firstly applied Spatial Filtering technique to remove the noises and artif...

  10. Separación ciega de fuentes no-determinada aplicada a mezclas de voz con base en la transformada wavelet discreta Undetermined blind source separation of speech mixtures based on discrete wavelet transform

    Directory of Open Access Journals (Sweden)

    Camilo Andrés Lemus

    2012-12-01

    Full Text Available La separación ciega de fuentes, conocida como BSS por sus siglas en inglés (Blind Source Separation, es una técnica de procesamiento de señales que consiste en estimar fuentes en señales mezcladas linealmente, utilizando métodos como el ICA, para señales fuentes estadísticamente independientes. Uno de los algoritmos BSS más conocidos es el algoritmo JADE, el cual exige que el número de señales independientes coincida con el número de señales observadas (sensores. En situaciones reales, el número de sensores es menor al número de señales fuentes (BSS no-determinado y el problema no tiene solución. En este proyecto se propone una solución para BSS no-determinado, adicionando una etapa de preprocesamiento y una etapa de descomposición basada en la transformada wavelet discreta. Nuestro modelo, el cual hemos denominado DWT+BSS, crea una señal virtual observada a partir de una señal real observada y utiliza los coeficientes wavelet de las señales observadas como entradas al algoritmo clásico JADE. El modelo se valida con señales de voz y audio, obteniendo índices de similitud entre las señales fuentes y las estimadas por encima de 0,7.Blind Source Separation, BSS, is a signal processing technique which estimates sources from linearly mixed signals and it uses methods such as ICA for sources that are statistically independent. Among the best known BSS algorithms is the JADE method, which requires that the number of independent signals match the number of observed signals (sensors. In the real world, the number of sensors is lower than the number of sources (undetermined BSS and therefore the problem has no solution. This work proposes a solution for undetermined BSS by pre-processing and decomposition stages based on the Discrete Wavelet Transform (DWT. Our proposal, which it is known as DWT+BSS, creates a virtual observed signal from a real observed signal and it uses the wavelet coefficients of the observed signals as the

  11. Signal Separation of Helicopter Radar Returns Using Wavelet-Based Sparse Signal Optimisation

    Science.gov (United States)

    2016-10-01

    transforms based on rational sampling factors, in Proc. Wavelet Applications in Industrial Processing. 56 UNCLASSIFIED UNCLASSIFIED DST-Group–RR–0436 [38...separation techniques cannot be applied. A sparse signal representation technique is now proposed for this problem with the tunable Q wavelet transform ...components using state-of-the-art wavelet transforms and sparse signal representation techniques. Wavelet transforms have been used extensively to

  12. Wavelet transform of generalized functions in K′{Mp} spaces

    Indian Academy of Sciences (India)

    Using convolution theory in K { M p } space we obtain bounded results for the wavelet transform. Calderón-type reproducing formula is derived in distribution sense as an application of the same. An inversion formula for the wavelet transform of generalized functions is established.

  13. A Comparative Study of Breast Mass Classification based on Spherical Wavelet Transform using ANN and KNN Classifiers

    Directory of Open Access Journals (Sweden)

    Pelin GÖRGEL

    2012-01-01

    Full Text Available Breast cancer may be missed by the radiologists at the early ages because of the mammography artifacts. Computer aided diagnosis can decrease the mortality rate by providing a second eye. The artifacts exist due to the noise and the inappropriate contrast in mammograms. Therefore a study that classifies the cropped region of interests (ROI’s as benign or malign and provides a second eye to the radiologists is proposed. The study consists of two steps: First step is the application of Spherical Wavelet Transform (SWT to the original ROI matrix prior to feature extraction. Second step is to extract some predetermined pixel and shape features both from wavelet and scaling coefficients. Finally, for classification the prepared feature matrix is given to Artificial Neural Networks (ANN and K-Nearest Neighbour (KNN systems which are widely used in image processing. The algorithms are tested on 60 abnormal digitized mammogram ROIs acquised from The Mammographic Image Analysis Society (MIAS which is a free mammogram database.

  14. Wavelet-frame-based microcalcification detection

    Science.gov (United States)

    Chang, Charles C.; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chui, Charles K.

    1997-10-01

    As the leading cause of death for adult women under 54 years of age in the United States, breast cancer accounts for 29% of all cancers in women. Early diagnosis of breast cancer is the most effective approach to reduce death rate. The rapid climbing of the health care cost further reiterates the importance of cost-effective, accurate screening tools for breast cancer. This paper proposes a wavelet frame based computer algorithm for screening of microcalcifications on digitized mammographical imagery. Despite its simplicity, the discrete wavelet transform (DWT) of compactly supported wavelets has been effectively used for detection of various types of signals. However, the shifting variant property of DWT makes it very unstable for detection of minute microcalcifications. Although increasing the sampling rate will improve the detection probability, this approach will drastically increase the size of mammographical images. The wavelet frame transform can be easily derived from the DWT algorithm by eliminating its down sampling step. The subtle difference between DWT and WF in down sampling is shown to be critical to the accuracy of microcalcifications detection. Without any down sampling, local image information at different scales is preserved. By joint thresholding of wavelet coefficients at different scales, one can accurately pin point suspected microcalcifications. A simple partitioning technique enables the detection algorithm to process image blocks independently. Four different partitioning techniques have been compared, and the method of repeating the end value on each partition boundary has the least significant impact on the detection accuracy.

  15. Application of the cross wavelet transform and wavelet coherence to geophysical time series

    Directory of Open Access Journals (Sweden)

    A. Grinsted

    2004-01-01

    Full Text Available Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/.

  16. A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

    Directory of Open Access Journals (Sweden)

    Debangshu Dey

    2015-03-01

    Full Text Available Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO. The present work explores a Cross-wavelet transform (XWT aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

  17. Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

    Directory of Open Access Journals (Sweden)

    Abhijit Bhattacharyya

    2017-03-01

    Full Text Available This paper analyses the complexity of multivariate electroencephalogram (EEG signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT. In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy

  18. A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform

    Science.gov (United States)

    Luo, Jiesi; Yu, Dejie; Liang, Ming

    2013-05-01

    This paper presents an adaptive demodulation technique for bearing fault detection. It is implemented via the tunable-Q wavelet transform (TQWT). With the TQWT, the bearing vibration signal is decomposed into sub-signals corresponding to different band-pass filters of the TQWT. Kurtosis as an effective indicator of signal impulsiveness is adopted to guide the merging of the sub-signals leading to a signal component which contains information most relevant to the bearing fault. The purpose of the proposed approach is to adaptively search for the best filter for envelope demodulation analysis. In fact, the implementation of the proposed method can be interpreted as the process to obtain the optimal filter for the Hilbert demodulation analysis by two steps of merging of the band-pass filters of the TQWT. The effectiveness of the proposed method has been demonstrated by both simulation and experimental analyses.

  19. Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation

    Directory of Open Access Journals (Sweden)

    Hongyang Deng

    2007-03-01

    Full Text Available The μ-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.

  20. Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation

    Directory of Open Access Journals (Sweden)

    Doroslovački Miloš

    2007-01-01

    Full Text Available The μ-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.

  1. Image Compression using Haar and Modified Haar Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Mohannad Abid Shehab Ahmed

    2013-04-01

    Full Text Available Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5 to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle, and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform. The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R, achieving higher peak signal to noise ratio (PSNR, and the resulting images are of much smoother as compared to standard JPEG especially for high C.R. A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally which approves the highest capability of Modified Haar between others.

  2. Epileptic Seizure Detection based on Wavelet Transform Statistics Map and EMD Method for Hilbert-Huang Spectral Analyzing in Gamma Frequency Band of EEG Signals

    Directory of Open Access Journals (Sweden)

    Morteza Behnam

    2015-08-01

    Full Text Available Seizure detection using brain signal (EEG analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial feature based on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.

  3. Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo

    Energy Technology Data Exchange (ETDEWEB)

    Nakagami, K.; Murayama, R.; Matsuoka, T. [Japan National Oil Corp., Tokyo (Japan)

    1996-05-01

    Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.

  4. Complex Wavelet Based Modulation Analysis

    DEFF Research Database (Denmark)

    Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt

    2008-01-01

    Low-frequency modulation of sound carry important information for speech and music. The modulation spectrum i commonly obtained by spectral analysis of the sole temporal envelopes of the sub-bands out of a time-frequency analysis. Processing in this domain usually creates undesirable distortions...... polynomial trends. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in an alternative transform domain coined as “Modulation Subbands”, this transform shows very promising denoising capabilities and suggests new approaches for joint...

  5. Discrete Fourier and wavelet transforms an introduction through linear algebra with applications to signal processing

    CERN Document Server

    Goodman, Roe W

    2016-01-01

    This textbook for undergraduate mathematics, science, and engineering students introduces the theory and applications of discrete Fourier and wavelet transforms using elementary linear algebra, without assuming prior knowledge of signal processing or advanced analysis.It explains how to use the Fourier matrix to extract frequency information from a digital signal and how to use circulant matrices to emphasize selected frequency ranges. It introduces discrete wavelet transforms for digital signals through the lifting method and illustrates through examples and computer explorations how these transforms are used in signal and image processing. Then the general theory of discrete wavelet transforms is developed via the matrix algebra of two-channel filter banks. Finally, wavelet transforms for analog signals are constructed based on filter bank results already presented, and the mathematical framework of multiresolution analysis is examined.

  6. Introduction to wavelet-based compression of medical images.

    Science.gov (United States)

    Schomer, D F; Elekes, A A; Hazle, J D; Huffman, J C; Thompson, S K; Chui, C K; Murphy, W A

    1998-01-01

    Medical image compression can significantly enhance the performance of picture archiving and communication systems and may be considered an enabling technology for telemedicine. The wavelet transform is a powerful mathematical tool with many unique qualities that are useful for image compression and processing applications. Although wavelet concepts can be traced back to 1910, the mathematics of wavelets have only recently been formalized. By exploiting spatial and spectral information redundancy in images, wavelet-based methods offer significantly better results for compressing medical images than do compression algorithms based on Fourier methods, such as the discrete cosine transform used by the Joint Photographic Experts Group. Furthermore, wavelet-based compression does not suffer from blocking artifacts, and the restored image quality is generally superior at higher compression rates.

  7. Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

    Science.gov (United States)

    Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

    2017-01-01

    People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

  8. A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern

    Directory of Open Access Journals (Sweden)

    Debotosh Bhattacharjee

    2012-01-01

    Full Text Available 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.

  9. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

  10. An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

    Science.gov (United States)

    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.

  11. Wavelet-based associative memory

    Science.gov (United States)

    Jones, Katharine J.

    2004-04-01

    Faces provide important characteristics of a person"s identification. In security checks, face recognition still remains the method in continuous use despite other approaches (i.e. fingerprints, voice recognition, pupil contraction, DNA scanners). With an associative memory, the output data is recalled directly using the input data. This can be achieved with a Nonlinear Holographic Associative Memory (NHAM). This approach can also distinguish between strongly correlated images and images that are partially or totally enclosed by others. Adaptive wavelet lifting has been used for Content-Based Image Retrieval. In this paper, adaptive wavelet lifting will be applied to face recognition to achieve an associative memory.

  12. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    Energy Technology Data Exchange (ETDEWEB)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun [North China Electric Power University, Beijing 102206 (China); Wang, Jianhui [Argonne National Laboratory, Argonne, IL 60439 (United States)

    2010-11-15

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  13. Analysis and removing noise from speech using wavelet transform

    Science.gov (United States)

    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.

  14. Fetal Heart Sounds Detection Using Wavelet Transform and Fractal Dimension

    OpenAIRE

    Elisavet Koutsiana; Hadjileontiadis, Leontios J.; Ioanna Chouvarda; Ahsan H. Khandoker

    2017-01-01

    Phonocardiography is a non-invasive technique for the detection of fetal heart sounds (fHSs). In this study, analysis of fetal phonocardiograph (fPCG) signals, in order to achieve fetal heartbeat segmentation, is proposed. The proposed approach (namely WT–FD) is a wavelet transform (WT)-based method that combines fractal dimension (FD) analysis in the WT domain for the extraction of fHSs from the underlying noise. Its adoption in this field stems from its successful use in the fields of lung ...

  15. A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems.

    Science.gov (United States)

    Liao, Ke; Zhu, Min; Ding, Lei

    2013-08-01

    The present study investigated the use of transform sparseness of cortical current density on human brain surface to improve electroencephalography/magnetoencephalography (EEG/MEG) inverse solutions. Transform sparseness was assessed by evaluating compressibility of cortical current densities in transform domains. To do that, a structure compression method from computer graphics was first adopted to compress cortical surface structure, either regular or irregular, into hierarchical multi-resolution meshes. Then, a new face-based wavelet method based on generated multi-resolution meshes was proposed to compress current density functions defined on cortical surfaces. Twelve cortical surface models were built by three EEG/MEG softwares and their structural compressibility was evaluated and compared by the proposed method. Monte Carlo simulations were implemented to evaluate the performance of the proposed wavelet method in compressing various cortical current density distributions as compared to other two available vertex-based wavelet methods. The present results indicate that the face-based wavelet method can achieve higher transform sparseness than vertex-based wavelet methods. Furthermore, basis functions from the face-based wavelet method have lower coherence against typical EEG and MEG measurement systems than vertex-based wavelet methods. Both high transform sparseness and low coherent measurements suggest that the proposed face-based wavelet method can improve the performance of L1-norm regularized EEG/MEG inverse solutions, which was further demonstrated in simulations and experimental setups using MEG data. Thus, this new transform on complicated cortical structure is promising to significantly advance EEG/MEG inverse source imaging technologies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  16. Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

    Science.gov (United States)

    Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

    2015-12-01

    Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

  17. Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

    Directory of Open Access Journals (Sweden)

    Jérôme Boudy

    2007-01-01

    Full Text Available 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.

  18. Wavelet-based moment invariants for pattern recognition

    Science.gov (United States)

    Chen, Guangyi; Xie, Wenfang

    2011-07-01

    Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.

  19. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  20. A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

    Directory of Open Access Journals (Sweden)

    Abdallah Bengueddoudj

    2017-05-01

    Full Text Available In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT. The fusion of the detail 2D-SMCWT coefficients is performed via a Bayesian Maximum a Posteriori (MAP approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients. For the approximation coefficients, a new fusion rule based on the Principal Component Analysis (PCA is applied. We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against different types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

  1. Modelling spatial density using continuous wavelet transforms

    Indian Academy of Sciences (India)

    Space debris; wavelets; Mexican hat; Laplace distribution; random search; parameter estimation. ... Digital Mapping and Modelling Division, Advanced Data Processing Research Institute, Secunderabad 500 009, India; Department of Mathematics, Osmania University, Hyderabad 500 007, India; Applied Mathematics ...

  2. Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

    Science.gov (United States)

    Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Lee, Shin-Jye; He, Kangjian

    2018-01-01

    In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

  3. A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines.

    Science.gov (United States)

    Khalaf, Aya; Sybeldon, Matthew; Sejdic, Ervin; Akcakaya, Murat

    2018-01-01

    Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow. The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals. The Wilcoxon test and support vector machines (SVM), with a linear kernel, were employed for feature reduction and classification. The experimental results showed that within approximately 3s of the onset of the cognitive task average accuracies of 80.29%, and 82.35% were obtained for the mental rotation versus resting state and the word generation versus resting state respectively. The mental rotation task versus word generation task achieved an average accuracy of 79.72% within 2.24s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68s. The results presented here provide significant improvement compared to the relevant fTCD-based systems presented in literature in terms of accuracy and speed. Specifically, the reported speed in this manuscript is at least 12 and 2.5 times faster than any existing binary and 3-class fTCD-based BCIs, respectively. These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI. Published by Elsevier B.V.

  4. Spectra resolution for simultaneous spectrophotometric determination of lamivudine and zidovudine components in pharmaceutical formulation of human immunodeficiency virus drug based on using continuous wavelet transform and derivative transform techniques.

    Science.gov (United States)

    Sohrabi, Mahmoud Reza; Tayefeh Zarkesh, Mahshid

    2014-05-01

    In the present paper, two spectrophotometric methods based on signal processing are proposed for the simultaneous determination of two components of an anti-HIV drug called lamivudine (LMV) and zidovudine (ZDV). The proposed methods are applied to synthetic binary mixtures and commercial pharmaceutical tablets without the need for any chemical separation procedures. The developed methods are based on the application of Continuous Wavelet Transform (CWT) and Derivative Spectrophotometry (DS) combined with the zero cross point technique. The Daubechies (db5) wavelet family (242 nm) and Dmey wavelet family (236 nm) were found to give the best results under optimum conditions for simultaneous analysis of lamivudine and zidovudine, respectively. In addition, the first derivative absorption spectra were selected for the determination of lamivudine and zidovudine at 266 nm and 248 nm, respectively. Assaying various synthetic mixtures of the components validated the presented methods. Mean recovery values were found to be between 100.31% and 100.2% for CWT and 99.42% and 97.37% for DS, respectively for determination of LMV and ZDV. The results obtained from analyzing the real samples by the proposed methods were compared to the HPLC reference method. One-way ANOVA test at 95% confidence level was applied to the results. The statistical data from comparing the proposed methods with the reference method showed no significant differences. Copyright © 2014 Elsevier B.V. All rights reserved.

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

    Energy Technology Data Exchange (ETDEWEB)

    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

  6. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

    Directory of Open Access Journals (Sweden)

    Hermanus Vermaak

    2016-01-01

    Full Text Available The dual-tree complex wavelet transform (DTCWT solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT. It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG, are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

  7. Human Iris Recognition System using Wavelet Transform and LVQ

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kwan Yong; Lim, Shin Young [Electronics and Telecommunications Research Institute (Korea); Cho, Seong Won [Hongik University (Korea)

    2000-07-01

    The popular methods to check the identity of individuals include passwords and ID cards. These conventional methods for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way. (author). 14 refs., 13 figs., 7 tabs.

  8. Discrete Wavelet Transform Method for High Flux n-γ Discrimination With Liquid Scintillators

    Science.gov (United States)

    Singh, Harleen; Mehra, Rohit

    2017-07-01

    A Novel method based on discrete wavelet transform (DWT) for n-γ discrimination in high radiation flux is presented. We investigated the behavior of higher order wavelets from different families such as the Daubechies family, symlets, and coiflet type wavelets for pulse shape discrimination. A DWT-based average pulse analysis of neutron and γ-ray pulses suggests less sensitivity of db2, db3, sym4, and coif1 wavelets over the widely used Haar wavelet and the charge comparison method for the pile-up events. The DWT method with proposed wavelets is applied to a mixed radiation field at an energy threshold of 500 keVee obtained from an americium-beryllium source exposed to BC501 liquid scintillator which was coupled to a 12-b digital oscilloscope with sampling rate of 2.5 GSamples/s. The proposed wavelets require a short processing gate and are more suitable when applied to high count rate measurement with large fraction of pile-up events in the data set. Furthermore, these wavelets are very stable toward the variation in the width of processing gate in DWT. This feature is very helpful in the optimization of processing gate for the real-time applications.

  9. Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network

    Directory of Open Access Journals (Sweden)

    Sahar Zolfaghari

    2017-12-01

    Full Text Available As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both time-scale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity.

  10. Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation

    Science.gov (United States)

    Solgi, Abazar; Pourhaghi, Amir; Bahmani, Ramin; Zarei, Heidar

    2017-07-01

    An accurate estimation of flow using different models is an issue for water resource researchers. In this study, support vector regression (SVR) and gene expression programming (GEP) models in daily and monthly scale were used in order to simulate Gamasiyab River flow in Nahavand, Iran. The results showed that although the performance of models in daily scale was acceptable and the result of SVR model was a little better, their performance in the daily scale was really better than the monthly scale. Therefore, wavelet transform was used and the main signal of every input was decomposed. Then, by using principal component analysis method, important sub-signals were recognized and used as inputs for the SVR and GEP models to produce wavelet-support vector regression (WSVR) and wavelet-gene expression programming. The results showed that the performance of WSVR was better than the SVR in such a way that the combination of SVR with wavelet could improve the determination coefficient of the model up to 3% and 18% for daily and monthly scales, respectively. Totally, it can be said that the combination of wavelet with SVR is a suitable tool for the prediction of Gamasiyab River flow in both daily and monthly scales.

  11. Doppler radar fall activity detection using the wavelet transform.

    Science.gov (United States)

    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.

  12. Short Exon Detection via Wavelet Transform Modulus Maxima.

    Science.gov (United States)

    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.

  13. Short Exon Detection via Wavelet Transform Modulus Maxima.

    Directory of Open Access Journals (Sweden)

    Xiaolei Zhang

    Full Text Available 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.

  14. Time-frequency analysis with the continuous wavelet transform

    Science.gov (United States)

    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.

  15. Comparison of a discrete wavelet transform method and a modified undecimated discrete wavelet transform method for denoising of mammograms.

    Science.gov (United States)

    Matsuyama, Eri; Tsai, Du-Yih; Lee, Yongbum; Takahashi, Noriyuki

    2013-01-01

    The purpose of this study was to evaluate the performance of a conventional discrete wavelet transform (DWT) method and a modified undecimated discrete wavelet transform (M-UDWT) method applied to mammographic image denoising. Mutual information, mean square error, and signal to noise ratio were used as image quality measures of images processed by the two methods. We examined the performance of the two methods with visual perceptual evaluation. A two-tailed F test was used to measure statistical significance. The difference between the M-UDWT processed images and the conventional DWT-method processed images was statistically significant (Pimage quality as compared to the conventional DWT.

  16. Comparison of Fourier transform and continuous wavelet transform to study echo-planar imaging flow maps

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez G, A.; Bowtell, R.; Mansfield, P. [Area de Procesamiento Digital de Senales e Imagenes Biomedicas. Universidad Autonoma Metropolitana Iztapalapa. Mexico D.F. 09340 Mexico (Mexico)

    1998-12-31

    Velocity maps were studied combining Doyle and Mansfield method (1986) with each of the following transforms: Fourier, window Fourier and wavelet (Mexican hat). Continuous wavelet transform was compared against the two Fourier transform to determine which technique is best suited to study blood maps generated by Half Fourier Echo-Planar Imaging. Coefficient images were calculated and plots of the pixel intensity variation are presented. Finally, contour maps are shown to visualize the behavior of the blood flow in the cardiac chambers for the wavelet technique. (Author)

  17. Wavelet Transforms: Application to Data Analysis-I

    Indian Academy of Sciences (India)

    ... Lecture Workshops · Refresher Courses · Symposia. Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 11. Wavelet Transforms: Application to Data Analysis – I. Jatan K Modi Sachin P Nanavati Amit S Phadke Prasanta K Panigrahi. General Article Volume 9 Issue 11 November 2004 pp 10-22 ...

  18. Wavelet Transforms: Application to Data Analysis–II

    Indian Academy of Sciences (India)

    ... Lecture Workshops · Refresher Courses · Symposia. Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 12. Wavelet Transforms: Application to Data Analysis – II. Jatan K Modi Sachin P Nanavati Amit S Phadke Prasanta K Panigrahi. General Article Volume 9 Issue 12 December 2004 pp 8-13 ...

  19. Discrete wavelet transforms over finite sets which are translation invariant

    NARCIS (Netherlands)

    L. Kamstra

    2001-01-01

    textabstractThe discrete wavelet transform was originally a linear operator that works on signals that are modeled as functions from the integers into the real or complex numbers. However, many signals have discrete function values. This paper builds on two recent developments: the extension of

  20. Wavelet Transformation for Damage Identication in Wind Turbine Blades

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Skov, Jonas falk; Kirkegaard, Poul Henning

    2014-01-01

    -damage mode shapes are derived through modal analysis and subsequently analyzed with continuous two-dimensional wavelet transformation for damage identification, namely detection, localization and assessment. It is found that valid damage identification is obtained even when utilizing the mode shape...

  1. A High Speed Architecture for Lifting-based 2-D Cohen-Daubechies-Feauveau (5,3 Discrete Wavelet Transform used in JPEG2000

    Directory of Open Access Journals (Sweden)

    Mohammad Rafi Lone

    2017-03-01

    Full Text Available For real-time applications, efficient VLSI implementation of DWT is desired. In this paper, DWT architecture based on retiming for pipelining and unfolding is presented. The architecture is based on lifting one-dimensional Cohen-Daubechies-Feauveau (CDF (5,3 wavelet filter, which is easily extended to 2-D implementation. It consists of low complexity and easily repeatable components. This paper is focused on the critical path minimization and throughput optimization at the same time. The architecture has been implemented on Virtex 6 Xilinx FPGA platform. The implementation results show that the critical path is minimized four to five times, while throughput is doubled, making the overall architecture approximately ten times faster when compared with the conventional lifting-based DWT architecture. Further with parallel implementation, the throughput has doubled without any increase in number of row buffers, implying that the architecture is memory efficient as well. The even and odd rows of the image are scanned in parallel fashion. To perform the 2-D DWT transform of an image of size 15 Megapixels, it takes 16.86 ms, which implies 59 images of that size can be processed in one second. This can be utilized for real-time video processing applications even for high resolution videos.

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

    Directory of Open Access Journals (Sweden)

    Yong Yang

    2014-11-01

    Full Text Available This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT. The Sum-Modified-Laplacian (SML-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

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

    Science.gov (United States)

    Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Sayadi Omid

    2007-01-01

    Full Text Available 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 -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.

  5. ERG signal analysis using wavelet transform.

    Science.gov (United States)

    Barraco, R; Persano Adorno, D; Brai, M

    2011-09-01

    The wavelet analysis is a powerful tool for analyzing and detecting features of signals characterized by time-dependent statistical properties, as biomedical signals. The identification and the analysis of the components of these signals in the time-frequency domain, give meaningful information about the physiological mechanisms that govern them. This article presents the results of the wavelet analysis applied to the a-wave component of the human electroretinogram. In order to deepen and improve our knowledge about the behavior of the early photoreceptoral response, including the possible activation of interactions and correlations among the photoreceptors, we have detected and identified the stable time-frequency components of the a-wave, using six representative values of luminance. The results indicate the occurrence of three frequencies lying in the range 20-200 Hz. The lowest one is attributed to the summed activities of the photoreceptors. The others are weaker and at low luminance one of them does not occur. We relate them to the response of the rods and the cones whose aggregate activities are non-linear and typically exhibit self-organization under selective stimuli. The identification of the stable frequency components and of their times of occurrence helps us to shine light about the complex mechanisms governing the a-wave. The present results are promising toward the assessment of more refined model concerning the photoreceptoral activities.

  6. Electroencephalography data analysis by using discrete wavelet packet transform

    Science.gov (United States)

    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.

  7. Method for Optimal Sensor Deployment on 3D Terrains Utilizing a Steady State Genetic Algorithm with a Guided Walk Mutation Operator Based on the Wavelet Transform

    Science.gov (United States)

    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

  8. Signal reconstruction of surface waves on SASW measurement using Gaussian Derivative wavelet transform

    Science.gov (United States)

    Rosyidi, Sri; Taha, Mohd; Chik, Zamri; Ismail, Amiruddin

    2009-09-01

    Surface wave method consists of measurement and processing of the dispersive Rayleigh waves recorded from two or more vertical transducers. The dispersive phase data are inverted and the shear wave velocity versus depth is obtained. However, in case of residual soil, the reliable phase spectrum curve is difficult to be produced. Noises from nature and other human-made sources disturb the generated surface wave data. In this paper, a continuous wavelet transform based on mother wavelet of Gaussian Derivative was used to analyze seismic waves in different frequency and time. Time-frequency wavelet spectrum was employed to localize the interested seismic response spectrum of generated surface waves. It can also distinguish the fundamental mode of the surface wave from the higher modes of reflected body waves. The results presented in this paper showed that the wavelet analysis is able to determine reliable surface wave spectrum of sandy clayey residual soil.

  9. Inverse problem in archeological magnetic surveys using complex wavelet transform.

    Science.gov (United States)

    Saracco, G.; Moreau, F.; Mathe, P. E.; Hermitte, D.

    2003-04-01

    The wavelet transform applied to potential fields (electric, magnetic, or gravimetric, ...) has been now used from several years in geophysical applications, in particular to define the depth of potentiel sources verifying Poisson equation and responsible for potential anomalies measured at the ground surface. The complex continuous wavelet transform (CCWT) has been described, but the phase has not yet been exploited. (For these kinds of problem we construct a complex analyzing wavelet by Hilbert transforms of the Poisson or derivative of the Poisson wavelet which is real by definition). We show, here, that the phase of the CCWT provides useful information on the geometric and total magnetic inclination of the potential sources, as the modulus allows to characterize their depth and heterogenety degree. Regarding the properties of the phase compared to the modulus, it is more stable in presence of noise and we can defined it, independantly of the low level of energy of the signal. In this sense, information carried by the phase is more efficient to detect small objects or to separate close sources. We have applied a multi-scale analysis on magnetic measurements providing from a cesium magnetometer on the Fox-Amphoux site (France), to detect and localize buried structures like antik ovens. Conjointly, a rock magnetic study including susceptibility and magnetisations (induced or remanent) measurements give a better constrain on the magnetic parameters we want to extract.

  10. Influences of the signal border extension in the discrete wavelet transform in EEG spike detection

    Directory of Open Access Journals (Sweden)

    Edras Reily Pacola

    Full Text Available Abstract Introduction The discrete wavelet transform is used in many studies as signal preprocessor for EEG spike detection. An inherent process of this mathematical tool is the recursive wavelet convolution over the signal that is decomposed into detail and approximation coefficients. To perform these convolutions, firstly it is necessary to extend signal borders. The selection of an unsuitable border extension algorithm may increase the false positive rate of an EEG spike detector. Methods In this study we analyzed nine different border extensions used for convolution and 19 mother wavelets commonly seen in other EEG spike detectors in the literature. Results The border extension may degrade an EEG spike detector up to 44.11%. Furthermore, results behave differently for distinct number of wavelet coefficients. Conclusion There is not a best border extension to be used with any EEG spike detector based on the discrete wavelet transform, but the selection of the most adequate border extension is related to the number of coefficients of a mother wavelet.

  11. Wavelet filtered shifted phase-encoded joint transform correlation for face recognition

    Science.gov (United States)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2017-05-01

    A new wavelet-filtered-based Shifted- phase-encoded Joint Transform Correlation (WPJTC) technique has been proposed for efficient face recognition. The proposed technique uses discrete wavelet decomposition for preprocessing and can effectively accommodate various 3D facial distortions, effects of noise, and illumination variations. After analyzing different forms of wavelet basis functions, an optimal method has been proposed by considering the discrimination capability and processing speed as performance trade-offs. The proposed technique yields better correlation discrimination compared to alternate pattern recognition techniques such as phase-shifted phase-encoded fringe-adjusted joint transform correlator. The performance of the proposed WPJTC has been tested using the Yale facial database and extended Yale facial database under different environments such as illumination variation, noise, and 3D changes in facial expressions. Test results show that the proposed WPJTC yields better performance compared to alternate JTC based face recognition techniques.

  12. Evaluating Interpersonal Synchrony: Wavelet Transform Toward an Unstructured Conversation.

    Science.gov (United States)

    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.

  13. Fast Frequency Estimation by Zero Crossings of Differential Spline Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Chen Jie

    2005-01-01

    Full Text Available Zero crossings or extrema of a wavelet transform constitute important signatures for signal analysis with the advantage of great simplicity. In this paper, we introduce a fast frequency-estimation method based on zero-crossing counting in the transform domain of a family of differential spline wavelets. The resolution and order of the vanishing moments of the chosen wavelets have a close relation with the frequency components of a signal. Theoretical results on estimating the highest and the lowest frequency components are derived, which are particularly useful for frequency estimation of harmonic signals. The results are illustrated with the help of several numerical examples. Finally, we discuss the connection of this approach with other frequency estimation methods, with the high-order level-crossing analysis in statistics, and with the scaling theorem in computer vision.

  14. UV spectrophotometric simultaneous determination of cefoperazone and sulbactam in pharmaceutical formulations by derivative, Fourier and wavelet transforms.

    Science.gov (United States)

    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. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Wavelet based denoising of power quality events for characterization

    African Journals Online (AJOL)

    The effectiveness of wavelet transform (WT) methods for analyzing different power quality (PQ) events with or without noise has been demonstrated in this paper. Multi-resolution signal decomposition based on discrete WT is used to localize and to classify different power quality disturbances. The energy distribution at ...

  16. Wavelet based denoising of power quality events for characterization

    African Journals Online (AJOL)

    user

    Angrisani L., Daponte P., D'Apuuo M. and Testa A., 1996, A new wavelet transform based procedure for electrical power quality analysis, Proceedings of the International Conference on Harmonics and Quality of Power (ICHQP), Las Vegas, Nevada,. USA, pp. 608-614. Bollen Math H.J., 2000, Understanding power quality ...

  17. Efficient algorithms for discrete wavelet transform with applications to denoising and fuzzy inference systems

    CERN Document Server

    Shukla, K K

    2013-01-01

    Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated

  18. Assessment of the Wavelet Transform for Noise Reduction in Simulated PET Images

    Directory of Open Access Journals (Sweden)

    Bahareh Shalchian

    2009-06-01

    Full Text Available Introduction: An efficient method of tomographic imaging in nuclear medicine is positron emission tomography (PET. Compared to SPECT, PET has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. However, high noise levels in the image limit its diagnostic utility. Noise removal in nuclear medicine is traditionally based on Fourier decomposition of images. This method is based on frequency components, irrespective of the spatial location of the noise or signal. The wavelet transform presents a solution by providing information on the frequency content while retaining spatial information. This alleviates the shortcoming of the Fourier transform and thus, wavelet transform has been extensively used for noise reduction, edge detection and compression. Materials and Methods: In this research, we used the SimSET software to simulate PET images of the NCAT phantom. The images were acquired using 250 million counts in a 128×128 matrix. For the reference image, we acquired an image with high counts (6 billion. Then, we reconstructed these images using our own software developed in MATLAB. After image reconstruction, a 250 million counts image (noisy image and a reference image were normalized and then root-mean-square error (RMSE was used to compare the images. Next, we wrote and applied de-noising programs. These programs were based on using 54 different wavelets and 4 methods. De-noised images were compared with the reference image using RMSE. Results: Our results indicate that the Stationary Wavelet Transform and Global Thresholding are more efficient at noise reduction compared to the other methods that we investigated. Discussion: The wavelet transform is a useful method for de-noising of simulated PET images. Noise reduction using this transform and loss of high-frequency information are simultaneous with each other. It seems that we should attend to the mutual agreement between noise reduction and

  19. Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach

    Science.gov (United States)

    Aloui, Chaker; Jammazi, Rania

    2015-10-01

    In this article, we propose a wavelet-based approach to accommodate the stylized facts and complex structure of financial data, caused by frequent and abrupt changes of markets and noises. Specifically, we show how the combination of both continuous and discrete wavelet transforms with traditional financial models helps improve portfolio's market risk assessment. In the empirical stage, three wavelet-based models (wavelet-EGARCH with dynamic conditional correlations, wavelet-copula, and wavelet-extreme value) are considered and applied to crude oil price and US dollar exchange rate data. Our findings show that the wavelet-based approach provides an effective and powerful tool for detecting extreme moments and improving the accuracy of VaR and Expected Shortfall estimates of oil-exchange rate portfolios after noise is removed from the original data.

  20. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  1. Determination of endpoint of procedure for radix rehmanniae steamed based on ultraviolet spectrophotometry combination with continuous wavelet transform and kernel independent component analysis.

    Science.gov (United States)

    Wang, Guoqing; Hou, Zhenyu; Tang, Yanxia; Zhao, Jianbo; Sun, Yu'an; Dong, Chunhong; Fu, Dexue

    2010-10-29

    A method for determination of the endpoint of the procedure for radix rehmanniae steamed was proposed based on UV spectrophotometry combination with continuous wavelet transform and kernel independent component analysis (UV-CWT-KICA). In the proposed method, the raw UV spectra of the rehmanniae samples during steamed procedure were measured. The raw UV spectral data were firstly pretreated by CWT for elimination of the noise signal and enrichment of the spectral resolution, then the independent components (ICs) were estimated from the mixed CWT coefficient matrix. The results show that the ICs are chemical significance with their relative concentrations gradually decreasing or increasing during the first steamed period, and the endpoint of the steamed procedure can be determined by inspection of the relative concentration profiles, at which the ICs should be approached maximum or minimum. Furthermore, the estimated ICs of rehmanniae samples from different areas or with different grades are similar, and the relative concentration of the similar ICs in different groups are increasing or decreasing before the first 14 h, and nearly steady or some decreasing after 16 h. Based on the variations of the relative concentration profiles of the ICs, the endpoint of the steamed procedure can be determined as 15 h, while that determined by sensory analysis is 14-20 h. The proposed UV-CWT-KICA method can avoid the higher deviations of the endpoints that were determined by sensory analysis. It provides an alternative approach for determination of the endpoint of the procedure for processing traditional Chinese medicine (TCM). Copyright © 2010 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  4. Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection

    Science.gov (United States)

    Yang, Huijuan; Guan, Cuntai; Sui Geok Chua, Karen; San Chok, See; Wang, Chuan Chu; Kok Soon, Phua; Tang, Christina Ka Yin; Keng Ang, Kai

    2014-06-01

    Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. Significance. These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

  5. Real time continuous wavelet transform implementation on a DSP processor.

    Science.gov (United States)

    Patil, S; Abel, E W

    2009-01-01

    The continuous wavelet transform (CWT) is an effective tool when the emphasis is on the analysis of non-stationary signals and on localization and characterization of singularities in signals. We have used the B-spline based CWT, the Lipschitz Exponent (LE) and measures derived from it to detect and quantify the singularity characteristics of biomedical signals. In this article, a real-time implementation of a B-spline based CWT on a digital signal processor is presented, with the aim of providing quantitative information about the signal to a clinician as it is being recorded. A recursive algorithm implementation was shown to be too slow for real-time implementation so a parallel algorithm was considered. The use of a parallel algorithm involves redundancy in calculations at the boundary points. An optimization of numerical computation to remove redundancy in calculation was carried out. A formula has been derived to give an exact operation count for any integer scale m and any B-spline of order n (for the case where n is odd) to calculate the CWT for both the original and the optimized parallel methods. Experimental results show that the optimized method is 20-28% faster than the original method. As an example of applying this optimized method, a real-time implementation of the CWT with LE postprocessing has been achieved for an EMG Interference Pattern signal sampled at 50 kHz.

  6. Spike separation from EEG/MEG data using morphological filter and wavelet transform.

    Science.gov (United States)

    Jia, Wenyan; Sclabassi, Robert J; Pon, Lin-Sen; Scheuer, Mark L; Sun, Mingui

    2006-01-01

    In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.

  7. A Variation on Uncertainty Principle and Logarithmic Uncertainty Principle for Continuous Quaternion Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Mawardi Bahri

    2017-01-01

    Full Text Available The continuous quaternion wavelet transform (CQWT is a generalization of the classical continuous wavelet transform within the context of quaternion algebra. First of all, we show that the directional quaternion Fourier transform (QFT uncertainty principle can be obtained using the component-wise QFT uncertainty principle. Based on this method, the directional QFT uncertainty principle using representation of polar coordinate form is easily derived. We derive a variation on uncertainty principle related to the QFT. We state that the CQWT of a quaternion function can be written in terms of the QFT and obtain a variation on uncertainty principle related to the CQWT. Finally, we apply the extended uncertainty principles and properties of the CQWT to establish logarithmic uncertainty principles related to generalized transform.

  8. An Adaptive Inpainting Algorithm Based on DCT Induced Wavelet Regularization

    Science.gov (United States)

    2013-01-01

    JOURNAL ARTICLE (Postprint) 3. DATES COVERED (From - To) JAN 2012 – JAN 2013 4. TITLE AND SUBTITLE AN ADAPTIVE INPAINTING ALGORITHM BASED ON...image inpainting optimization model whose objective functional is a smoothed ℓ1 norm of the weighted non-decimated discrete cosine transform (DCT...analysis. Non-decimated wavelet transforms with these filters are explored to analyze images to be inpainted . Our numerical experiments verify that

  9. WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

    Directory of Open Access Journals (Sweden)

    Li Song

    2010-04-01

    Full Text Available Abstract Background Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results We developed a novel discrete wavelet transform (DWT and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP, a commonly used open source proteomics analysis pipeline. Conclusions We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.

  10. Discrete wavelet transform coefficients for emotion recognition from EEG signals.

    Science.gov (United States)

    Yohanes, Rendi E J; Ser, Wee; Huang, Guang-bin

    2012-01-01

    In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: F(p1) and F(p2). Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.

  11. Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

    Science.gov (United States)

    Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.

    2013-02-01

    The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

  12. Tree structured wavelet transform segmentation of microcalcifications in digital mammography.

    Science.gov (United States)

    Qian, W; Kallergi, M; Clarke, L P; Li, H D; Venugopal, P; Song, D; Clark, R A

    1995-08-01

    A novel multistage algorithm is proposed for the automatic segmentation of microcalcification clusters (MCCs) in digital mammography. First, a previously reported tree structured nonlinear filter is proposed for suppressing image noise, while preserving image details, to potentially reduce the false positive (FP) detection rate for MCCs. Second, a tree structured wavelet transform (TSWT) is applied to the images for microcalcification segmentation. The TSWT employs quadrature mirror filters as basic subunits for both multiresolution decomposition and reconstruction processes, where selective reconstruction of subimages is used to segment MCCs. Third, automatic linear scaling is then used to display the image of the segmented MCCs on a computer monitor for interpretation. The proposed algorithms were applied to an image database of 100 single view mammograms at a resolution of 105 microns and 12 bits deep (4096 gray levels). The database contained 50 cases of biopsy proven malignant MCCs, 8 benign cases, and 42 normal cases. The measured sensitivity (true positive detection rate) was 94% with a low FP detection rate of 1.6 MCCs/image. The image details of the segmented MCCs were reasonably well preserved, for microcalcification of less than 500 microns, with good delineation of the extent of the microcalcification clusters for each case based on visual criteria.

  13. Fetal Heart Sounds Detection Using Wavelet Transform and Fractal Dimension.

    Science.gov (United States)

    Koutsiana, Elisavet; Hadjileontiadis, Leontios J; Chouvarda, Ioanna; Khandoker, Ahsan H

    2017-01-01

    Phonocardiography is a non-invasive technique for the detection of fetal heart sounds (fHSs). In this study, analysis of fetal phonocardiograph (fPCG) signals, in order to achieve fetal heartbeat segmentation, is proposed. The proposed approach (namely WT-FD) is a wavelet transform (WT)-based method that combines fractal dimension (FD) analysis in the WT domain for the extraction of fHSs from the underlying noise. Its adoption in this field stems from its successful use in the fields of lung and bowel sounds de-noising analysis. The efficiency of the WT-FD method in fHS extraction has been evaluated with 19 simulated fHS signals, created for the present study, with additive noise up to (3 dB), along with the simulated fPCGs database available at PhysioBank. Results have shown promising performance in the identification of the correct location and morphology of the fHSs, reaching an overall accuracy of 89% justifying the efficacy of the method. The WT-FD approach effectively extracts the fHS signals from the noisy background, paving the way for testing it in real fHSs and clearly contributing to better evaluation of the fetal heart functionality.

  14. Fetal Heart Sounds Detection Using Wavelet Transform and Fractal Dimension

    Directory of Open Access Journals (Sweden)

    Elisavet Koutsiana

    2017-09-01

    Full Text Available Phonocardiography is a non-invasive technique for the detection of fetal heart sounds (fHSs. In this study, analysis of fetal phonocardiograph (fPCG signals, in order to achieve fetal heartbeat segmentation, is proposed. The proposed approach (namely WT–FD is a wavelet transform (WT-based method that combines fractal dimension (FD analysis in the WT domain for the extraction of fHSs from the underlying noise. Its adoption in this field stems from its successful use in the fields of lung and bowel sounds de-noising analysis. The efficiency of the WT–FD method in fHS extraction has been evaluated with 19 simulated fHS signals, created for the present study, with additive noise up to (3 dB, along with the simulated fPCGs database available at PhysioBank. Results have shown promising performance in the identification of the correct location and morphology of the fHSs, reaching an overall accuracy of 89% justifying the efficacy of the method. The WT–FD approach effectively extracts the fHS signals from the noisy background, paving the way for testing it in real fHSs and clearly contributing to better evaluation of the fetal heart functionality.

  15. IMAGE ENHANCEMENT MENGGGUNAKAN METODE LINEAR FILTERING DAN STATIONARY WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Silvester Tena

    2009-12-01

    Full Text Available The aim of the research is image enhancement using the linear filtering and the Stationary Wavelet Transform (SWT method. The linear filtering method using in this research is median filter, low pass filter and wiener filter and the SWT method using wavelet haar/db1. The Noise as input for source image is salt&pepper and gaussian. The quality of image enhancement determined by qualitative and quantitative assesments. Quantitative performance of the method can be measured by MSE and PSNR. The research shows that ever greater of  the noise density cause the value of MSE uphill progressively but the value of PSNR decrease progressively. The qualitative assessment depend on the everyone perception to the image enhancement. The result obtained shows that the SWT method better than linear filtering method.

  16. Discovering the Merit of the Wavelet Transform for Object Classification

    Science.gov (United States)

    2004-03-01

    Targeting Covert Messages: A Unique Approach for Detecting Novel Steganography . MS thesis, AFIT/GCE/ENG/03-02, Graduate School of Engineering, Air...Matl02] Math Works. MATLAB User’s Guide. 2002. [Mend01] Mendenhall, M. Wavelet-Based Audio Embedding and Audio /Video Compression. MS

  17. Impedance cardiography signal denoising using discrete wavelet transform.

    Science.gov (United States)

    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.

  18. Modelling spatial density using continuous wavelet transforms

    Indian Academy of Sciences (India)

    A K ANILKUMAR3. 1Digital Mapping and Modelling Division, Advanced Data Processing Research .... dow based search for peaks is sensitive to noise and do not differentiate strong and weak peaks. Methods based ... eter space and iteratively searching for optimal parameters by minimizing a criteria (e.x., sum of square ...

  19. Random wavelet transforms, algebraic geometric coding, and their applications in signal compression and de-noising

    Energy Technology Data Exchange (ETDEWEB)

    Bieleck, T.; Song, L.M.; Yau, S.S.T. [Univ. of Illinois, Chicago, IL (United States); Kwong, M.K. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

    1995-07-01

    The concepts of random wavelet transforms and discrete random wavelet transforms are introduced. It is shown that these transforms can lead to simultaneous compression and de-noising of signals that have been corrupted with fractional noises. Potential applications of algebraic geometric coding theory to encode the ensuing data are also discussed.

  20. Wavelet-based Image Enhancement Using Fourth Order PDE

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren

    2011-01-01

    The presence of noise interference signal may cause problems in signal and image analysis; hence signal and image de-noising is often used as a preprocessing stage in many signal processing applications. In this paper, a new method is presented for image de-noising based on fourth order partial...... differential equations (PDEs) and wavelet transform. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate coefficients of the image unchanged. These coefficients have the main information of the image. Since noise affects both...... the approximate and detail coefficients, in this research, the anisotropic diffusion technique for noise reduction is applied on the approximation band to alleviate the deficiency of the existing wavelet thresholding methods. The proposed method was applied on several standard noisy images and the results...

  1. RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Celso A. G. Santos

    2016-01-01

    Full Text Available The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009 from a raingauge and also using daily rainfall data (1998–2013 from the Tropical Rainfall Measuring Mission (TRMM. The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256– 512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

  2. Lung Lesion Segmentation Using Gaussian Filter and Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Dimililer Kamil

    2017-01-01

    Full Text Available Lung cancer is the growth of a tumour, referred to as a nodule that arises from cells covering the airways of the respiratory arrangement. Effective detection of lung cancer at premature stages enables any cure options, and reduce risk of insidious surgery and increased continued existence rate. Recently, image processing techniques are extensively used in different medical areas for lung tumour image improvement in early detection and cure stages. This is due to the importance of the time factor of discovering the abnormality issues in target images. The developed system is mainly an algorithm combining different image processing techniques such as filtering, erosion, discrete wavelets transform, and thresholding. However, the main aim of this work is to investigate the effectiveness of different filters along with different types of discrete wavelets toward an accurate segmentation of a lung tumour in a CT image. The experimental results of the developed system show that the use of Gaussian filter with the Haar wavelets is the best for such segmentation task.

  3. Assessing heart rate variability through wavelet-based statistical measures.

    Science.gov (United States)

    Wachowiak, Mark P; Hay, Dean C; Johnson, Michel J

    2016-10-01

    Because of its utility in the investigation and diagnosis of clinical abnormalities, heart rate variability (HRV) has been quantified with both time and frequency analysis tools. Recently, time-frequency methods, especially wavelet transforms, have been applied to HRV. In the current study, a complementary computational approach is proposed wherein continuous wavelet transforms are applied directly to ECG signals to quantify time-varying frequency changes in the lower bands. Such variations are compared for resting and lower body negative pressure (LBNP) conditions using statistical and information-theoretic measures, and compared with standard HRV metrics. The latter confirm the expected lower variability in the LBNP condition due to sympathetic nerve activity (e.g. RMSSD: p=0.023; SDSD: p=0.023; LF/HF: p=0.018). Conversely, using the standard Morlet wavelet and a new transform based on windowed complex sinusoids, wavelet analysis of the ECG within the observed range of heart rate (0.5-1.25Hz) exhibits significantly higher variability, as measured by frequency band roughness (Morlet CWT: p=0.041), entropy (Morlet CWT: p=0.001), and approximate entropy (Morlet CWT: p=0.004). Consequently, this paper proposes that, when used with well-established HRV approaches, time-frequency analysis of ECG can provide additional insights into the complex phenomenon of heart rate variability. Copyright © 2016. Published by Elsevier Ltd.

  4. Wavelet Transforms in Parallel Image Processing

    Science.gov (United States)

    1994-01-27

    Gaussian taken as the scale values. This was advocated by Marr and Hildreth, for example, using zero-crossings of the Laplacian of a Gaussian under...Wickerhauser, Entropy -Based Algorithms for Best Basis Selections, IEEE Trans. on Information Theory, Vol. 38, 1992, 713-718. [CYPH90 R. Cypher, J.L. Sanz, L...and Q.F. Stout, eds. Reconfigurable Massively Parallel Computers, Prentice Hall, Englewood Cliffs, New Jersey, 1991. [MAHI80 D. Marr and E. Hildreth

  5. Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai; Hermann, Cristoph S.

    2006-01-01

    -Montes, E., Valdes-Sosa, P.A., Nishiyama, N., Mizuhara, H., Yamaguchi, Y., 2004. Decomposing EEG data into space-time-frequency components using parallel factor analysis. Neuroimage 22, 1035-1045). In this article, PARAFAC is used for the first time to decompose wavelet transformed event-related EEG given...... of frequency transformed multi-channel EEG of channel x frequency x time data. The multi-way decomposition method Parallel Factor (PARAFAC), also named Canonical Decomposition (CANDECOMP), was recently used to decompose the wavelet transformed ongoing EEG of channel x frequency x time (Miwakeichi, F., Martinez......In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and independent component analysis (ICA) have widely been used. However, as both methods are based on handling two-way data, i.e. two-dimensional matrices, multi-way methods might improve the interpretation...

  6. adaptive single-pole autoreclosure scheme based on wavelet ...

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    WAVELET TRANSFORM AND MULTILAYER PERCEPTRON. E. A. Frimpong, P. Y. Okyere and E. K. Anto. Department of ... value to achieve successful autoreclosing (Park et al., 2004; Megahed et al., 2003; Kim et al.,. 2000; Ahn ... transform (Fitton et al., 1996; Zoric et al.,. 2000), and wavelet transform (Yu and Song,. 1998a ...

  7. Facial Expression Recognition Using Stationary Wavelet Transform Features

    Directory of Open Access Journals (Sweden)

    Huma Qayyum

    2017-01-01

    Full Text Available Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.

  8. Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

    Directory of Open Access Journals (Sweden)

    Verônica Isabela Quandt

    Full Text Available Introduction Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1 and ASYMW (Antisymmetric-Padding (whole-point modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5 were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.

  9. LOW COMPLEXITY HYBRID LOSSY TO LOSSLESS IMAGE CODER WITH COMBINED ORTHOGONAL POLYNOMIALS TRANSFORM AND INTEGER WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    R. Krishnamoorthy

    2012-05-01

    Full Text Available In this paper, a new lossy to lossless image coding scheme combined with Orthogonal Polynomials Transform and Integer Wavelet Transform is proposed. The Lifting Scheme based Integer Wavelet Transform (LS-IWT is first applied on the image in order to reduce the blocking artifact and memory demand. The Embedded Zero tree Wavelet (EZW subband coding algorithm is used in this proposed work for progressive image coding which achieves efficient bit rate reduction. The computational complexity of lower subband coding of EZW algorithm is reduced in this proposed work with a new integer based Orthogonal Polynomials transform coding. The normalization and mapping are done on the subband of the image for exploiting the subjective redundancy and the zero tree structure is obtained for EZW coding and so the computation complexity is greatly reduced in this proposed work. The experimental results of the proposed technique also show that the efficient bit rate reduction is achieved for both lossy and lossless compression when compared with existing techniques.

  10. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

    Science.gov (United States)

    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.

  11. Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition.

    Science.gov (United States)

    Yang, Bang-hua; Yan, Guo-zheng; Yan, Rong-guo; Wu, Ting

    2006-12-01

    A method based on wavelet packet best basis decomposition (WPBBD) is investigated for the purpose of extracting features of electroencephalogram signals produced during motor imagery tasks in brain-computer interfaces. The method includes the following three steps. (1) Original signals are decomposed by wavelet packet transform (WPT) and a wavelet packet library can be formed. (2) The best basis for classification is selected from the library. (3) Subband energies included in the best basis are used as effective features. Three different motor imagery tasks are discriminated using the features. The WPBBD produces a 70.3% classification accuracy, which is 4.2% higher than that of the existing wavelet packet method.

  12. Feature extraction for EEG-based brain computer interfaces by wavelet packet best basis decomposition

    Science.gov (United States)

    Yang, Bang-hua; Yan, Guo-zheng; Yan, Rong-guo; Wu, Ting

    2006-12-01

    A method based on wavelet packet best basis decomposition (WPBBD) is investigated for the purpose of extracting features of electroencephalogram signals produced during motor imagery tasks in brain-computer interfaces. The method includes the following three steps. (1) Original signals are decomposed by wavelet packet transform (WPT) and a wavelet packet library can be formed. (2) The best basis for classification is selected from the library. (3) Subband energies included in the best basis are used as effective features. Three different motor imagery tasks are discriminated using the features. The WPBBD produces a 70.3% classification accuracy, which is 4.2% higher than that of the existing wavelet packet method.

  13. Use of the Wavelet Transform for Interference Detection and Mitigation in Global Navigation Satellite Systems

    Directory of Open Access Journals (Sweden)

    Luciano Musumeci

    2014-01-01

    Full Text Available Radio frequency interference detection and mitigation are becoming of paramount importance due to the increasing number of services and applications based on the position obtained by means of Global Navigation Satellite Systems. A way to cope with such threats is the implementation in the receiver of advanced signal processing algorithm able to raise proper warning or improve the receiver performance. In this paper, we propose a method based on the Wavelet Transform able to split the useful signal from the interfering component in a transformed domain. The wavelet packet decomposition and proper statistical thresholds allow the algorithm to show very good performance in case of multiple pulse interference as well as in the case of narrowband interference, two scenarios in which traditional countermeasures might not be effective.

  14. Curie temperature determination via thermogravimetric and continuous wavelet transformation analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hasier, John; Nash, Philip [Thermal Processing Technology Center, IIT, Chicago, IL (United States); Riolo, Maria Annichia [University of Michigan, Center for the Study of Complex Systems, Ann Arbor, MI (United States)

    2017-12-15

    A cost effective method for conversion of a vertical tube thermogravimetric analysis system into a magnetic balance capable of measuring Curie Temperatures is presented. Reference and preliminary experimental data generated using this system is analyzed via a general-purpose wavelet based Curie point edge detection technique allowing for enhanced speed, ease and repeatability of magnetic balance data analysis. The Curie temperatures for a number of Heusler compounds are reported. (orig.)

  15. Steady-state sweep visual evoked potential processing denoised by wavelet transform

    Science.gov (United States)

    Weiderpass, Heinar A.; Yamamoto, Jorge F.; Salomão, Solange R.; Berezovsky, Adriana; Pereira, Josenilson M.; Sacai, Paula Y.; de Oliveira, José P.; Costa, Marcio A.; Burattini, Marcelo N.

    2008-03-01

    Visually evoked potential (VEP) is a very small electrical signal originated in the visual cortex in response to periodic visual stimulation. Sweep-VEP is a modified VEP procedure used to measure grating visual acuity in non-verbal and preverbal patients. This biopotential is buried in a large amount of electroencephalographic (EEG) noise and movement related artifact. The signal-to-noise ratio (SNR) plays a dominant role in determining both systematic and statistic errors. The purpose of this study is to present a method based on wavelet transform technique for filtering and extracting steady-state sweep-VEP. Counter-phase sine-wave luminance gratings modulated at 6 Hz were used as stimuli to determine sweep-VEP grating acuity thresholds. The amplitude and phase of the second-harmonic (12 Hz) pattern reversal response were analyzed using the fast Fourier transform after the wavelet filtering. The wavelet transform method was used to decompose the VEP signal into wavelet coefficients by a discrete wavelet analysis to determine which coefficients yield significant activity at the corresponding frequency. In a subsequent step only significant coefficients were considered and the remaining was set to zero allowing a reconstruction of the VEP signal. This procedure resulted in filtering out other frequencies that were considered noise. Numerical simulations and analyses of human VEP data showed that this method has provided higher SNR when compared with the classical recursive least squares (RLS) method. An additional advantage was a more appropriate phase analysis showing more realistic second-harmonic amplitude value during phase brake.

  16. The wavelet transform and the suppression theory of binocular vision for stereo image compression

    Energy Technology Data Exchange (ETDEWEB)

    Reynolds, W.D. Jr [Argonne National Lab., IL (United States); Kenyon, R.V. [Illinois Univ., Chicago, IL (United States)

    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.

  17. Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain

    Directory of Open Access Journals (Sweden)

    Oraintara Soontorn

    2008-09-01

    Full Text Available Abstract 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.

  18. Adaptive Wavelet Transform Method to Identify Cracks in Gears

    Directory of Open Access Journals (Sweden)

    Belsak Ales

    2010-01-01

    Full Text Available Many damages and faults can cause problems in gear unit operation. A crack in the tooth root is probably the least desirable among them. It often leads to failure of gear unit operation. By monitoring vibrations, it is possible to determine the presence of a crack. Signals are, however, very noisy. This makes it difficult to define properties of individual components. Wavelet analysis is an effective tool for analysing signals and for defining properties. In this paper, a denoising method based on wavelet analysis, which takes prior information about impulse probability density into consideration, is used to identify transient information from vibration signals of a gear unit with a fatigue crack in the tooth root.

  19. Time-frequency analysis of spike-wave discharges using a modified wavelet transform

    NARCIS (Netherlands)

    Bosnyakova, D.Y.; Gabova, A.V.; Kuznetsova, G.D.; Obukhov, Y.; Midzyanovskaya, I.S.; Salonin, D.V.; Rijn, C.M. van; Coenen, A.M.L.; Tuomisto, L.M.; Luijtelaar, E.L.J.M. van

    2006-01-01

    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

  20. Continuous wavelet transform of wind and wind-induced pressures on a building in suburban terrain

    NARCIS (Netherlands)

    Geurts, C.P.W.; Hajj, M.R.; Tieleman, H.W.

    1998-01-01

    The wavelet transform is a promising tool for the analysis of incident wind and wind loading on structures. The continuous wavelet transform is applied to full-scale velocity and pressure measurements, taken at the main building of Eindhoven University of Technology. Initial results indicate that

  1. Wavelet applications in engineering electromagnetics

    National Research Council Canada - National Science Library

    Sarkar, Tapan; Salazar-Palma, Magdalena; Wicks, Michael C

    2002-01-01

    ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Road Map of the Book . . . . . . Introduction . . . . . . . . Why Use Wavelets? . . . . . . What Are Wavelets? . . . . . . What Is the Wavelet Transform? . . . Use...

  2. Determination of curvature and twist by digital shearography and wavelet transforms.

    Science.gov (United States)

    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.

  3. Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Bartosch, T. [Erlanger-Nuernberg Univ., Erlanger (Germany). Lehrstul fuer Nachrichtentechnik I; Seidl, D. [Seismologisches Zentralobservatorium Graefenberg, Erlanegen (Greece). Bundesanstalt fuer Geiwissenschaften und Rohstoffe

    1999-06-01

    Among a variety of spectrogram methods short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were selected to analyse transients in non-stationary signals. Depending on the properties of the tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli (Italy).

  4. SPECTRAL ANALYSIS OF EEG SIGNALS BY USING WAVELET AND HARMONIC TRANSFORMS

    Directory of Open Access Journals (Sweden)

    A.H. SIDDIQI

    2014-05-01

    Full Text Available In this study, wavelet transforms and FFT methods, which transform method is better for spectral analysis of the brain signals are investigated. Statistical and Fourier methods are traditional techniques and tools to analyze time series signals in general, including biomedical data. In this paper as spectral analysis tools, wavelet transform and harmonic transform are used. Both transform methods are applied to electroencephalogram (EEG of a possibly epilepsy patient and are compared. For this purpose in the harmonic transform case, the variations of first-sixth-order harmonic amplitudes and phases provide a useful tool of understanding the large- and local-scale effects on the parameters. Moreover, temporal and frequency variations of variables are also detected by wavelet transforms. The results of this study are compared with previous studies. The comparison of results show that the wavelet transform method has more advantage in detecting brain diseases.

  5. Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

    Science.gov (United States)

    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.

  6. SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    A. Rezaeian

    2015-12-01

    Full Text Available 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.

  7. The application study of wavelet packet transformation in the de-noising of dynamic EEG data.

    Science.gov (United States)

    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.

  8. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform

    Science.gov (United States)

    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.

  9. Distance protection using a wavelet-based filtering algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Kleber M. [University of Brasilia, Electrical Engineering Department, 70919-970 Brasilia (Brazil); Neves, Washington L.A.; Souza, Benemar A. [Federal University of Campina Grande, Paraiba, Brazil, Electrical Engineering Department, 882 Aprigio Veloso Ave, Bodocongo, 58109-970 Campina Grande (Brazil)

    2010-01-15

    This paper presents a novel filter design technique based on the maximal overlap discrete wavelet transform (MODWT). Time and frequency response of the designed filters are compared to the ones of traditional discrete Fourier transform (DFT) based filters. The obtained results show improvements on response speed when comparing to full cycle traditional filters and improvements on frequency response when compared to half cycle traditional filters. According to these characteristics, the designed filter may be used in secure high-speed distance protection, since it provides fast relay operating times, without compromising their frequency response. (author)

  10. Cross-correlation of bio-signals using continuous wavelet transform and genetic algorithm.

    Science.gov (United States)

    Sukiennik, Piotr; Białasiewicz, Jan T

    2015-05-30

    Continuous wavelet transform allows to obtain time-frequency representation of a signal and analyze short-lived temporal interaction of concurrent processes. That offers good localization in both time and frequency domain. Scalogram and coscalogram analysis of two signal interaction dynamics gives an indication of the cross-correlation of analyzed signals in both domains. We have used genetic algorithm with a fitness function based on signals convolution to find time delay between investigated signals. Two methods of cross-correlation are proposed: one that finds single delay for analyzed signals, and one returns a vector of delay values for each of wavelet transform sub-band center frequencies. Algorithms were implemented using MATLAB. We have extracted the data of simultaneously recorded encephalogram and arterial blood pressure and have investigated their interaction dynamics. We found time delay whose value cannot be precisely determined by scalograms and coscalogram inspection. The biomedical signals used come from MIMIC database. Cross-correlation of two complex signals is commonly performed using fast Fourier transform. It works well for signals with invariant frequency content. We have determined the time delay between analyzed signals using wavelet scalograms and we have accordingly shifted one of them, aligning associated events. Their coscalogram indicates the cross-correlation of the associated events. Introducing new methods of wavelet transform in cross-correlation analysis has proven to be beneficial to the gain of the information about process interaction. Introduced solutions could be used to reason about causality between processes and gain bigger insight regarding analyzed systems. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

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

    Science.gov (United States)

    Jiang, Hua; Lu, Wenke; Zhang, Guoan

    2013-07-01

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

  13. Digital Watermarks Using Discrete Wavelet Transformation and Spectrum Spreading

    Directory of Open Access Journals (Sweden)

    Ryousuke Takai

    2003-12-01

    Full Text Available In recent tears, digital media makes rapid progress through the development of digital technology. Digital media normally assures fairly high quality, nevertheless can be easily reproduced in a perfect form. This perfect reproducibility takes and advantage from a certain point of view, while it produces an essential disadvantage, since digital media is frequently copied illegally. Thus the problem of the copyright protection becomes a very important issue. A solution of this problem is to embed digital watermarks that is not perceived clearly by usual people, but represents the proper right of original product. In our method, the images data in the frequency domain are transformed by the Discrete Wavelet Transform and analyzed by the multi resolution approximation, [1]. Further, the spectrum spreading is executed by using PN-sequences. Choi and Aizawa [7] embed watermarks by using block correlation of DCT coefficients. Thus, we apply Discrete Cosine Transformation, abbreviated to DCT, instead of the Fourier transformation in order to embed watermarks.If the value of this variance is high then we decide that the block has bigger magnitude for visual fluctuations. Henceforth, we may embed stronger watermarks, which gives resistance for images processing, such as attacks and/or compressions.

  14. A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

    Directory of Open Access Journals (Sweden)

    Guiji Tang

    2016-01-01

    Full Text Available A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT, the improved multiscale permutation entropy (IMPE, and the linear local tangent space alignment (LLTSA with the extreme learning machine (ELM, is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

  15. Comparative Study on Facial Expression Recognition using Gabor and Dual-Tree Complex Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Alaa Eleyan

    2017-04-01

    Full Text Available Moving from manually interaction with machines to automated systems, stressed on the importance of facial expression recognition for human computer interaction (HCI. In this article, an investigation and comparative study about the use of complex wavelet transforms for Facial Expression Recognition (FER problem was conducted. Two complex wavelets were used as feature extractors; Gabor wavelets transform (GWT and dual-tree complex wavelets transform (DT-CWT. Extracted feature vectors were fed to principal component analysis (PCA or local binary patterns (LBP. Extensive experiments were carried out using three different databases, namely; JAFFE, CK and MUFE databases. For evaluation of the performance of the system, k-nearest neighbor (kNN, neural networks (NN and support vector machines (SVM classifiers were implemented. The obtained results show that the complex wavelet transform together with sophisticated classifiers can serve as a powerful tool for facial expression recognition problem.

  16. Mammographic image enhancement using wavelet transform and homomorphic filter

    Directory of Open Access Journals (Sweden)

    F Majidi

    2015-12-01

    Full Text Available Mammography is the most effective method for the early diagnosis of breast cancer diseases. As mammographic images contain low signal to noise ratio and low contrast, it becomes too difficult for radiologists to analyze mammogram. To deal with the above stated problems, it is very important to enhance the mammographic images using image processing methods. This paper introduces a new image enhancement approach for mammographic images which uses the modified mathematical morphology, wavelet transform and homomorphic filter to suppress the noise of images. For performance evaluation of the proposed method, contrast improvement index (CII and edge preservation index (EPI are adopted. Experimental results on mammographic images from Pejvak Digital Imaging Center (PDIC show that the proposed algorithm improves the two indexes, thereby achieving the goal of enhancing mammographic images.

  17. Histogram Modification and Wavelet Transform for High Performance Watermarking

    Directory of Open Access Journals (Sweden)

    Ying-Shen Juang

    2012-01-01

    Full Text Available This paper proposes a reversible watermarking technique for natural images. According to the similarity of neighbor coefficients’ values in wavelet domain, most differences between two adjacent pixels are close to zero. The histogram is built based on these difference statistics. As more peak points can be used for secret data hiding, the hiding capacity is improved compared with those conventional methods. Moreover, as the differences concentricity around zero is improved, the transparency of the host image can be increased. Experimental results and comparison show that the proposed method has both advantages in hiding capacity and transparency.

  18. Time series analysis of homoclinic nonlinear systems using a wavelet transform method

    Science.gov (United States)

    Austin, James C.; Healey, Jonathan J.

    2004-06-01

    Homoclinic (and heteroclinic) trajectories are closed paths in phase space that connect one or more saddle points. They play an important role in the study of dynamical systems and are associated with the creation/destruction of limit cycles as a parameter is varied. Often, this creation/destruction process involves complicated sequences of bifurcations in small regions of parameter space and there is now an established theoretical framework for the study of such systems. The eigenvalues of saddle points in the phase space determine the behaviour of the system. In this article we present a new eigenvalue estimation technique based on a wavelet transformation of a time series under study and compare it with an existing method based on phase space reconstruction. We find that the two methods give good agreement with theory using clean model data, but where noisy data are analysed the wavelet technique is both more robust and easier to implement.

  19. A New Texture Synthesis Algorithm Based on Wavelet Packet Tree

    Directory of Open Access Journals (Sweden)

    Hsi Chin Hsin

    2012-01-01

    Full Text Available This paper presents an efficient texture synthesis based on wavelet packet tree (TSWPT. It has the advantage of using a multiresolution representation with a greater diversity of bases functions for the nonlinear time series applications such as fractal images. The input image is decomposed into wavelet packet coefficients, which are rearranged and organized to form hierarchical trees called wavelet packet trees. A 2-step matching, that is, coarse matching based on low-frequency wavelet packet coefficients followed by fine matching based on middle-high-frequency wavelet packet coefficients, is proposed for texture synthesis. Experimental results show that the TSWPT algorithm is preferable, especially in terms of computation time.

  20. A common dominant scale emerges from images of diverse satellite platforms using the wavelet transform

    NARCIS (Netherlands)

    Pittiglio, C.; Skidmore, A.K.; Bie, de C.A.J.M.; Murwira, A.

    2011-01-01

    In this article we investigate the scale dependence of spatial heterogeneity in multiresolution and multisensor data using the wavelet transform. The landscape analysed with the wavelets retains the same dominant pattern irrespective of the original pixel size of the image. In agricultural areas,

  1. Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

    Directory of Open Access Journals (Sweden)

    Zhigang Liu

    2015-08-01

    Full Text Available Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE and modified incomplete S-transform (MIST is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ disturbances including single disturbances and combined disturbances is extracted. Then, a ruled decision tree is designed to recognize the combined disturbances. The proposed method is tested and evaluated using a large number of simulated PQ disturbances and some real-life signals, which include voltage sag, swell, interruption, oscillation transient, impulsive transient, harmonics, voltage fluctuation and their combinations. In addition, the comparison of the proposed recognition approach with some existing techniques is made. The experimental results show that the proposed method can effectively recognize the single and combined PQ disturbances.

  2. A Wavelet Algorithm for Fourier-Bessel Transform Arising in Optics

    Directory of Open Access Journals (Sweden)

    Nagma Irfan

    2015-01-01

    Full Text Available The aim of the paper is to propose an efficient and stable algorithm that is quite accurate and fast for numerical evaluation of the Fourier-Bessel transform of order ν,  ν>-1, using wavelets. The philosophy behind the proposed algorithm is to replace the part tf(t of the integral by its wavelet decomposition obtained by using CAS wavelets thus representing Fν(p as a Fourier-Bessel series with coefficients depending strongly on the input function tf(t. The wavelet method indicates that the approach is easy to implement and thus computationally very attractive.

  3. Time-Frequency-Wavenumber Analysis of Surface Waves Using the Continuous Wavelet Transform

    Science.gov (United States)

    Poggi, V.; Fäh, D.; Giardini, D.

    2013-03-01

    A modified approach to surface wave dispersion analysis using active sources is proposed. The method is based on continuous recordings, and uses the continuous wavelet transform to analyze the phase velocity dispersion of surface waves. This gives the possibility to accurately localize the phase information in time, and to isolate the most significant contribution of the surface waves. To extract the dispersion information, then, a hybrid technique is applied to the narrowband filtered seismic recordings. The technique combines the flexibility of the slant stack method in identifying waves that propagate in space and time, with the resolution of f- k approaches. This is particularly beneficial for higher mode identification in cases of high noise levels. To process the continuous wavelet transform, a new mother wavelet is presented and compared to the classical and widely used Morlet type. The proposed wavelet is obtained from a raised-cosine envelope function (Hanning type). The proposed approach is particularly suitable when using continuous recordings (e.g., from seismological-like equipment) since it does not require any hardware-based source triggering. This can be subsequently done with the proposed method. Estimation of the surface wave phase delay is performed in the frequency domain by means of a covariance matrix averaging procedure over successive wave field excitations. Thus, no record stacking is necessary in the time domain and a large number of consecutive shots can be used. This leads to a certain simplification of the field procedures. To demonstrate the effectiveness of the method, we tested it on synthetics as well on real field data. For the real case we also combine dispersion curves from ambient vibrations and active measurements.

  4. SVD-based digital image watermarking using complex wavelet ...

    Indian Academy of Sciences (India)

    A new robust method of non-blind image watermarking is proposed in this paper. The suggested method is performed by modification on singular value decomposition (SVD) of images in Complex Wavelet Transform (CWT) domain while CWT provides higher capacity than the real wavelet domain. Modification of the ...

  5. On Seismic Ground Roll Filtering Using the Wavelet Transform and Neural Network

    Science.gov (United States)

    Benaissa, Zahia; Benaissa, Abdelkader; Ouadfeul, Sid-Ali; Aliouane, Leila; Boudella, Amar

    2013-04-01

    Here, we present an adapted filtering technique for the non-stationary signals. It is based on the wavelet transform and its rebuilding formula. This technique is used generally to detect and extract locally in the time-scale field particular events from seismic data. We show the efficiency of this technique to filter the ground roll from reflection seismic vibroseis recording (shot gather). The results for two different filtering processes are presented, one of these results is based on the annulment of the transform coefficients in the selected zone relating to the ground roll, and the other one is based on their attenuation (roll-off). Obtained results shows the efficiency of the first process especially when the wavelet transform is calculated only on the noisy zone and when the ground roll is made up of two or more pseudo-Rayleigh waves, in this case iterations are mandatory to improve the signal to noise ratio using the second process. The current work shows also the use of the artificial neural network on the prediction of the mute parameters in the F-K domain to be used on the Ground Roll attenuation. The proposed idea is very robust and useful in case of 3D seismic data. A set of 3D seismic Inlines are used for the training of the Multilayer Perceptron (MLP) neural network machine. Application to real data shows clearly the robustness of the proposed technique. Keywords: Filtering - Ground roll - Wavelet transform - Seismic - Reflection - Signal to noise ratio - Artificial neuronal network -3D-MLP- Training.

  6. Wavelets and Morphological Operators Based Classification of Epilepsy Risk Levels

    Directory of Open Access Journals (Sweden)

    Harikumar Rajaguru

    2014-01-01

    Full Text Available 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 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 peaks, spike and sharp waves, events, and average duration are extracted using Haar, dB2, dB4, and Sym8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. 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.

  7. Wavelet and K-L Seperability Based Feature Extraction Method for Functional Data Classification

    OpenAIRE

    Jun Wan; Zehua Chen; Yingwu Chen; Zhidong Bai

    2010-01-01

    This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A ...

  8. Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

    Directory of Open Access Journals (Sweden)

    D. Seidl

    1999-06-01

    Full Text Available Among a variety of spectrogram methods Short-Time Fourier Transform (STFT and Continuous Wavelet Transform (CWT were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.

  9. Joint multifractal analysis based on wavelet leaders

    Science.gov (United States)

    Jiang, Zhi-Qiang; Yang, Yan-Hong; Wang, Gang-Jin; Zhou, Wei-Xing

    2017-12-01

    Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.

  10. Sleep spindles and spike-wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis.

    Science.gov (United States)

    Sitnikova, Evgenia; Hramov, Alexander E; Koronovsky, Alexey A; van Luijtelaar, Gilles

    2009-06-15

    Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. A relationship between SWD and normal sleep spindles is often assumed. This study compares time-frequency parameters of SWD and sleep spindles as recorded in the EEG in the WAG/Rij rat model of absence epilepsy. Fast Fourier transformation and continuous wavelet transformation were used for EEG analysis. Wavelet analysis was performed in non-segmented full-length EEG. A specific wavelet-based algorithm was developed for the automatic identification of sleep spindles and SWD. None of standard wavelet templates provided precise identification of all sleep spindles and SWD in the EEG and different wavelet templates were imperative in order to accomplish this task. SWD were identified with high probability using standard Morlet wavelet, but sleep spindles were identified using two types of customized adoptive 'spindle wavelets'. It was found that (1) almost 100% of SWD (but only 50-60% of spindles) were identified using the Morlet-based wavelet transform. (2) 82-91% of sleep spindles were selected using adoptive 'spindle wavelet 1' (template's peak frequency approximately 12.2 Hz), the remaining sleep spindles with 'spindle wavelet 2' (peak frequency approximately 20-25 Hz). (3) Sleep spindles and SWD were detected by the elevation of wavelet energy in different frequencies: SWD, in 30-50 Hz band, sleep spindles, in 7-14 Hz. It is concluded that the EEG patterns of sleep spindles and SWD belong to different families of phasic EEG events with different time frequency characteristics.

  11. Modeling and Forecast Biological Oxygen Demand (BOD using Combination Support Vector Machine with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Abazar Solgi

    2017-06-01

    Full Text Available Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated. Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1, the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was

  12. Wavelet Transforms: Application to Data Analysis - I -10 ...

    Indian Academy of Sciences (India)

    include image processing and artificial intelligence. ... Fourier domain. Broadly speaking, two different fea- tures common to all wavelets are responsible for their utility value. The basis functions of the wavelets are produced from two units, the ... Here, j and k take integral values, the values of j range from 0 to 00, whereas ...

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. WAVELET TRANSFORM ANALYSIS OF ELECTROMYOGRAPHY KUNG FU STRIKES DATA

    Directory of Open Access Journals (Sweden)

    Ana Carolina de Miranda Marzullo

    2009-11-01

    Full Text Available 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

  15. Wavelet-based multifractal analysis of laser biopsy imagery

    CERN Document Server

    Jagtap, Jaidip; Panigrahi, Prasanta K; Pradhan, Asima

    2011-01-01

    In this work, we report a wavelet based multi-fractal study of images of dysplastic and neoplastic HE- stained human cervical tissues captured in the transmission mode when illuminated by a laser light (He-Ne 632.8nm laser). It is well known that the morphological changes occurring during the progression of diseases like cancer manifest in their optical properties which can be probed for differentiating the various stages of cancer. Here, we use the multi-resolution properties of the wavelet transform to analyze the optical changes. For this, we have used a novel laser imagery technique which provides us with a composite image of the absorption by the different cellular organelles. As the disease progresses, due to the growth of new cells, the ratio of the organelle to cellular volume changes manifesting in the laser imagery of such tissues. In order to develop a metric that can quantify the changes in such systems, we make use of the wavelet-based fluctuation analysis. The changing self- similarity during di...

  16. Adaptive Gain and Analog Wavelet Transform for Low-Power Infrared Image Sensors

    Directory of Open Access Journals (Sweden)

    P. Villard

    2012-01-01

    Full Text Available A decorrelation and analog-to-digital conversion scheme aiming to reduce the power consumption of infrared image sensors is presented in this paper. To exploit both intraframe redundancy and inherent photon shot noise characteristics, a column based 1D Haar analog wavelet transform combined with variable gain amplification prior to A/D conversion is used. This allows to use only an 11-bit ADC, instead of a 13-bit one, and to save 15% of data transfer. An 8×16 pixels test circuit demonstrates this functionality.

  17. Cryptosystem for Securing Image Encryption Using Structured Phase Masks in Fresnel Wavelet Transform Domain

    Science.gov (United States)

    Singh, Hukum

    2016-12-01

    A cryptosystem for securing image encryption is considered by using double random phase encoding in Fresnel wavelet transform (FWT) domain. Random phase masks (RPMs) and structured phase masks (SPMs) based on devil's vortex toroidal lens (DVTL) are used in spatial as well as in Fourier planes. The images to be encrypted are first Fresnel transformed and then single-level discrete wavelet transform (DWT) is apply to decompose LL,HL, LH and HH matrices. The resulting matrices from the DWT are multiplied by additional RPMs and the resultants are subjected to inverse DWT for the encrypted images. The scheme is more secure because of many parameters used in the construction of SPM. The original images are recovered by using the correct parameters of FWT and SPM. Phase mask SPM based on DVTL increases security that enlarges the key space for encryption and decryption. The proposed encryption scheme is a lens-less optical system and its digital implementation has been performed using MATLAB 7.6.0 (R2008a). The computed value of mean-squared-error between the retrieved and the input images shows the efficacy of scheme. The sensitivity to encryption parameters, robustness against occlusion, entropy and multiplicative Gaussian noise attacks have been analysed.

  18. DETECTION OF MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING ONE DIMENSIONAL WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    T. Balakumaran

    2010-11-01

    Full Text Available Mammography is the most efficient method for breast cancer early detection. Clusters of microcalcifications are the early sign of breast cancer and their detection is the key to improve prognosis of breast cancer. Microcalcifications appear in mammogram image as tiny localized granular points, which is often difficult to detect by naked eye because of their small size. Automatic and accurately detection of microcalcifications has received much more attention from radiologists and physician. An efficient method for automatic detection of clustered microcalcifications in digitized mammograms is the use of Computer Aided Diagnosis (CAD systems. This paper presents a one dimensional wavelet-based multiscale products scheme for microcalcification detection in mammogram images. The detection of microcalcifications were achieved by decomposing the each line of mammograms by 1D wavelet transform into different frequency sub-bands, suppressing the low-frequency subband, and finally reconstructing the mammogram from the subbands containing only significant high frequencies features. The significant features are obtained by multiscale products. Preliminary results indicate that the proposed scheme is better in suppressing the background and detecting the microcalcification clusters than any other wavelet decomposition methods.

  19. Damage identification in beam structure based on thresholded variance of normalized wavelet scalogram

    Science.gov (United States)

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

    2017-10-01

    In this paper, a damage identification algorithm based on continuous wavelet transform of one-dimensional structures exploiting mode shapes is presented. Numerical models of aluminium and carbon composite beams, containing a mill-cut and impact damage, respectively are considered for this study. Wavelet scalogram is used to obtain the transform coefficients at different wavelet scales and is subsequently normalized in order to emphasize locations with largest coefficients. Variance of normalized wavelet scalogram is computed along the axis of the beam yielding sharp peaks in the zones corresponding to damage in beams. This operation excludes wavelet scale factors as variables for damage localization problems. The universal threshold is applied to filter out lower amplitude peaks that do not indicate damage. These results are summed up for all nodes of beams and all wavelet functions that are analysed in this paper in order to also exclude the number of different wavelet functions as another variable for damage localization. The universal threshold is applied the second time to yield the final result on the locations of damage. Results suggest that the proposed damage localization method is a fast and reliable tool for damage detection in one-dimensional metal and composite beam structures.

  20. Wavefield analysis in inhomogeneous media by wavelet transform; Wavelet henkan ni yoru fukinshitsu baitai no hadoba kaiseki

    Energy Technology Data Exchange (ETDEWEB)

    Matsushima, J.; Rokugawa, S.; Kato, Y. [The University of Tokyo, Tokyo (Japan). Faculty of Engineering; Yokota, T.; Miyazaki, T. [Geological Survey of Japan, Tsukuba (Japan); Ichie, Y. [The University of Tokyo, Tokyo (Japan)

    1996-10-01

    Data processing techniques have been investigated for clarifying structures and physical properties of geothermal reservoirs in the deep underground by seismic exploration using multiple wells. They include the initial motion time-distance tomography, amplitude tomography, diffracted wave tomography, and structure imaging using reflected wave or scattered wave. When applying these data processing methods to observed records, weak and minor signals essentially required are canceled due to averaging the analytical fields. In this study, influence of inhomogeneous media on the wavefield was evaluated. Data were analyzed considering frequency by using wavelet transform by which time-frequency can be easily analyzed. From the time-frequency analysis using wavelet transform, it was illustrated that high frequency scattered waves, generated by scatterer like cracks or by irregularity on the reflection surface, arrive behind direct P-wave and direct S-wave. 5 refs., 8 figs.

  1. l1-norm regularization and wavelet transform: An improved plane-wave destruction method

    Science.gov (United States)

    Lin, Peng; Peng, Suping; Zhao, Jingtao; Cui, Xiaoqin; Wang, Huaihong

    2018-01-01

    Seismic diffractions are the specific responses of small-scale inhomogeneities or discontinuous structures in the subsurface, such as faults and cracks, and can be used for locating reservoirs of oil and gas. However, because diffraction energy is much weaker than reflection energy, separating diffractions against the background of strong reflections from seismic data is difficult. In this paper, we propose a regularization method based on the l1-norm constraint to extract seismic diffractions from seismic records in the common-offset gathers. Regularization is a practical method for ill-posed nonlinear problems. The proposed method considers wavelet transform and l1-norm regularization in the plane-wave destruction method, which enhances the stability and accuracy of reflection local slopes. Wavelet transform has multi-level and multi-scale analysis properties and thus it is an effective method for sparse transform. Further, the l1-norm can effectively constrain sparsity properties. Through a synthetic example, the stability of this regularization method is demonstrated to be stronger than that of the conventional plane-wave destruction (PWD) method. Both the numerical simulation and field data application indicate that the proposed regularization method for diffraction extraction is promising and feasible in removing specular reflections and strengthening diffractions.

  2. Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

    Directory of Open Access Journals (Sweden)

    Rusmir Bajric

    2016-01-01

    Full Text Available Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

  3. Digital Image Watermarking Algorithm Based on Wavelet Packet

    OpenAIRE

    Geetha, A.; B.Vijayakumari; C.Nagavani; T.Pandiselvi

    2011-01-01

    In this paper, a method for Digital Image Watermarking based on the modification of certain subband of the wavelet packet decomposition was presented. A key is used for wavelet bases selection, watermark generation and selection of blocks for embedding the watermark bits. To efficiently embed the watermark within the images and provide the robustness for the watermark detection under attacks, watermark is embedded by quantizing the mean of the wavelet coefficient block. A method for exploitin...

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

    Science.gov (United States)

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

    2013-02-01

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

  5. Implementation of 2D Discrete Wavelet Transform by Number Theoretic Transform and 2D Overlap-Save Method

    Directory of Open Access Journals (Sweden)

    Lina Yang

    2014-01-01

    Full Text Available To reduce the computation complexity of wavelet transform, this paper presents a novel approach to be implemented. It consists of two key techniques: (1 fast number theoretic transform(FNTT In the FNTT, linear convolution is replaced by the circular one. It can speed up the computation of 2D discrete wavelet transform. (2 In two-dimensional overlap-save method directly calculating the FNTT to the whole input sequence may meet two difficulties; namely, a big modulo obstructs the effective implementation of the FNTT and a long input sequence slows the computation of the FNTT down. To fight with such deficiencies, a new technique which is referred to as 2D overlap-save method is developed. Experiments have been conducted. The fast number theoretic transform and 2D overlap-method have been used to implement the dyadic wavelet transform and applied to contour extraction in pattern recognition.

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

    Science.gov (United States)

    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.

  7. A Wavelet-based Algorithm for Vehicle Flow Information Extraction

    OpenAIRE

    Ling-ling Li; Li-duan Liang; Lei Shi; Zhi Qiao

    2013-01-01

    This paper proposed an improved algorithm applied in video intelligent traffic control system for vehicle detection. The accuracy of original algorithm, which is based on the comparision of contrast and luminance distortion of present image with background, reduces greatly under bad weather because of false detection caused by noises in captured images. In this paper we chose Daubechies wavelet as mother wavelet to add a 2-dimension wavelet process before the algorithm, just after the image i...

  8. DENOISING RESISTIVITY PHOSPHATE “DISTURBANCES” USING HAAR MOTHER WAVELET TRANSFORM (SIDI CHENNANE, MOROCCO

    Directory of Open Access Journals (Sweden)

    Bakkali Saad

    2008-06-01

    Full Text Available Wavelet transforms originated in geophysics in the early 1980s for the analysis of seismic signals. Since then, significant mathematical advances in wavelet theory have enabled a suite of applications in diverse fields. In geophysics, the power of wavelets for analysis of non stationary processes that contain multiscale features, detection
    of singularities, analysis of transient phenomena, fractal and multifractal processes, and signal compression is now being exploited for the study of several processes including resistivity surveys. The present paper deals with denoising Moroccan phosphate "disturbances" resistivity data? map using the Haar wavelet mother transform method. The results show a significant suppression of noise and a very good smoothing and recovery of resistivity anomalies.

  9. From cardinal spline wavelet bases to highly coherent dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    Andrle, Miroslav; Rebollo-Neira, Laura [Aston University, Birmingham B4 7ET (United Kingdom)

    2008-05-02

    Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation. (fast track communication)

  10. Wavelet transform as a new approach to the enhancement of signal-to-noise ratio in anodic stripping voltammetry.

    Science.gov (United States)

    Prikler, Simon; Einax, Jürgen W

    2009-11-01

    De-noising signals is a frequent aim achieved by signal processing in analytical chemistry. The purpose is to enable the detection of trace concentrations of analytes. The limit of detection is defined as the lowest amount of analyte that still causes signals greater than the background noise. Appropriate de-noising decreases only the noise and maintains the measurement signal, so that signal-to-noise ratios are enhanced. One adequate mean of signal processing for this purpose is wavelet transform, which still is not a common tool in analytical chemistry. In this paper, the ability of de-noising by wavelet transform is shown for measurements in anodic stripping voltammetry using a hanging mercury drop electrode. The calculation of limits of detection and signal-to-noise ratios on the basis of peak-to-peak noise is exercised to quantify the performance of de-noising. Furthermore, signal shape with regard of easing the application of base lines is discussed. Different wavelet functions are used, and the results are compared also to Fourier transform. Coiflet2 was found out to reduce noise by the factor of 330 and is proposed as the adequate wavelet function for voltammetric and similar signals.

  11. Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques

    Directory of Open Access Journals (Sweden)

    K.C.Deekshit Kompella

    2017-03-01

    Full Text Available About sixty percent of the power in industries is consumed by induction machines, which implies induction machines are an integral part of industries. Even though these motors are stalwart and rugged in construction, they often experiences faults due to long time usage without maintenance. Bearing damage accounts 40% in the total faults and cause severe damage to the machine if unnoticed at nascent stage. So these faults should be continuously monitored for efficient operation, otherwise may cause severe damage to the machine. Conventional vibration monitoring is difficult due to requirement of high manpower and costly sensors. So motor current signature analysis (MCSA is widely used for detection and localization of these faults. In this paper, the bearing faults are estimated by means of current frequency spectral subtraction using discrete wavelet transform. In addition to this, the current signature analysis after spectral subtraction is carried out using Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT and Wavelet Packet Decomposition (WPD and a comparative analysis is presented to estimate fault severity using statistical parameters. The proposed method is assessed based on current signatures obtained from a 2.2kW induction machine. The experimental results acknowledged the effectiveness of proposed method.

  12. Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

    Science.gov (United States)

    Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

    2012-12-01

    The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

  13. 3D wavelet-based codec for lossy compression of pre-scan-converted ultrasound video

    Science.gov (United States)

    Andrew, Rex K.; Stewart, Brent K.; Langer, Steven G.; Stegbauer, Keith C.

    1999-05-01

    We present a wavelet-based video codec based on a 3D wavelet transformer, a uniform quantizer/dequantizer and an arithmetic encoder/decoder. The wavelet transformer uses biorthogonal Antonini wavelets in the two spatial dimensions and Haar wavelets in the time dimensions. Multiple levels of decomposition are supported. The codec has been applied to pre-scan-converted ultrasound image data and does not produce the type of blocking artifacts that occur in MPEG- compressed video. The PSNR at a given compression rate increases with the number of levels of decomposition: for our data at 50:1 compression, the PSNR increases from 18.4 dB at one level to 24.0 dB at four levels of decomposition. Our 3D wavelet-based video codec provides the high compression rates required to transmit diagnostic ultrasound video over existing low bandwidth links without introducing the blocking artifacts which have been demonstrated to diminish clinical utility.

  14. Tuning of a Wavelet Filter for Miniature Accelerometers Denoising based Joint Symbolic Dynamics (JSD Method

    Directory of Open Access Journals (Sweden)

    Ioana Raluca EDU

    2015-06-01

    Full Text Available The paper exposes a wavelet filtering mechanism related to the noise suppression in the acceleration sensors, with direct application in the strap-down inertial navigation systems. The presented procedure is related to the actual trend in the inertial navigation field to use miniaturized inertial measurement units, which includes MEMS or NEMS sensors. Beside the already wavelet filtering used method, based on different thresholding mechanisms, the here proposed work refers to the use of an alternative tuning mechanism for the wavelet filters, based on the Joint Symbolic Dynamics (JSD method. The main idea of the proposed method is to process and analyze signals received from the sensors in the inertial measurement unit of the navigator by using the Wavelet transform until optimal levels of decomposition are established and the useful signals are achieved.

  15. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    Directory of Open Access Journals (Sweden)

    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  16. Heterogeneities characterization from a core image using the wavelet transform

    Science.gov (United States)

    Gaci, S.; Zaourar, N.; Ouadfeul, S.

    2012-04-01

    Core analysis provides valuable information about rocks properties in the sub-surface. In this paper, we suggest a new approach which goes beyond the conventional core analysis. It consists of investigating heterogeneities from core image using two-dimensional Brownian motion (2D-mBm) model. The latter allows to study processes whose regularity varies in space. Synthetic 2D-mBm paths are firstly generated using the kriging method. Then, these simulated paths are used to validate algorithms, developed for estimating Hölderian regularity functions, which are: FFT-based algorithms using the Morlet wavelet and the Mexican hat, and the multiple filter technique generalized to 2 dimensions (2D MFT). The results showed that the latter algorithm yields the best regularity estimates. Next, the suggested analysis is extended to digitalized image data of a core extracted from an Algerian borehole. It is demonstrated that the data exhibit a fractal behavior. In addition, the derived regularity maps can characterize the core heterogeneities. The lithological changes (faults, breaks, stratifications, etc.) are perfectly reflected by local variations of the Hölder exponent value. Keywords: core image, two-dimensional multifractional Brownian motion, fractal, regularity

  17. Analysis of Mold Friction in a Continuous Casting Using Wavelet Transform

    Science.gov (United States)

    Ma, Yong; Fang, Bohan; Ding, Qiqi; Wang, Fangyin

    2018-01-01

    Mold friction (MDF) is an important parameter reflecting the lubrication condition between the initial shell and the mold during continuous casting. In this article, based on practical MDF from the slab continuous casting driven by a mechanical vibration device, the characteristics of friction were analyzed by continuous wavelet transform (CWT) and discrete wavelet transform (DWT) in different casting conditions, such as normal casting, level fluctuation, and alarming of the temperature measurement system. The results show that the CWT of friction accurately captures the subtle changes in friction force, such as the periodic characteristic of MDF during normal casting and the disordered feature of MDF during level fluctuation. Most important, the results capture the occurrence of abnormal casting and display the friction frequency characteristics at this abnormal time. In addition, in this article, there are some abnormal casting conditions, and the friction signal is stable until there is a sudden large change when abnormal casting, such as split breakout and submerged entry nozzle breakage, occurs. The DWT has a good ability to capture the friction characteristics for such abnormal situations. In particular, the potential abnormal features of MDF were presented in advance, which provides strong support for identifying abnormal casting and even preventing abnormal casting.

  18. Detection on Structural Sudden Damage Using Continuous Wavelet Transform and Lipschitz Exponent

    Directory of Open Access Journals (Sweden)

    Bo Chen

    2015-01-01

    Full Text Available The degradation of civil engineering structures may lead to a sudden stiffness reduction in a structure and such a sudden damage will cause a discontinuity in the dynamic responses. The detection on structural sudden damage has been actively carried out in this study. The signal singularity of the acceleration responses with sudden stiffness reduction is characterized by the coefficients of continuous wavelet transform with fine scales. A detection approach based on the CWT is proposed in terms of the decomposed detail coefficients of continuous wavelet transform to detect the damage time instant and location. The Lipschitz exponent is mathematically used to estimate the local properties of certain function and is applied to reflect the damage severity. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the validity of the proposed detection approach 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 effects of both measurement noise intensity and frequency range on the damage detection are numerically investigated.

  19. Fault Classification and Location in Transmission Lines Using Traveling Waves Modal Components and Continuous Wavelet Transform (CWT

    Directory of Open Access Journals (Sweden)

    Farhad Namdari

    2016-06-01

    Full Text Available Accurate fault classification and localization are the bases of protection for transmission systems. This paper presents a new method for classifying and showing location of faults by travelling waves and modal analysis. In the proposed method, characteristics of different faults are investigated using Clarke transformation and initial current traveling wave; then, appropriate indices are introduced to identify different types of faults. Continuous wavelet transform (CWT is employed to extract information of current and voltage travelling waves. Fault location and classification algorithm is being designed according to wavelet transform coefficients relating to current and voltage modal components. The performance of the proposed method is tested for different fault conditions (different fault distance, different fault resistances, and different fault inception angles by using PSCAD and MATLAB with satisfactory results

  20. Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

    DEFF Research Database (Denmark)

    Bajric, Rusmir; Zuber, Ninoslav; Skrimpas, Georgios Alexandros

    2016-01-01

    with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First...... reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines......Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes...

  1. Evidence of self-organization in brain electrical activity using wavelet-based informational tools

    Science.gov (United States)

    Rosso, O. A.; Martin, M. T.; Plastino, A.

    2005-03-01

    In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEG data, provide one with valuable insights into the dynamics of neural activity. Twenty tonic-clonic secondary generalized epileptic records pertaining to eight patients have been analyzed. If the electromyographic activity is excluded the difference between the ictal and pre-ictal mean entropic values (ΔH=-) is negative in 95% of the cases (p-) is positive in 85% of the cases (p=0.0002). Thus during the seizure entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus in this kind of seizures triggers a self-organized brain state characterized by both order and maximal complexity.

  2. Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings

    Directory of Open Access Journals (Sweden)

    C.T. Yiakopoulos

    2002-01-01

    Full Text Available Vibration signals resulting from roller bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. The envelope detection or demodulation methods have been established as the dominant analysis methods for this purpose, since they can separate the useful part of the signal from its redundant contents. The paper proposes a new effective demodulation method, based on the wavelet transform. The method fully exploits the underlying physical concepts of the modulation mechanism, present in the vibration response of faulty bearings, using the excellent time-frequency localization properties of the wavelet analysis. The choice of the specific wavelet family is marginal to their overall effect, while the necessary number of wavelet levels is quite limited. Experimental results and industrial measurements for three different types of bearing faults confirm the validity of the overall approach.

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

    Science.gov (United States)

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

    2017-10-01

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

  4. Discovery of a Strongly-Interrelated Gene Network in Corals under Constant Darkness by Correlation Analysis after Wavelet Transform on Complex Network Model

    OpenAIRE

    Longlong Liu; Jieqiong Qu; Xilong Zhou; Xuefeng Liu; Zhaobao Zhang; Xumin Wang; Tao Liu; Guiming Liu

    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 t...

  5. Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    VANCEA, F.

    2014-11-01

    Full Text Available The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.

  6. Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface.

    Science.gov (United States)

    John, Agnes Aruna; Subramanian, Aruna Priyadharshni; Jaganathan, Saravana Kumar; Sethuraman, Balasubramanian

    2015-01-01

    To process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate. The subject condition was identified using R-peak detection based on discrete wavelet transform followed by a Bayes classifier that classifies the ECG signals. The GUI was designed to display the ECG signal plot. Obtained from MIT database 18 patients had normal heart rate and 9 patients had abnormal heart rate; 14.81% of the patients suffered from tachycardia and 18.52% of the patients have bradycardia. The proposed GUI display was found useful to analyze the digitized ECG signal by a non-technical user and may help in diagnostics. Further improvement can be done by employing field programmable gate array for the real time processing of cardiac signals. Copyright © 2015 Cardiological Society of India. Published by Elsevier B.V. All rights reserved.

  7. Digital mammography: hybrid four-channel wavelet transform for microcalcification segmentation.

    Science.gov (United States)

    Qian, W; Clarke, L P; Song, D; Clark, R A

    1998-05-01

    The authors evaluated an algorithm for the automatic segmentation of microcalcification clusters (MCCs) at digital mammography. Two- and four-channel wavelet transforms were evaluated to determine whether sensitivity in the detection of MCCs can be improved and if the selective reconstruction of the higher-order M2 subimages allows better preservation of the segmented MCCs, which is required for their classification. The hybrid method involved the use of a nonlinear filter for image noise suppression coupled with wavelet transforms for image decomposition and an adaptive method for selective subimage reconstruction as a basis for segmentation of MCCs. The two- and four-channel wavelet transforms were implemented with different filter bank structures (i.e., polyphase quadrature mirror filters [QMFs], tree structure, and lattice structure) to determine if their computational efficiency can be improved while retaining properties such as near-perfect reconstruction. The hybrid wavelet transforms were applied to a common image database of biopsy-proved MCCs (100 images, 105-micron resolution, 12 bits deep; 52 cases with at least one MCC of varying subtlety [46 malignant and six benign cases] and eight normal cases). The two- and four-channel wavelet transforms yielded sensitivities of 93% and 94% and false-positive (PP) detection rates of 1.58 and 1.35 MCCs per image, respectively. The lattice structure provided greater than fivefold improvement in computational speed compared to the polyphase QMF structure, particularly for the higher order of channels (M = 4). The four-channel wavelet transform provided better sensitivity and FP detection rates and greater image detail preservation for the segmented MCCs.

  8. Numerical implementation of wavelet and fuzzy transform IFOC for three-phase induction motor

    DEFF Research Database (Denmark)

    Padamanaban, Sanjeevi Kumar; Daya, J.L. Febin; Blaabjerg, Frede

    2016-01-01

    This article elaborates the numerical implementation of a novel, indirect field-oriented control (IFOC) for induction motor drive by wave-let discrete transform/fuzzy logic interface system unique combination. The feedback (speed) error signal is a mixed component of multiple low and high...... frequencies. Further, these signals are decomposed by the discrete wave-let transform (WT), then fuzzy logic (FL) generates the scaled gains for the proportional-integral (P-I) controller parameters. This unique combination improves the high precision speed control of induction motor during both transient...

  9. Cutting force response in milling of Inconel: analysis by wavelet and Hilbert-Huang Transforms

    Directory of Open Access Journals (Sweden)

    Grzegorz Litak

    Full Text Available We study the milling process of Inconel. By continuously increasing the cutting depth we follow the system response and appearance of oscillations of larger amplitude. The cutting force amplitude and frequency analysis has been done by means of wavelets and Hilbert-Huang transform. We report that in our system the force oscillations are closely related to the rotational motion of the tool and advocate for a regenerative mechanism of chatter vibrations. To identify vibrations amplitudes occurrence in time scale we apply wavelet and Hilbert-Huang transforms.

  10. Adaptive Wavelet Scale Selection-based Method for Separating Respiration and Heartbeat in Bio-radars

    Directory of Open Access Journals (Sweden)

    Hu Xikun

    2016-10-01

    Full Text Available Extracting periodic heartbeat signals based on the traditional Fourier transform using a noncontact bio-radar is difficult because chest displacements caused by the heart are much smaller than those caused by respiration. Normally, they can be separated using the continuous wavelet transform; however, the miniscule difference of wavelet scale selection under different conditions may influence the separation performance to some extent. To solve this problem, this study proposes a method based on signal-to-noise ratio calibration to adaptively select the Morletdyadic wavelet scales and then separate the heartbeat signal from the respiration one using the selected scales, which can be applied to detect vital signs of different conditions. The experimental results have exhibited the accuracy and feasibility of the proposed method.

  11. A speech recognition system based on hybrid wavelet network including a fuzzy decision support system

    Science.gov (United States)

    Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri

    2015-02-01

    This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

  12. ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation

    Directory of Open Access Journals (Sweden)

    Sarunya Kanjanawattana

    2017-07-01

    Full Text Available Image classification plays a vital role in many areas of study, such as data mining and image processing; however, serious problems collectively referred to as the course of dimensionality have been encountered in previous studies as factors that reduce system performance. Furthermore, we also confront the problem of different graph characteristics even if graphs belong to same types. In this study, we propose a novel method of graph-type classification. Using our approach, we open up a new solution of high-dimensional images and address problems of different characteristics by converting graph images to one dimension with a discrete Fourier transformation and creating numeric datasets using wavelet and Hough transformations. Moreover, we introduce a new classifier, which is a combination between artificial neuron networks (ANNs and support vector machines (SVMs, which we call ANNSVM, to enhance accuracy. The objectives of our study are to propose an effective graph-type classification method that includes finding a new data representative used for classification instead of two-dimensional images and to investigate what features make our data separable. To evaluate the method of our study, we conducted five experiments with different methods and datasets. The input dataset we focused on was a numeric dataset containing wavelet coefficients and outputs of a Hough transformation. From our experimental results, we observed that the highest accuracy was provided using our method with Coiflet 1, which achieved a 0.91 accuracy.

  13. A parallel 3-D discrete wavelet transform architecture using pipelined lifting scheme approach for video coding

    Science.gov (United States)

    Hegde, Ganapathi; Vaya, Pukhraj

    2013-10-01

    This article presents a parallel architecture for 3-D discrete wavelet transform (3-DDWT). The proposed design is based on the 1-D pipelined lifting scheme. The architecture is fully scalable beyond the present coherent Daubechies filter bank (9, 7). This 3-DDWT architecture has advantages such as no group of pictures restriction and reduced memory referencing. It offers low power consumption, low latency and high throughput. The computing technique is based on the concept that lifting scheme minimises the storage requirement. The application specific integrated circuit implementation of the proposed architecture is done by synthesising it using 65 nm Taiwan Semiconductor Manufacturing Company standard cell library. It offers a speed of 486 MHz with a power consumption of 2.56 mW. This architecture is suitable for real-time video compression even with large frame dimensions.

  14. Mammographic image enhancement using wavelet transform and homomorphic filter

    OpenAIRE

    Majidi, F.; Latif, A. M.; J. Malakouti; H Kasayi

    2015-01-01

    Mammography is the most effective method for the early diagnosis of breast cancer diseases. As mammographic images contain low signal to noise ratio and low contrast, it becomes too difficult for radiologists to analyze mammogram. To deal with the above stated problems, it is very important to enhance the mammographic images using image processing methods. This paper introduces a new image enhancement approach for mammographic images which uses the modified mathematical morphology, wavelet tr...

  15. Morphology-based wavelet features and multiple mother wavelet strategy for spike classification in EEG signals.

    Science.gov (United States)

    Zhou, Jing; Schalkoff, Robert J; Dean, Brian C; Halford, Jonathan J

    2012-01-01

    New wavelet-derived features and strategies that can improve autonomous EEG classifier performance are presented. Various feature sets based on the morphological structure of wavelet subband coefficients are derived and evaluated. The performance of these new feature sets is superior to Guler's classic features in both sensitivity and specificity. In addition, the use of (scalp electrode) spatial information is also shown to improve EEG classification. Finally, a new strategy based upon concurrent use of several mother wavelets is shown to result in increased sensitivity and specificity. Various attempts at reducing feature vector dimension are shown. A non-parametric method, k-NNR, is implemented for classification and 10-fold cross-validation is used for assessment.

  16. Epileptic Seizure Detection in Eeg Signals Using Multifractal Analysis and Wavelet Transform

    Science.gov (United States)

    Uthayakumar, R.; Easwaramoorthy, D.

    This paper explores the three different methods to explicitly recognize the healthy and epileptic EEG signals: Modified, Improved, and Advanced forms of Generalized Fractal Dimensions (GFD). The newly proposed scheme is based on GFD and the discrete wavelet transform (DWT) for analyzing the EEG signals. First EEG signals are decomposed into approximation and detail coefficients using DWT and then GFD values of the original EEGs, approximation and detail coefficients are computed. Significant differences are observed among the GFD values of the healthy and epileptic EEGs allowing us to classify seizures with high accuracy. It is shown that the classification rate is very less accurate without DWT as a preprocessing step. The proposed idea is illustrated through the graphical and statistical tools. The EEG data is further tested for linearity by using normal probability plot and we proved that epileptic EEG had significant nonlinearity whereas healthy EEG distributed normally and similar to Gaussian linear process. Therefore, we conclude that the GFD and the wavelet decomposition through DWT are the strong indicators of the state of illness of epileptic patients.

  17. Assessment of Fluctuation Patterns Similarity in Temperature and Vapor Pressure Using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    A. Araghi

    2014-12-01

    Full Text Available Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. Wavelet transform is a mathematical based powerful method which has been widely used in signal processing and time series analysis in recent years. In this research, trend and main periodic patterns similarity in temperature and vapor pressure has been studied in Babolsar, Tehran and Shahroud synoptic stations during 55 years period (from 1956 to 2010, using wavelet method and the sequential Mann-Kendall trend test. The results show that long term fluctuation patterns in temperature and vapor pressure have more correlations in the arid and semi-arid climates, as well as short term oscillation patterns in temperature and vapor pressure in the humid climates, and these dominant periods increase with the aridity of region.

  18. Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation

    Science.gov (United States)

    Witwit, Wasnaa; Zhao, Yifan; Jenkins, Karl; Zhao, Yitian

    2017-03-01

    An image resolution enhancement approach based on discrete wavelet transform (DWT) and new edge-directed interpolation (NEDI) for degraded satellite images by geometric distortion to correct the errors in image geometry and recover the edge details of directional high-frequency subbands is proposed. The observed image is decomposed into four frequency subbands through DWT, and then the three high-frequency subbands and the observed image are processed with NEDI. To better preserve the edges and remove potential noise in the estimated high-frequency subbands, an adaptive threshold is applied to process the estimated wavelet coefficients. Finally, the enhanced image is reconstructed by applying inverse DWT. Four criteria are introduced, aiming to better assess the overall performance of the proposed approach for different types of satellite images. A public satellite images data set is selected for the validation purpose. The visual and quantitative results show the superiority of the proposed approach over the conventional and state-of-the-art image resolution enhancement techniques.

  19. Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

    Science.gov (United States)

    Sutha, P; Jayanthi, V E

    2017-12-08

    Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with

  20. [The comparison of the extraction of beta wave from EEG between FFT and wavelet transform].

    Science.gov (United States)

    Wang, Haowen; Qian, Zhiyu; Li, Hongjing; Chen, Chunxiao; Ding, Shangwen

    2013-08-01

    In order to choose a fast and efficient real-time method in beta wave information extraction, we compared the result and the efficiency of the information separation of both fast Fourier transform (FFT) and wavelet transform of EEG beta band in the present paper. Our work provides the basis for the EEG data come from the real-time health assessment of 3DTV. We took the EEGs of 5 healthy volunteers before, after and during the process of watching 3DTV and meanwhile recorded the results. The trends of the relative energy and the time cost of two methods were compared by using both the FFT and wavelet packet transform (WPT) which was to extract the feature of EEG beta wave. It demonstrated that (1) Results of the two methods were consistent in the trends of watching 3DTV; (2) Results of the differences in two methods were consistent before and after watching 3DTV; (3) FFT took less time than the wavelet transform in the same case. It is concluded that the results of both FFT and Wavelet transform are consistent in feature extraction of EEG, and a fast method to work with the large quantities of EEG data obtained in the experiments can be offered in the future.

  1. Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir

    Science.gov (United States)

    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).

  2. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles.

    Science.gov (United States)

    Chowdhury, Suman Kanti; Nimbarte, Ashish D; Jaridi, Majid; Creese, Robert C

    2013-10-01

    Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue under dynamic repetitive conditions. Ten human participants performed 40min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12-23Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the "Rbio3.1" wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Heath monitoring of a glass transfer robot in the mass production line of liquid crystal display using abnormal operating sounds based on wavelet packet transform and artificial neural network

    Science.gov (United States)

    Kim, Eui-Youl; Lee, Young-Joon; Lee, Sang-Kwon

    2012-07-01

    This paper presents the fault detect method of a moving transfer robot in the mass production line of liquid crystal display (LCD) manufacturers based on the wavelet packet transform (WPT) for feature extraction and the artificial neural network (ANN) for fault classification. Most of fault detection methods in a mechanical system have been researched based on the vibration signal. Unlike the existing methodologies, this study aims to minimize the uncertainty of a field engineer's decision making process for determining whether a fault is present or not based on the human auditory perception by developing a fault diagnosis system that uses the abnormal operating sound radiated from a moving transfer robot as a source signal. Abnormal operating sound radiated from a moving transfer robot has been used for this work instead of other source signals such as vibration, acoustic emission, electrical signal, etc. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone despite a relatively low sensitivity. In the application of ANN, since it is important to minimize the error of trained ANN in terms of the accuracy of fault diagnosis logic, in the paper, the number of input and target data samples was increased through a regeneration process based on statistical properties, and then the uncorrelated nodes in the input vector were also removed to improve the orthogonality of the input vector based on the entropy based feature selection method. Consequently, it can be concluded that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

  4. Multispectral image pansharpening based on the contourlet transform

    Energy Technology Data Exchange (ETDEWEB)

    Amro, Israa; Mateos, Javier, E-mail: iamro@correo.ugr.e, E-mail: jmd@decsai.ugr.e [Departamento de Ciencias de la Computacion e I.A., Universidad de Granada, 18071 Granada (Spain)

    2010-02-01

    Pansharpening is a technique that fuses the information of a low resolution multispectral image (MS) and a high resolution panchromatic image (PAN), usually remote sensing images, to provide a high resolution multispectral image. In the literature, this task has been addressed from different points of view being one of the most popular the wavelets based algorithms. Recently, the contourlet transform has been proposed. This transform combines the advantages of the wavelets transform with a more efficient directional information representation. In this paper we propose a new pansharpening method based on contourlets, compare with its wavelet counterpart and assess its performance numerically and visually.

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

    Science.gov (United States)

    Han, Guoqiang; Xu, Zhijun

    2016-08-01

    Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

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

    Science.gov (United States)

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

    2015-02-01

    Studies conducted by the World Health Organization (WHO) indicate that over one billion suffer from neurological disorders worldwide, and lack of efficient diagnosis procedures affects their therapeutic interventions. Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. A wavelet-based method for multispectral face recognition

    Science.gov (United States)

    Zheng, Yufeng; Zhang, Chaoyang; Zhou, Zhaoxian

    2012-06-01

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

  8. Fault location in underground cables using ANFIS nets and discrete wavelet transform

    Directory of Open Access Journals (Sweden)

    Shimaa Barakat

    2014-12-01

    Full Text Available This paper presents an accurate algorithm for locating faults in a medium voltage underground power cable using a combination of Adaptive Network-Based Fuzzy Inference System (ANFIS and discrete wavelet transform (DWT. The proposed method uses five ANFIS networks and consists of 2 stages, including fault type classification and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents. Other four ANFIS networks are utilized to pinpoint the faults (one for each fault type. Four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on the cable. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances.

  9. Trabecular Bone Image Segmentation Using Wavelet and Marker-Controlled Watershed Transformation

    Directory of Open Access Journals (Sweden)

    Wafa Abid Fourati

    2014-01-01

    Full Text Available This paper presents a new strategy for the segmentation of trabecular bone image. This kind of image is acquired with microcomputed tomography (micro-CT to assess bone microarchitecture based chiefly on bone mineral density (BMD measurements to improve fracture risk prediction. Disease osteoporosis can be predicted from features of CT image where a bone region may consist of several disjoint pieces. It relies on a multiresolution representation of the image by the wavelet transform to compute the multiscale morphological gradient. The coefficients of detail found at the different scales are used to determine the markers and homogeneous regions that are extracted with the watershed algorithm. The method reduces the tendency of the watershed algorithm to oversegment and results in closed homogeneous regions. The performance of the proposed segmentation scheme is presented via experimental results obtained with a broad series of images.

  10. Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis

    Directory of Open Access Journals (Sweden)

    SU, H.

    2011-08-01

    Full Text Available Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.

  11. On the Daubechies-based wavelet differentiation matrix

    Science.gov (United States)

    Jameson, Leland

    1993-01-01

    The differentiation matrix for a Daubechies-based wavelet basis is constructed and superconvergence is proven. That is, it will be proven that under the assumption of periodic boundary conditions that the differentiation matrix is accurate of order 2M, even though the approximation subspace can represent exactly only polynomials up to degree M-1, where M is the number of vanishing moments of the associated wavelet. It is illustrated that Daubechies-based wavelet methods are equivalent to finite difference methods with grid refinement in regions of the domain where small-scale structure is present.

  12. A de-noising method using the improved wavelet threshold function based on noise variance estimation

    Science.gov (United States)

    Liu, Hui; Wang, Weida; Xiang, Changle; Han, Lijin; Nie, Haizhao

    2018-01-01

    The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

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

    Science.gov (United States)

    DelMarco, Stephen

    2016-05-01

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

  14. Serial identification of EEG patterns using adaptive wavelet-based analysis

    Science.gov (United States)

    Nazimov, A. I.; Pavlov, A. N.; Nazimova, A. A.; Grubov, V. V.; Koronovskii, A. A.; Sitnikova, E.; Hramov, A. E.

    2013-10-01

    A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.

  15. Fractional M-band dual tree complex wavelet transform for digital ...

    Indian Academy of Sciences (India)

    In this paper, a novel digital watermarking scheme using fractional M-band dual tree complex wavelet transform (Fr-M-band-DT-CWT) is proposed. High frequency channels have wide bandwidth and low frequency channels have narrow bandwidth. These characteristics are suitable for analysing low frequency signal, but ...

  16. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  17. Perceptual Wavelet packet transform based Wavelet Filter Banks Modeling of Human Auditory system for improving the intelligibility of voiced and unvoiced speech: A Case Study of a system development

    OpenAIRE

    Ranganadh Narayanam

    2015-01-01

    The objective of this project is to discuss a versatile speech enhancement method based on the human auditory model. In this project a speech enhancement scheme is being described which meets the demand for quality noise reduction algorithms which are capable of operating at a very low signal to noise ratio. We will be discussing how proposed speech enhancement system is capable of reducing noise with little speech degradation in diverse noise environments. In this model to reduce the resi...

  18. Motor-operated valve fault detection using Wavelet transform; Deteccao de falhas em valvulas moto-operadas utilizando transformada de Wavelet

    Energy Technology Data Exchange (ETDEWEB)

    Carneiro, Alvaro Luiz G.; Silva, Aucyone A. da; Ting, Daniel Kao S. [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Sao Paulo, SP (Brazil)]. E-mail: carneiro@net.ipen.br; Upadhyaya, Belle R. [The University of Tennessee, Knoxville, TN (United States). Dept. of Nuclear Engineering

    2002-07-01

    The reliability question of the components, specifically the motor operated valves related to the security systems, became one of the most important point to be investigated in the nuclear plants, considering security and extension life of the plant. Therefore, the necessity of improvements in monitoring and diagnosis methods started to be the extreme relevance in the maintenance predictive field, establishing as main goal the reliability and readiness of the components systems. Particularly in nuclear plants, the predictive maintenance contributes in the security factor in order to diagnosis in advance the occurrence of a possible failure, preventing catastrophic situations. Moreover, the predictive maintenance presents a contribution on the economic point, establishing a better maintenance programming, reducing unexpected shut down. This work presents a monitoring and diagnosis method for motor operated valves, using the Wavelet Transform, that during the last decade, has been successfully applied in many areas of science and engineering. The method is based on the analysis of the power signatures of the engine, through the measures of currents and voltages of the phases, during the closing and opening stroke time of the valve. The Wavelet transform, is applied to characterize the transients phenomena or trending to failure, allowing to point out the events in the time and frequency domain, correlating them with the failures situations in the system, identifying them in the incipient state. (author)

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

    Science.gov (United States)

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

    2010-01-01

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

  20. On the Use of Wavelet Transform for Quench Precursors Characterisation in the LHC Superconducting Dipole Magnets

    CERN Document Server

    Calvi, M; Bottura, L; Masi, A; Siemko, A

    2006-01-01

    Premature training quenches are caused by transient energy released within the magnet coil while it is energized. Signals recorded across the so-called quench antenna carry information about these disturbances. A new method for identifying and characterizing those events is proposed, which applies the wavelet transform approach to the recorded signals. Such an approach takes into account the time of occurrence as well as frequency content of the events. The choice of the optimal mother wavelet is discussed, and the results obtained from the application of the method to actual signals are given. The criteria to recognize the interesting events are presented as well as the methodology to classify their global behavior.

  1. Wavelet transform of generalized functions in K ′{Mp} spaces

    Indian Academy of Sciences (India)

    defined as. Daτbψ(t) = ψb,a(t) = a. −n/2 ψ ( t − b a ). , t ∈ Rn. (1). The wavelet transform W(b,a) of an element f ∈ L2(Rn) with respect to the wavelet ψb,a(t) ..... bounded function on R. Theorem 9. Suppose ψ ∈ L1(Rn) is real valued radial and satisfies ∫ ∞. 0. [ ˆψ(tξ)]2dt/t = 1 if ξ ∈ Rn\\{0}. If f ∈ L2(Rn) and fε,δ(x) = ∫ δ.

  2. Wavelets in neuroscience

    CERN Document Server

    Hramov, Alexander E; Makarov, Valeri A; Pavlov, Alexey N; Sitnikova, Evgenia

    2015-01-01

    This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural ...

  3. Nondestructive Damage Assessment of Composite Structures Based on Wavelet Analysis of Modal Curvatures: State-of-the-Art Review and Description of Wavelet-Based Damage Assessment Benchmark

    Directory of Open Access Journals (Sweden)

    Andrzej Katunin

    2015-01-01

    Full Text Available The application of composite structures as elements of machines and vehicles working under various operational conditions causes degradation and occurrence of damage. Considering that composites are often used for responsible elements, for example, parts of aircrafts and other vehicles, it is extremely important to maintain them properly and detect, localize, and identify the damage occurring during their operation in possible early stage of its development. From a great variety of nondestructive testing methods developed to date, the vibration-based methods seem to be ones of the least expensive and simultaneously effective with appropriate processing of measurement data. Over the last decades a great popularity of vibration-based structural testing has been gained by wavelet analysis due to its high sensitivity to a damage. This paper presents an overview of results of numerous researchers working in the area of vibration-based damage assessment supported by the wavelet analysis and the detailed description of the Wavelet-based Structural Damage Assessment (WavStructDamAs Benchmark, which summarizes the author’s 5-year research in this area. The benchmark covers example problems of damage identification in various composite structures with various damage types using numerous wavelet transforms and supporting tools. The benchmark is openly available and allows performing the analysis on the example problems as well as on its own problems using available analysis tools.

  4. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.

    Science.gov (United States)

    Barzegar, Rahim; Fijani, Elham; Asghari Moghaddam, Asghar; Tziritis, Evangelos

    2017-12-01

    Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R2), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. CHARACTERIZATION OF RENAL BLOOD FLOW REGULATION BASED ON WAVELET COEFFICIENTS

    DEFF Research Database (Denmark)

    Pavlov, A.N.; Pavlova, O.N.; Mosekilde, Erik

    2010-01-01

    The purpose of this study is to demonstrate the possibility of revealing new characteristic features of renal blood flow autoregulation in healthy and pathological states through the application of discrete wavelet transforms to experimental time series for normotensive and hypertensive rats....... A reduction in the variability of the wavelet coefficients in hypertension is observed at both the microscopic level of the blood flow in efferent arterioles of individual nephrons and at the macroscopic level of the blood pressure in the main arteries. The reduction is manifest in both of the main frequency...

  6. Classification of epileptic EEG using neural network and wavelet transform

    Science.gov (United States)

    Petrosian, Arthur A.; Homan, Richard; Prokhorov, Danil; Wunsch, Donald C., II

    1996-10-01

    One of the major contributions of electroencephalography has been its application in the diagnosis and clinical evaluation of epilepsy. The interpretation of the EEG is achieved through visual inspection by a trained electroencephalographer. However, descriptions of rules used during the visual analysis of data are often subjective and can vary from one reader to another. Computerized methods are a means to standardize this process. In recent years, much effort has been made to develop such methods that can characterize different interictal, ictal, and postictal stages. the main issue of whether there exists a preictal phenomenon remains unresolved. In the present study we address this issue making use of specifically designed and trained recurrent neural networks in conjunction with signal wavelet decomposition technique. The purpose of this combined consideration was to demonstrate the potential for seizure prediction by up to several minutes prior to its onset.

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

    Directory of Open Access Journals (Sweden)

    Vu Dang Hoang

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  10. Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

    Directory of Open Access Journals (Sweden)

    Imaouchen Yacine

    2015-01-01

    Full Text Available To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

  11. On exploiting wavelet bases in statistical region-based segmentation

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due to ex...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....

  12. Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning

    Science.gov (United States)

    García Plaza, E.; Núñez López, P. J.

    2018-01-01

    On-line monitoring of surface finish in machining processes has proven to be a substantial advancement over traditional post-process quality control techniques by reducing inspection times and costs and by avoiding the manufacture of defective products. This study applied techniques for processing cutting force signals based on the wavelet packet transform (WPT) method for the monitoring of surface finish in computer numerical control (CNC) turning operations. The behaviour of 40 mother wavelets was analysed using three techniques: global packet analysis (G-WPT), and the application of two packet reduction criteria: maximum energy (E-WPT) and maximum entropy (SE-WPT). The optimum signal decomposition level (Lj) was determined to eliminate noise and to obtain information correlated to surface finish. The results obtained with the G-WPT method provided an in-depth analysis of cutting force signals, and frequency ranges and signal characteristics were correlated to surface finish with excellent results in the accuracy and reliability of the predictive models. The radial and tangential cutting force components at low frequency provided most of the information for the monitoring of surface finish. The E-WPT and SE-WPT packet reduction criteria substantially reduced signal processing time, but at the expense of discarding packets with relevant information, which impoverished the results. The G-WPT method was observed to be an ideal procedure for processing cutting force signals applied to the real-time monitoring of surface finish, and was estimated to be highly accurate and reliable at a low analytical-computational cost.

  13. 3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms.

    Science.gov (United States)

    Dicente Cid, Yashin; Muller, Henning; Platon, Alexandra; Poletti, Pierre; Depeursinge, Adrien

    2017-02-06

    Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3-D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3-D content. One of the most common methods to analyze 3-D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3-D. Current state-of-the- art techniques face many challenges when working with 3-D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3-D Riesz- wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3-D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher- order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3-D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which is better than all other techniques that are published and tested on the same database.

  14. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    National Research Council Canada - National Science Library

    Rosenblatt, Mariel; Figliola, Alejandra; Paccosi, Gustavo; Serrano, Eduardo; Rosso, Osvaldo

    2014-01-01

      The characterization of the dynamics associated with electroencephalogram (EEG) signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed...

  15. Use of Wavelet Transform to Detect Compensated and Decompensated Stages in the Congestive Heart Failure Patient

    Directory of Open Access Journals (Sweden)

    Pratibha Sharma

    2017-09-01

    Full Text Available This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state of the patient but with lack of retention of fluids in lungs, whereas decompensated state leads to unstable state of the patient with lots of fluid retention in the lungs, where the patient needs medication. Acoustic signals from the heart and the lung were analyzed using wavelet transforms to measure changes in the CHF patient’s status from the decompensated to compensated and vice versa. Measurements were taken on CHF patients diagnosed to be in compensated and decompensated states by using a digital stethoscope and electrocardiogram (ECG in order to monitor their progress in the management of their disease. Analysis of acoustic signals of the heart due to the opening and closing of heart valves as well as the acoustic signals of the lungs due to respiration and the ECG signals are presented. Fourier, short-time Fourier, and wavelet transforms are evaluated to determine the best method to detect shifts in the status of a CHF patient. The power spectra obtained through the Fourier transform produced results that differentiate the signals from healthy people and CHF patients, while the short-time Fourier transform (STFT technique did not provide the desired results. The most promising results were obtained by using wavelet analysis. Wavelet transforms provide better resolution, in time, for higher frequencies, and a better resolution, in frequency, for lower frequencies.

  16. Mono-modal feature extraction for bonding quality detection of explosive clad structure with optimized dual-tree complex wavelet transform

    Science.gov (United States)

    Si, Yue; Zhang, Zhousuo; Wang, Hongfang; Yuan, Feichen

    2017-03-01

    Bonding quality detection of explosive clad structure is significant to prevent catastrophic accidents. Multi-modal features related to bonding quality are contained in structural vibration response signal. Different modal feature has different sensitivity to the bonding quality. Extracting the desired mono-modal feature from the vibration response signal is necessary. Due to the mode aliasing easily appeared in the process of extracting the desired mono-modal feature, there is no effective method for this task. Dual-tree complex wavelet with attractive properties such as shift invariance and reduced spectral aliasing may provide a better way to extract the mono-modal feature. However, the fixed basis functions independent of the analyzed signal may weak the advantage of the method and even reduce the accuracy of detection result. To overcome this shortcoming, a technique called optimized dual-tree complex wavelet transform (ODTCWT) is proposed in this paper. Based on the analyzed signal, the optimized dual-tree complex wavelet basis function is constructed by searching for the proper parameters of vanishing moment K and the order of filter L. The optimized dual-tree complex wavelet with improved wavelet filters can best matched the modal frequencies of the analyzed signal. The ODTCWT can extract the mono-modal feature from vibration response signal with lower mode aliasing. The feasibility and effectiveness of the method of constructing ODTCWT is illustrated by the simulated signal. The proposed ODTCWT is combined with time entropy to detecting bonding quality of explosive clad pipes. For comparison, un-optimized dual-tree complex wavelet transform (UODTCWT), second-generation wavelet transform (SGWT) and band-pass filter (BPF) are also used for this task to demonstrate the validity of ODTCWT.

  17. Numerical implementation of wavelet and fuzzy transform IFOC for three-phase induction motor

    Directory of Open Access Journals (Sweden)

    Sanjeevikumar Padmanaban

    2016-03-01

    Full Text Available This article elaborates the numerical implementation of a novel, indirect field-oriented control (IFOC for induction motor drive by wave-let discrete transform/fuzzy logic interface system unique combination. The feedback (speed error signal is a mixed component of multiple low and high frequencies. Further, these signals are decomposed by the discrete wave-let transform (WT, then fuzzy logic (FL generates the scaled gains for the proportional-integral (P-I controller parameters. This unique combination improves the high precision speed control of induction motor during both transient as well as steady-state conditions. Numerical simulation model is implemented with proposed control scheme using Matlab/Simulink software and obtained results confirm the expectation.

  18. [The application of adaptive algorithm and wavelet transform in the filtering of ECG signal].

    Science.gov (United States)

    Zhang, Jingzhou; Zhang, Guanglei; Dai, Guanzhong

    2006-10-01

    Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. These noises bring obstacle to the diagnosis of cardiovascular diseases. To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.

  19. Power-law behaviour evaluation from foreign exchange market data using a wavelet transform method

    Science.gov (United States)

    Wei, H. L.; Billings, S. A.

    2009-09-01

    Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A simple new statistical approach, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

  20. Concurrent Monitoring of Chip Formation and Prediction of Roundness in CNC Turning Using Wavelet Transform

    Science.gov (United States)

    Tangjitsitcharoen, Somkiat; Sassantiwong, Mumin

    2017-10-01

    The aim of this research is to investigate the chip formation, the roundness and the dynamic cutting forces in CNC turning process. The dynamic cutting forces are decomposed to classify the signals of the broken chip and the roundness. The Daubechies wavelet transform is utilized to identify those signals into different levels due to the different frequencies of itself. The experimentally obtained results showed that the decomposed cutting forces can be used to estimate the roundness under various cutting conditions.

  1. R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

    Directory of Open Access Journals (Sweden)

    Jeong-Seon Park

    2017-01-01

    Full Text Available Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs. In this paper, we present an R peak detection method using the wavelet transform (WT and a modified Shannon energy envelope (SEE for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

  2. R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

    Science.gov (United States)

    Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

    2017-01-01

    Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

  3. Use of muscle synergies and wavelet transforms to identify fatigue during squatting.

    Science.gov (United States)

    Smale, Kenneth B; Shourijeh, Mohammad S; Benoit, Daniel L

    2016-06-01

    The objective of this study was to supplement continuous wavelet transforms with muscle synergies in a fatigue analysis to better describe the combination of decreased firing frequency and altered activation profiles during dynamic muscle contractions. Nine healthy young individuals completed the dynamic tasks before and after they squatted with a standard Olympic bar until complete exhaustion. Electromyography (EMG) profiles were analyzed with a novel concatenated non-negative matrix factorization method that decomposed EMG signals into muscle synergies. Muscle synergy analysis provides the activation pattern of the muscles while continuous wavelet transforms output the temporal frequency content of the EMG signals. Synergy analysis revealed subtle changes in two-legged squatting after fatigue while differences in one-legged squatting were more pronounced and included the shift from a general co-activation of muscles in the pre-fatigue state to a knee extensor dominant weighting post-fatigue. Continuous wavelet transforms showed major frequency content decreases in two-legged squatting after fatigue while very few frequency changes occurred in one-legged squatting. It was observed that the combination of methods is an effective way of describing muscle fatigue and that muscle activation patterns play a very important role in maintaining the overall joint kinetics after fatigue. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Anisotropy in wavelet-based phase field models

    KAUST Repository

    Korzec, Maciek

    2016-04-01

    When describing the anisotropic evolution of microstructures in solids using phase-field models, the anisotropy of the crystalline phases is usually introduced into the interfacial energy by directional dependencies of the gradient energy coefficients. We consider an alternative approach based on a wavelet analogue of the Laplace operator that is intrinsically anisotropic and linear. The paper focuses on the classical coupled temperature/Ginzburg--Landau type phase-field model for dendritic growth. For the model based on the wavelet analogue, existence, uniqueness and continuous dependence on initial data are proved for weak solutions. Numerical studies of the wavelet based phase-field model show dendritic growth similar to the results obtained for classical phase-field models.

  5. Noise reduction of nuclear magnetic resonance (NMR) transversal data using improved wavelet transform and exponentially weighted moving average (EWMA)

    Science.gov (United States)

    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.

  6. Multiple-input multiple-output wavelet packet modulation based software-defined radio transceiver design

    OpenAIRE

    Cribbs, Michael R.

    2015-01-01

    Approved for public release; distribution is unlimited In this thesis, a software-defined radio (SDR) transmitter and receiver is developed using GNU Radio. The designed SDR multiplexes orthogonal subcarriers using wavelet packet modulation (WPM). WPM achieves subcarrier orthogonality by employing the inverse discrete wavelet packet transform (IDWPT) for the transmitter and discrete wavelet packet transform (DWPT) for the receiver. Realization concerns for the IDWPT and DWPT are discussed ...

  7. Application of wavelet-based multiple linear regression model to rainfall forecasting in Australia

    Science.gov (United States)

    He, X.; Guan, H.; Zhang, X.; Simmons, C.

    2013-12-01

    In this study, a wavelet-based multiple linear regression model is applied to forecast monthly rainfall in Australia by using monthly historical rainfall data and climate indices as inputs. The wavelet-based model is constructed by incorporating the multi-resolution analysis (MRA) with the discrete wavelet transform and multiple linear regression (MLR) model. The standardized monthly rainfall anomaly and large-scale climate index time series are decomposed using MRA into a certain number of component subseries at different temporal scales. The hierarchical lag relationship between the rainfall anomaly and each potential predictor is identified by cross correlation analysis with a lag time of at least one month at different temporal scales. The components of predictor variables with known lag times are then screened with a stepwise linear regression algorithm to be selectively included into the final forecast model. The MRA-based rainfall forecasting method is examined with 255 stations over Australia, and compared to the traditional multiple linear regression model based on the original time series. The models are trained with data from the 1959-1995 period and then tested in the 1996-2008 period for each station. The performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and correlation coefficient. The results show that the wavelet-based regression model provides considerably more accurate monthly rainfall forecasts for all of the selected stations over Australia than the traditional regression model.

  8. Wavelet-based analogous phase scintillation index for high latitudes

    Science.gov (United States)

    Ahmed, A.; Tiwari, R.; Strangeways, H. J.; Dlay, S.; Johnsen, M. G.

    2015-08-01

    The Global Positioning System (GPS) performance at high latitudes can be severely affected by the ionospheric scintillation due to the presence of small-scale time-varying electron density irregularities. In this paper, an improved analogous phase scintillation index derived using the wavelet-transform-based filtering technique is presented to represent the effects of scintillation regionally at European high latitudes. The improved analogous phase index is then compared with the original analogous phase index and the phase scintillation index for performance comparison using 1 year of data from Trondheim, Norway (63.41°N, 10.4°E). This index provides samples at a 1 min rate using raw total electron content (TEC) data at 1 Hz for the prediction of phase scintillation compared to the scintillation monitoring receivers (such as NovAtel Global Navigation Satellite Systems Ionospheric Scintillation and TEC Monitor receivers) which operate at 50 Hz rate and are thus rather computationally intensive. The estimation of phase scintillation effects using high sample rate data makes the improved analogous phase index a suitable candidate which can be used in regional geodetic dual-frequency-based GPS receivers to efficiently update the tracking loop parameters based on tracking jitter variance.

  9. Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

    Directory of Open Access Journals (Sweden)

    TjongWan Sen

    2009-11-01

    Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

  10. An improved method for broadband interferometric lightning location using wavelet transforms

    Science.gov (United States)

    Qiu, Shi; Zhou, Bi-Hua; Shi, Li-Hua; Dong, Wan-Sheng; Zhang, Yi-Jun; Gao, Tai-Chang

    2009-09-01

    The principle of VHF broadband interferometer for lightning observations is to extract the phase differences between a pair of RF signals at different frequency components, and thereafter, to compute the direction of the radiation source. However, the phase difference spectra are usually distorted by random noise, which directly affects the location accuracy and reliability. In order to suppress phase interference effectively, a phase filtering algorithm which combines circular correlation with translation-invariant denoising is proposed. Application of the algorithm to a segment of observational data shows that the proposed algorithm does a much better job in recovering the phase spectra than other techniques. Simulated data distorted by the addition of random noise are also used to illustrate the improvements of the method in resolving incident angle, which is compared with other wavelet-based thresholding techniques and the conventional Fourier transform-based cross-correlation method. Finally, this phase filtering algorithm is applied to mapping two natural lightning processes. Contrast analysis reveals that this algorithm could be utilized to retrieve well-defined paths which are not discerned by conventional method, and depict the branches more clearly and precisely.

  11. Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

    Directory of Open Access Journals (Sweden)

    Tjong Wan Sen

    2013-09-01

    Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

  12. A fast and memory efficient stationary wavelet transform for 3D cell segmentation

    Science.gov (United States)

    Padfield, Dirk R.

    2015-03-01

    Wavelet approaches have proven effective in many segmentation applications and in particular in the segmentation of cells, which are blob-like in shape. We build upon an established wavelet segmentation algorithm and demonstrate how to overcome some of its limitations based on the theoretical derivation of the compounding process of iterative convolutions. We demonstrate that the wavelet decomposition can be computed for any desired level directly without iterative decompositions that require additional computation and memory. This is especially important when dealing with large 3D volumes that consume significant amounts of memory and require intense computation. Our approach is generalized to automatically handle both 2D and 3D and also implicitly handles the anisotropic pixel size inherent in such datasets. Our results demonstrate a 28X improvement in speed and 8X improvement in memory efficiency for standard size 3D confocal image volumes without adversely affecting the accuracy.

  13. Procesamiento de datos mediante Wavelet para la modelación térmica de transformadores de potencia; Data processing using wavelet for power transformers thermal model

    Directory of Open Access Journals (Sweden)

    Rómulo Pérez

    2014-04-01

    Full Text Available En este trabajo las mediciones recabadas por una estación experimental instalada en un Transformador de 100 MVA de la Subestación Barquisimeto de Venezuela son procesadas para eliminar factores de ruido que introducen errores en la identificación de parámetros del modelo térmico para el cálculo de la temperatura superior del aceite. Se usa una metodología para el control de calidad y eliminación del ruido en las mediciones recabadas basada en experiencias propias y reforzadas con experiencias de reconocidos autores internacionales, la cual aplica la Transformada Discreta de Wavelet DWT para obtener datos que muestran buenos indicadores de calidad en las principales variables del modelo térmico, como lo son la corriente de carga, la temperatura ambiente y la temperatura del aceite superior. Finalmente se comparan los resultados de la modelación térmica antes y después de ser procesados los datos, donde se evidencia un notable incremento en la exactitud del modelo.  In this work measurement get of experimental station connected in a power transformer of 100 MVA in a Barquisimeto Substation in Venezuela are processing to eliminate noise that introduce mistake in the parameters identification for top oil temperature model calculation. A methodology based in your experiences with experiences of international authors for the control of quality and elimination of the noise in the successfully obtained measurements is used. It’s apply the Discreet Wavelet Transform (DWT to collect data that show good indicators of quality in the main values of the thermal model, as the load current, the ambient temperature and the top oil temperature. Finally is compared thermal model results after and beforedata processing, where at increase in the exactitude of the thermal model is demonstrated.

  14. Procesamiento de datos mediante Wavelet para la modelación térmica de transformadores de potencia Data processing using wavelet for power transformers thermal model

    Directory of Open Access Journals (Sweden)

    Rómulo Pérez

    2012-02-01

    Full Text Available En este trabajo las mediciones recabadas por una estación experimental instalada en un Transformador de 100 MVA de la Subestación Barquisimeto de Venezuela son procesadas para eliminar factores de ruido que introducen errores en la identificación de parámetros del modelo térmico para el cálculo de la temperatura superior del aceite. Se usa una metodología para el control de calidad y eliminación del ruido en las mediciones recabadas basada en experiencias propias y reforzadas con experiencias de reconocidos autores internacionales, la cual aplica la Transformada Discreta de Wavelet DWT para obtener datos que muestran buenos indicadores de calidad en las principales variables del modelo térmico, como lo son la corriente de carga, la temperatura ambiente y la temperatura del aceite superior. Finalmente se comparan los resultados de la modelación térmica antes y después de ser procesados los datos, donde se evidencia un notable incremento en la exactitud del modelo.In this work measurement get of experimental station connected in a power transformer of 100 MVA in a Barquisimeto Substation in Venezuela are processing to eliminate noise that introduce mistake in the parameters identification for top oil temperature model calculation. A methodology based in your experiences with experiences of international authors for the control of quality and elimination of the noise in the successfully obtained measurements is used. It’s apply the Discreet Wavelet Transform (DWT to collect data that show good indicators of quality in the main values of the thermal model, as the load current, the ambient temperature and the top oil temperature. Finally is compared thermal model results after and before data processing, where at increase in the exactitude of the thermal model is demonstrated.

  15. Despeckle and geographical feature extraction in SAR images by wavelet transform

    Science.gov (United States)

    Gupta, Karunesh K.; Gupta, Rajiv

    This paper presents a method to despeckle Synthetic Aperture Radar (SAR) image, and then extract geographical features in it. In this research work, speckle is reduced by multiscale analysis in wavelet domain. In terms of geographical feature preservation the result shows that the method is better compared to spatial domain filters, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters. The geographical features such as roads, airport runways, rivers and other ribbon-like shape structures are detected by the new wavelet-based method as proposed by Yuan Yan Tang. Experimental results show that the proposed method extracts geographical features of different width as well as different gray levels.

  16. Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings

    OpenAIRE

    Yiakopoulos, C.T.; Antoniadis, I.A.

    2002-01-01

    Vibration signals resulting from roller bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. The envelope detection or demodulation methods have been established as the dominant analysis methods for this purpose, since they can separate the useful part of the signal from its redundant contents. The paper proposes a new effective demodulation method, based on the wavelet transform. Th...

  17. Infinitely oscillating wavelets and a efficient implementation algorithm based the FFT

    Directory of Open Access Journals (Sweden)

    Marcela Fabio

    2015-01-01

    Full Text Available In this work we present the design of an orthogonal wavelet, infinitely oscillating, located in time with decay 1/|t|n and limited-band. Its appli- cation leads to the signal decomposition in waves of instantaneous, well defined frequency. We also present the implementation algorithm for the analysis and synthesis based on the Fast Fourier Transform (FFT with the same complexity as Mallat’s algorithm.

  18. Wavelet-based analysis of gastric microcirculation in rats with ulcer bleedings

    Science.gov (United States)

    Pavlov, A. N.; Rodionov, M. A.; Pavlova, O. N.; Semyachkina-Glushkovskaya, O. V.; Berdnikova, V. A.; Kuznetsova, Ya. V.; Semyachkin-Glushkovskij, I. A.

    2012-03-01

    Studying of nitric oxide (NO) dependent mechanisms of regulation of microcirculation in a stomach can provide important diagnostic markers of the development of stress-induced ulcer bleedings. In this work we use a multiscale analysis based on the discrete wavelet-transform to characterize a latent stage of illness formation in rats. A higher sensitivity of stomach vessels to the NO-level in ill rats is discussed.

  19. On the study of applying Morlet wavelet to the Hilbert transform for the envelope detection of bearing vibrations

    Science.gov (United States)

    Sheen, Yuh-Tay

    2009-07-01

    In this paper, the Morlet wavelet is studied to apply in the envelope analysis for the bearing vibration and, in practice, would be easier to apply in the real-time vibration analyses. The parameter designation of Morlet wavelet is proposed to filter out and demodulate one of the resonance modes of a bearing vibration, but the designation of the filtering passband would not be required. Therefore, the mode vibration and its corresponding envelope could be derived from the real part and the absolute value of the wavelet transform, respectively. In addition, the Morlet wavelet with properly designating the parameters possesses a very excellent property of fast waveform convergence and could effectively reduce the computing burden. From theoretical and experimental studies, it is shown that the designation of Morlet wavelet could be effectively applied in the envelope detection for the vibration signals and could be useful in the defect diagnosis of bearing vibrations.

  20. SU-F-J-27: Segmentation of Prostate CBCT Images with Implanted Calypso Transponders Using Double Haar Wavelet Transform

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y [Shandong Communication and Media College, Jinan, Shandong (China); Saleh, Z; Tang, X [Memorial Sloan Kettering Cancer Center, West Harrison, NY (United States); Song, Y; Obcemea, C [Memorial Sloan-Kettering Cancer Center, Sleepy Hollow, NY (United States); Chan, M [Memorial Sloan-Kettering Cancer Center, Basking Ridge, NJ (United States); Li, X [Memorial Sloan Kettering Cancer Center, Rockville Centre, NY (United States); Happersett, L [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Shi, C [Saint Vincent Medical Center, Bridgeport, CT (United States); Qian, X [North Shore Long Island Jewish health System, North New Hyde Park, NY (United States)

    2016-06-15

    Purpose: Segmentation of prostate CBCT images is an essential step towards real-time adaptive radiotherapy. It is challenging For Calypso patients, as more artifacts are generated by the beacon transponders. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. Methods: Five hypofractionated prostate patients with daily CBCT were studied. Each patient had 3 Calypso transponder beacons implanted, and the patients were setup and treated with Calypso tracking system. Two sets of CBCT images from each patient were studied. The structures (i.e. rectum, bladder, and prostate) were contoured by a trained expert, and these served as ground truth. For a given CBCT, the moving window-based Double Haar transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied to the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary/segmented image of the object of interest is therefore obtained. DICE, sensitivity, inclusiveness and ΔV were used to evaluate the segmentation result. Results: Considering all patients, the bladder has the DICE, sensitivity, inclusiveness, and ΔV ranges of [0.81–0.95], [0.76–0.99], [0.83–0.94], [0.02–0.21]. For prostate, the ranges are [0.77–0.93], [0.84–0.97], [0.68–0.92], [0.1–0.46]. For rectum, the ranges are [0.72–0.93], [0.57–0.99], [0.73–0.98], [0.03–0.42]. Conclusion: The proposed algorithm appeared effective segmenting prostate CBCT images with the present of the Calypso artifacts. However, it is not robust in two scenarios: 1) rectum with significant amount of gas; 2) prostate with very low contrast. Model

  1. Apple Shape Classification Method Based on Wavelet Moment

    Directory of Open Access Journals (Sweden)

    Jiangsheng Gui

    2014-09-01

    Full Text Available Shape is not only an important indicator for assessing the grade of the apple, but also the important factors for increasing the value of the apple. In order to improve the apple shape classification accuracy rate, an approach for apple shape sorting based on wavelet moments was proposed, the image was first subjected to a normalization process using its regular moments to obtain scale and translation invariance, the rotation invariant wavelet moment features were then extracted from the scale and translation normalized images and the method of cluster analysis was used for finished the shape classification. This method performs better than traditional approaches such as Fourier descriptors and Zernike moments, because of that Wavelet moments can provide time-domain and frequency domain window, which was verified by experiments. The normal fruit shape, mild deformity and severe deformity classification accuracy is 86.21 %, 85.82 %, 90.81 % by our method.

  2. [Multi-scale correlation analysis of soil organic carbon with its influence factors using wavelet transform].

    Science.gov (United States)

    Jiang, Chun; Qian, Le-Xiang; Wu, Zhi-Feng; Wen, Ya; Deng, Nan-Rong

    2013-12-01

    Based on GIS, this paper chose the soil organic carbon (SOC) density in soil surface layer (0-20 cm) and its influence factors (NDVI, elevation, slope and aspect) as research objects, one-dimensional discrete wavelet transform (DWT) was used as the multi-scale decomposition tool to quantitatively revealed the multi-scale correlation relationships among SOC density and its influence factors on the grid scale along 4 transects of the mountainous area of Guangdong Province. The results showed that the correlation among SOC density and its influence factors was scale-dependent with varying degree. The influence of NDVI was strongest at the scales of 2, 8 and 16 km, while elevation showed its greatest influence at the scales of 8 and 16 km. The control action of slope was rather weak, with a less significant correlation depending on scale. The negative effect of aspect became stronger with increasing scale at > 2 km scale. The SOC density of the different transects was affected by various factors, of which NDVI and elevation were the main factors, and slope and aspect only reacted with individual transects at larger scales.

  3. Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models

    Directory of Open Access Journals (Sweden)

    Oksana V. Mandrikova

    2015-11-01

    Full Text Available The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models. This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC datasets from Kamchatka (a region in the Russian Far East and Magadan (a town in the Russian Far East. The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka.

  4. Prediction of Protein-Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests.

    Science.gov (United States)

    Jia, Jianhua; Xiao, Xuan; Liu, Bingxiang

    2016-06-01

    Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF. © 2015 Society for Laboratory Automation and Screening.

  5. Pigmented skin lesion detection using random forest and wavelet-based texture

    Science.gov (United States)

    Hu, Ping; Yang, Tie-jun

    2016-10-01

    The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

  6. Wavelet Transform Of Acoustic Signal From A Ranque- Hilsch Vortex Tube

    Science.gov (United States)

    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.

  7. Analysis of high impedance transients and improved data compression using wavelet transform

    Directory of Open Access Journals (Sweden)

    Subramaniam N.P.

    2006-01-01

    Full Text Available High impedance transients are difficult to detect and classify by using conventional methods due to low transient current [1]. This paper proposes an alternative technique to detect and classify the high impedance transient by obtaining the energy curve from the wavelet co-efficient at each level. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with high impedance fault. From the results obtained it can be inferred, that the energy level of each transient disturbance has unique deviation from pure sinusoidal waveform in particular energy level, which is adopted to provide reliable classification of the type of transient. Also, this paper proposes a novel technique for disturbance data compression which is called as Improved Disturbance Compression Method (IDCM. In this method, only the disturbance data is compressed not the whole waveform using sparse representation property of Wavelet Transform.

  8. Operational modal analysis and wavelet transformation for damage identification in wind turbine blades

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Kirkegaard, Poul Henning

    2014-01-01

    The presented study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to a SSP 34m blade mounted on a test rig. Operational modal analysis (OMA) was conducted to obtain...

  9. Operational Modal Analysis and Wavelet Transformation for Damage Identification in Wind Turbine Blades

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Kirkegaard, Poul Henning

    2016-01-01

    This study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to an SSP 34-m blade mounted on a test rig. Operational modal analysis was conducted to obtain mode shapes...

  10. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

    Science.gov (United States)

    Janjarasjitt, Suparerk

    2017-02-13

    In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

  11. A new time-adaptive discrete bionic wavelet transform for enhancing speech from adverse noise environment

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2013-04-01

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

  13. On the equivalence of brushlet and wavelet bases

    DEFF Research Database (Denmark)

    Nielsen, Morten; Borup, Lasse

    2005-01-01

    We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results...

  14. On the equivalence of brushlet and wavelet bases

    DEFF Research Database (Denmark)

    Borup, Lasse; Nielsen, Morten

    We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results...

  15. Energy-Efficient Transmission of Wavelet-Based Images in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Vincent Lecuire

    2007-01-01

    Full Text Available We propose a self-adaptive image transmission scheme driven by energy efficiency considerations in order to be suitable for wireless sensor networks. It is based on wavelet image transform and semireliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semireliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful tradeoff between the reconstructed images quality and the sensor nodes' lifetime. An analytical study in terms of dissipated energy is performed to compare the self-adaptive image transmission scheme to a fully reliable scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 80% reduction in the energy consumption achieved by our proposal compared to a nonenergy-aware one, with the guarantee for the image quality to be lower-bounded.

  16. Real-time estimation of optical flow based on optimized haar wavelet features

    DEFF Research Database (Denmark)

    Salmen, Jan; Caup, Lukas; Igel, Christian

    2011-01-01

    -objective optimization. In this work, we build on a popular algorithm developed for realtime applications. It is originally based on the Census transform and benefits from this encoding for table-based matching and tracking of interest points. We propose to use the more universal Haar wavelet features instead...... of the Census transform within the same framework. The resulting approach is more flexible, in particular it allows for sub-pixel accuracy. For comparison with the original method and another baseline algorithm, we considered both popular benchmark datasets as well as a long synthetic video sequence. We...

  17. Accuracy of a wavelet-based fall detection approach using an accelerometer and a barometric pressure sensor.

    Science.gov (United States)

    Ejupi, Andreas; Galang, Chantel; Aziz, Omar; Park, Edward J; Robinovitch, Stephen

    2017-07-01

    Falls are a major source of morbidity in older adults, and 50% of older adults who fall cannot rise independently after falling. Wearable sensor-based fall detection devices may assist in preventing long lies after falls. The goal of this study was to determine the accuracy of a novel wavelet-based approach to automatically detect falls based on accelerometer and barometric pressure sensor data. Participants (n=15) mimicked a range of falls, near falls, and activities of daily living (ADLs) while wearing accelerometer and barometric pressure sensors on the lower back, chest, wrists and thighs. The wavelet transform using pattern adapted wavelets was applied to detect falls from the sensor data. In total, 525 trials (194 falls, 105 near-falls and 226 ADLs) were included in our analysis. When we applied the wavelet-based method on only accelerometer data, classification accuracies ranged from 82% to 96%, with the chest sensor providing the highest accuracy. Accuracy improved by 3.4% on average (p=0.041; SD=3.0%) when we also included the barometric pressure sensor data. The highest classification accuracies (of 98%) were achieved when we combined wavelet-based features and traditional statistical features in a multiphase fall detection model using machine learning. We show that the wavelet-based approach accurately distinguishes falls from near-falls and ADLs, and that it can be applied on wearable sensor data generated from various body locations. Additionally, we show that the accuracy of a wavelet-based fall detection system can be further improved by combining accelerometer and barometric pressure sensor data, and by incorporating wavelet and statistical features in a machine learning classification algorithm.

  18. Wavelet basics

    CERN Document Server

    Chan, Y T

    1995-01-01

    Since the study of wavelets is a relatively new area, much of the research coming from mathematicians, most of the literature uses terminology, concepts and proofs that may, at times, be difficult and intimidating for the engineer. Wavelet Basics has therefore been written as an introductory book for scientists and engineers. The mathematical presentation has been kept simple, the concepts being presented in elaborate detail in a terminology that engineers will find familiar. Difficult ideas are illustrated with examples which will also aid in the development of an intuitive insight. Chapter 1 reviews the basics of signal transformation and discusses the concepts of duals and frames. Chapter 2 introduces the wavelet transform, contrasts it with the short-time Fourier transform and clarifies the names of the different types of wavelet transforms. Chapter 3 links multiresolution analysis, orthonormal wavelets and the design of digital filters. Chapter 4 gives a tour d'horizon of topics of current interest: wave...

  19. Fast wavelet based sparse approximate inverse preconditioner

    Energy Technology Data Exchange (ETDEWEB)

    Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

    1996-12-31

    Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

  20. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  1. Wavelet analysis in neurodynamics

    Science.gov (United States)

    Pavlov, Aleksei N.; Hramov, Aleksandr E.; Koronovskii, Aleksei A.; Sitnikova, Evgenija Yu; Makarov, Valeri A.; Ovchinnikov, Alexey A.

    2012-09-01

    Results obtained using continuous and discrete wavelet transforms as applied to problems in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for decoding signal information from neural systems and networks. The following areas of application are considered: (1) the microscopic dynamics of single cells and intracellular processes, (2) sensory data processing, (3) the group dynamics of neuronal ensembles, and (4) the macrodynamics of rhythmical brain activity (using multichannel EEG recordings). The detection and classification of various oscillatory patterns of brain electrical activity and the development of continuous wavelet-based brain activity monitoring systems are also discussed as possibilities.

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

    Energy Technology Data Exchange (ETDEWEB)

    Krug, R; Carballido-Gamio, J; Burghardt, A; Haase, S; Sedat, J W; Moss, W C; Majumdar, S

    2005-04-11

    Trabecular bone structure and bone density contribute to the strength of bone and are important in the study of osteoporosis. Wavelets are a powerful tool to characterize and quantify texture in an image. In this study the thickness of trabecular bone was analyzed in 8 cylindrical cores of the vertebral spine. Images were obtained from 3 Tesla (T) magnetic resonance imaging (MRI) and micro-computed tomography ({micro}CT). Results from the wavelet based analysis of trabecular bone were compared with standard two-dimensional structural parameters (analogous to bone histomorphometry) obtained using mean intercept length (MR images) and direct 3D distance transformation methods ({micro}CT images). Additionally, the bone volume fraction was determined from MR images. We conclude that the wavelet based analyses delivers comparable results to the established MR histomorphometric measurements. The average deviation in trabecular thickness was less than one pixel size between the wavelet and the standard approach for both MR and {micro}CT analysis. Since the wavelet based method is less sensitive to image noise, we see an advantage of wavelet analysis of trabecular bone for MR imaging when going to higher resolution.

  3. Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization

    Science.gov (United States)

    Silva, Leandro A.; Del-Moral-Hernandez, Emilio; Moreno, Ramon A.; Furuie, Sérgio S.

    2011-10-01

    Images are fundamental sources of information in modern medicine. The images stored in a database and divided in categories are an important step for image retrieval. For an automatic categorization process, detailed analysis is done regarding image representation and generalization method. The baseline method for this process, in the medical image context, is using thumbnails and K-nearest neighbor (KNN), which is easily implemented and has had satisfactory results in literature. This work addresses an alternative method for automatic categorization, which jointly uses discrete wavelet transform with Hu's moments for image representation and self-organizing maps (SOM) neural networks combined with the KNN classifier (SOM-KNN), for medical image categorization. Furthermore, extensive experiments are conducted, to define the best wavelet family and to select the best coefficients set, to consider the remaining wavelet coefficients set (not selected as the best ones) through their Hu's moments, and to carry out a contrastive study with other successful approaches for categorization. The categorization result from a database with 10,000 images in 116 categories yielded 81.8% of correct rate, which is much better than the 67.9% obtained by the baseline method; and the time consumed in classification processing with SOM-KNN is 100 times shorter than KNN.

  4. A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2012-01-01

    Full Text Available Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67% is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

  5. Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion

    Directory of Open Access Journals (Sweden)

    Triwiyanto Triwiyanto

    2017-01-01

    Full Text Available Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG signals using the Continuous Wavelet Transform (CWT. Four male participants were recruited to perform a repetitive motion (flexion and extension movements from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS and the Scale-Average Wavelet Power (SAWP parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF and the Instantaneous Mean Power Spectrum (IMNP parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1 and 0.0095mV^2Hz^(-1 for R^2 and slope, respectively.

  6. WaveJava: Wavelet-based network computing

    Science.gov (United States)

    Ma, Kun; Jiao, Licheng; Shi, Zhuoer

    1997-04-01

    Wavelet is a powerful theory, but its successful application still needs suitable programming tools. Java is a simple, object-oriented, distributed, interpreted, robust, secure, architecture-neutral, portable, high-performance, multi- threaded, dynamic language. This paper addresses the design and development of a cross-platform software environment for experimenting and applying wavelet theory. WaveJava, a wavelet class library designed by the object-orient programming, is developed to take advantage of the wavelets features, such as multi-resolution analysis and parallel processing in the networking computing. A new application architecture is designed for the net-wide distributed client-server environment. The data are transmitted with multi-resolution packets. At the distributed sites around the net, these data packets are done the matching or recognition processing in parallel. The results are fed back to determine the next operation. So, the more robust results can be arrived quickly. The WaveJava is easy to use and expand for special application. This paper gives a solution for the distributed fingerprint information processing system. It also fits for some other net-base multimedia information processing, such as network library, remote teaching and filmless picture archiving and communications.

  7. High Sensitive Distinction of Discharge in Air by Daubechies Wavelet Transform

    Science.gov (United States)

    Yamada, Ioya; Kubota, Hisashi; Inui, Akifumi; Kawaguchi, Yoshihiro

    If partial discharge occurs in high voltage apparatus, it is unfavorable in view point of its insulation reliability, because they might develop into its insulation degradation or its electrical breakdown. In order to raise the insulation reliability of an apparatus, it is important to detect a minute partial discharge with sufficient sensitivity, especially suppressing background noise. This paper deals with the waveform processing technology by the Daubechies wavelet transform to make relief of the partial discharge signal from a measured noise-containing signal. On this basic idea, here is discussed that the optimal Daubechies order and its level have a close relation with the detection impedance and the sampling interval of the measured signal. Since the partial discharge waveform measured with the detection impedance of parallel circuits of RLC tuned into a damped oscillatory pulse, it has been demonstrated that the Daubechies wavelet transform is effective in discriminating the partial discharge signal from the measured noise-containing signal. Moreover, by choosing suitably the Daubechies order and its level applied to the measured data, it has been clarified that even a minute glow corona which have been masked by the background noise, and also the streamer corona turns into clear appearance on the transformed wave with sufficient sensitivity.

  8. Comparative spectral analysis of veterinary powder product by continuous wavelet and derivative transforms.

    Science.gov (United States)

    Dinç, Erdal; Kanbur, Murat; Baleanu, Dumitru

    2007-10-01

    Comparative simultaneous determination of chlortetracycline and benzocaine in the commercial veterinary powder product was carried out by continuous wavelet transform (CWT) and classical derivative transform (or classical derivative spectrophotometry). In this quantitative spectral analysis, two proposed analytical methods do not require any chemical separation process. In the first step, several wavelet families were tested to find an optimal CWT for the overlapping signal processing of the analyzed compounds. Subsequently, we observed that the coiflets (COIF-CWT) method with dilation parameter, a=400, gives suitable results for this analytical application. For a comparison, the classical derivative spectrophotometry (CDS) approach was also applied to the simultaneous quantitative resolution of the same analytical problem. Calibration functions were obtained by measuring the transform amplitudes corresponding to zero-crossing points for both CWT and CDS methods. The utility of these two analytical approaches were verified by analyzing various synthetic mixtures consisting of chlortetracycline and benzocaine and they were applied to the real samples consisting of veterinary powder formulation. The experimental results obtained from the COIF-CWT approach were statistically compared with those obtained by classical derivative spectrophotometry and successful results were reported.

  9. Biometric gait recognition for mobile devices using wavelet transform and support vector machines

    DEFF Research Database (Denmark)

    Hestbek, Martin Reese; Nickel, C.; Busch, C.

    2012-01-01

    The ever growing number of mobile devices has turned the attention to security and usability. If a mobile device is lost or stolen this can lead to loss of personal information and the possibility of identity theft. People often tend not to use passwords which leads to lack of personal security...... mainly due to convenience and frequent use. This paper suggests to serve both convenience and security needs at the same time. Thus we suggest to observe the user's gait characteristic. Our approach realizes user authentication by applying the discrete wavelet transform (DWT) to acceleration signals...

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

    Science.gov (United States)

    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.

  11. Plasma Plume Oscillations Monitoring during Laser Welding of Stainless Steel by Discrete Wavelet Transform Application

    Directory of Open Access Journals (Sweden)

    Teresa Sibillano

    2010-04-01

    Full Text Available 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.

  12. Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection

    Science.gov (United States)

    Flórez-Prias, L. A.; Contreras-Ortiz, S. H.

    2017-11-01

    The purpose of the present article is to characterize sEMG signals to determine muscular fatigue levels. To do this, the signal is decomposed using the discrete wavelet transform, which offers noise filtering features, simplicity and efficiency. sEMG signals on the forearm were acquired and analyzed during the execution of cyclic muscular contractions in the presence and absence of fatigue. When the muscle fatigues, the sEMG signal shows a more erratic behavior of the signal as more energy is required to maintain the effort levels.

  13. Using computer algebra to perform image compression with wavelet transform and SVD

    Science.gov (United States)

    Díaz, Felipe

    2014-05-01

    Computer Algebra Software, especially Maple and its Image Tools package, is used to develop image compression using the Weibull distribution, Wavelet transform application and Singular Value Decomposition (SVD). For prototyping of the image compression process, Maple packages, Linear Algebra, Array Tools and Discrete Transform are used simultaneously with Image Tools image processing package. The image compression process implies the realization of matrix computing with high dimension matrices, and Maple software develops those operations easily and efficiently. Some image compression experiments are done, and the matrix dimension for minimum information needed to store an image is shown clearly, also the matrix dimension of redundant information. Implementation of algorithms for image compression in other computer algebra systems such as Mathematica and Maxima is proposed as future investigation path. Also it is proposed the use of curvelet transform as a tool for image compression,

  14. On the spline-based wavelet differentiation matrix

    Science.gov (United States)

    Jameson, Leland

    1993-01-01

    The differentiation matrix for a spline-based wavelet basis is constructed. Given an n-th order spline basis it is proved that the differentiation matrix is accurate of order 2n + 2 when periodic boundary conditions are assumed. This high accuracy, or superconvergence, is lost when the boundary conditions are no longer periodic. Furthermore, it is shown that spline-based bases generate a class of compact finite difference schemes.

  15. Two-dimensional Morlet wavelet transform and its application to wave recognition methodology of automatically extracting two-dimensional wave packets from lidar observations in Antarctica

    Science.gov (United States)

    Chen, Cao; Chu, Xinzhao

    2017-09-01

    Waves in the atmosphere and ocean are inherently intermittent, with amplitudes, frequencies, or wavelengths varying in time and space. Most waves exhibit wave packet-like properties, propagate at oblique angles, and are often observed in two-dimensional (2-D) datasets. These features make the wavelet transforms, especially the 2-D wavelet approach, more appealing than the traditional windowed Fourier analysis, because the former allows adaptive time-frequency window width (i.e., automatically narrowing window size at high frequencies and widening at low frequencies), while the latter uses a fixed envelope function. This study establishes the mathematical formalism of modified 1-D and 2-D Morlet wavelet transforms, ensuring that the power of the wavelet transform in the frequency/wavenumber domain is equivalent to the mean power of its counterpart in the time/space domain. Consequently, the modified wavelet transforms eliminate the bias against high-frequency/small-scale waves in the conventional wavelet methods and many existing codes. Based on the modified 2-D Morlet wavelet transform, we put forward a wave recognition methodology that automatically identifies and extracts 2-D quasi-monochromatic wave packets and then derives their wave properties including wave periods, wavelengths, phase speeds, and time/space spans. A step-by-step demonstration of this methodology is given on analyzing the lidar data taken during 28-30 June 2014 at McMurdo, Antarctica. The newly developed wave recognition methodology is then applied to two more lidar observations in May and July 2014, to analyze the recently discovered persistent gravity waves in Antarctica. The decomposed inertia-gravity wave characteristics are consistent with the conclusion in Chen et al. (2016a) that the 3-10 h waves are persistent and dominant, and exhibit lifetimes of multiple days. They have vertical wavelengths of 20-30 km, vertical phase speeds of 0.5-2 m/s, and horizontal wavelengths up to several

  16. A New Alignment Method Based on The Wavelet Multi-Scale Cross-Correlation for Noisy High Resolution ECG Records

    National Research Council Canada - National Science Library

    Laciar, E

    2001-01-01

    ... between the wavelet transforms of the template and the detected beat, respectively. The wavelet and temporal methods were tested for several simulated records corrupted with white noise and electromyographic (EMG...

  17. Wavelet-based tracking of bacteria in unreconstructed off-axis holograms.

    Science.gov (United States)

    Marin, Zach; Wallace, J Kent; Nadeau, Jay; Khalil, Andre

    2017-09-13

    We propose an automated wavelet-based method of tracking particles in unreconstructed off-axis holograms to provide rough estimates of the presence of motion and particle trajectories in digital holographic microscopy (DHM) time series. The wavelet transform modulus maxima segmentation method is adapted and tailored to extract Airy-like diffraction disks, which represent bacteria, from DHM time series. In this exploratory analysis, the method shows potential for estimating bacterial tracks in low-particle-density time series, based on a preliminary analysis of both living and dead Serratia marcescens, and for rapidly providing a single-bit answer to whether a sample chamber contains living or dead microbes or is empty. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2017-08-01

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

  19. Multichannel EEG compression: wavelet-based image and volumetric coding approach.

    Science.gov (United States)

    Srinivasan, K; Dauwels, J; Ramasubba, M R

    2013-01-01

    In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram signals (EEG) are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of lossy plus residual coding, consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately.

  20. WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis.

    Science.gov (United States)

    Wang, Nizhuan; Zeng, Weiming; Shi, Yingchao; Ren, Tianlong; Jing, Yanshan; Yin, Jun; Yang, Jiajun

    2015-05-15

    Researches declared that the super-Gaussian property contributed to the success of some spatial independent component analysis (ICA) algorithms in brain fMRI source separation (e.g., Infomax and FastICA), which implied that sparse approximation transforming the sources (super-Gaussian or Gaussian-like) with stronger super-Gaussian feature would possibly improve the separation performance of these algorithms. This paper presented a novel wavelet-shrinkage based ICA (WASICA) model, an extension of our previous SACICA, for single-subject analysis. In contrast, two main aspects had been effectively enhanced: (1) sparse approximation coefficients set formation, made up of two sub-procedures: the wavelet-shrinkage of wavelet packet (WP) tree nodes, and the automatic nodes selection and integration based on the relative WP energy; (2) ICA-based decomposition and reconstruction, composed of temporal dynamics extraction using ICA, WP reconstruction based on the sparse approximation coefficients set and least-square-based functional networks reconstruction. The wavelet-shrinkage and the automatic nodes selection and integration simultaneously transformed both the mixtures and underlying sources into effective sparse approximation coefficients sets, exhibiting stronger super-Gaussian distribution; WP projected-back approximation with nuisance-exclusion contributed to networks reconstruction. Simulation 1 revealed WASICA successfully recovered super-Gaussian and some Gaussian-like sources. Simulation 2 and hybrid data experiments showed that WASICA with good temporal performance had stronger source recovery ability and signal detection sensitivity spatially than FastICA, Infomax and SACICA did; the higher intra-consistency in task-related experiments denoted WASICA occupied stronger spatial robustness against smooth kernels. WASICA was a promising brain signal separation model with charming spatial-temporal performance. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating.

    Science.gov (United States)

    Hassan, Ahnaf Rashik; Siuly, Siuly; Zhang, Yanchun

    2016-12-01

    Epileptic seizure detection is traditionally performed by expert clinicians based on visual observation of EEG signals. This process is time-consuming, burdensome, reliant on expensive human resources, and subject to error and bias. In epilepsy research, on the other hand, manual detection is unsuitable for handling large data-sets. A computerized seizure identification scheme can eradicate the aforementioned problems, aid clinicians, and benefit epilepsy research. In this work, a new automated epilepsy diagnosis scheme based on Tunable-Q factor wavelet transform (TQWT) and bootstrap aggregating (Bagging) using Electroencephalogram (EEG) signals is proposed. Until now, this is the first time spectral features in the TQWT domain in conjunction with Bagging are employed for epilepsy seizure identification to the best of the authors' knowledge. At first, we decompose the EEG signal segments into sub-bands using TQWT. We then extract various spectral features from the TQWT sub-bands. The suitability of spectral features in the TQWT domain is established through statistical measures and graphical analyses. Afterwards, Bagging is employed for epileptic seizure classification. The efficacy of Bagging in the proposed detection scheme is also studied in this research. The effects of various TQWT and Bagging parameters are investigated. The optimal choices of these parameters are also determined. The performance of the proposed scheme is studied using a publicly available benchmark EEG database for various classification cases that include inter-ictal (seizure-free interval), ictal (seizure) and healthy; seizure and non-seizure; ictal and inter-ictal; and seizure and healthy. In comparison with the state-of-the-art algorithms, the performance of the proposed method is superior in terms of sensitivity, specificity, and accuracy. The seizure detection method proposed herein therefore can alleviate the burden of medical professionals of analyzing a large bulk of data by visual

  2. Decision support system for age-related macular degeneration using discrete wavelet transform.

    Science.gov (United States)

    Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Koh, Joel E W; Chua, Chua Kuang; Tan, Jen Hong; Chandran, Vinod; Lim, Choo Min; Noronha, Kevin; Laude, Augustinus; Tong, Louis

    2014-09-01

    Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, [Formula: see text]-nearest neighbor ([Formula: see text]-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70%, sensitivity of 91.11%, and specificity of 96.30% using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

  3. WAVELET BASED SEGMENTATION USING OPTIMAL STATISTICAL FEATURES ON BREAST IMAGES

    Directory of Open Access Journals (Sweden)

    A. Sindhuja

    2014-05-01

    Full Text Available Elastography is the emerging imaging modality that analyzes the stiffness of the tissue for detecting and classifying breast tumors. Computer-aided detection speeds up the diagnostic process of breast cancer improving the survival rate. A multiresolution approach using Discrete wavelet transform is employed on real time images, using the low-low (LL, low-high (LH, high-low (HL, and high-high (HH sub-bands of Daubechies family. Features are extracted, selected and then finally segmented by K-means clustering algorithm. The proposed work can be extended to Classification of the tumors.

  4. Practice-related changes in neural activation patterns investigated via wavelet-based clustering analysis

    Science.gov (United States)

    Lee, Jinae; Park, Cheolwoo; Dyckman, Kara A.; Lazar, Nicole A.; Austin, Benjamin P.; Li, Qingyang; McDowell, Jennifer E.

    2012-01-01

    Objectives To evaluate brain activation using functional magnetic resonance imaging (fMRI) and specifically, activation changes across time associated with practice-related cognitive control during eye movement tasks. Experimental design Participants were engaged in antisaccade performance (generating a glance away from a cue) while fMR images were acquired during two separate time points: 1) at pre-test before any exposure to the task, and 2) at post-test, after one week of daily practice on antisaccades, prosaccades (glancing towards a target) or fixation (maintaining gaze on a target). Principal observations The three practice groups were compared across the two time points, and analyses were conducted via the application of a model-free clustering technique based on wavelet analysis. This series of procedures was developed to avoid analysis problems inherent in fMRI data and was composed of several steps: detrending, data aggregation, wavelet transform and thresholding, no trend test, principal component analysis and K-means clustering. The main clustering algorithm was built in the wavelet domain to account for temporal correlation. We applied a no trend test based on wavelets to significantly reduce the high dimension of the data. We clustered the thresholded wavelet coefficients of the remaining voxels using the principal component analysis K-means clustering. Conclusion Over the series of analyses, we found that the antisaccade practice group was the only group to show decreased activation from pre- to post-test in saccadic circuitry, particularly evident in supplementary eye field, frontal eye fields, superior parietal lobe, and cuneus. PMID:22505290

  5. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

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

    Science.gov (United States)

    Lu, Wenke; Zhu, Changchun

    2011-11-01

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

  7. A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform.

    Science.gov (United States)

    Bhattacharyya, Abhijit; Pachori, Ram Bilas

    2017-09-01

    This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs. The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database. Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings. The proposed method develops time-frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.

  8. [Prediction of chlorophyll content of greenhouse tomato using wavelet transform combined with NIR spectra].

    Science.gov (United States)

    Ding, Yong-Jun; Li, Min-Zan; Zheng, Li-Hua; Zhao, Rui-Jiao; Li, Xiu-Hua; An, Deng-Kui

    2011-11-01

    In quantitative analysis of spectral data, noises and background interference always degrade the accuracy of spectral feature extraction. The wavelet transform is multi-scale decomposition used to reduce the noise and improve the analysis precision. On the other hand, the wavelet transform denoising is often followed by destroying the efficiency information. The present research introduced two indexes to control the scale of decomposition, the smoothness index (SI) and the time shift index (TSI). When the parameters satisfied TSI 0.100 4, the noise of spectral characteristic was reduced. In the meanwhile, the reflection peaks of biochemical components were reserved. Through analyzing the correlation between denoised spectrum and chlorophyll content, some spectral characteristics parameters reflecting the changing tendency of chlorophyll content were chosen. Finally, the partial least squares regression (PLSR) was used to develop the prediction model of the chlorophyll content of tomato leaf. The result showed that the predictiong model, which used the values of absorbance at 366, 405, 436, 554, 675 and 693 nm as input variables, had higher predictive ability (calibration coefficient was 0. 892 6, and validation coefficient was 0.829 7) and better potential to diagnose tomato growth in greenhouse.

  9. Digital mammography: computer-assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms.

    Science.gov (United States)

    Li, L; Qian, W; Clarke, L P

    1997-11-01

    The authors evaluated a modular computer-assisted diagnosis (CAD) method for mass detection that uses computation of features in three domains (gray level, morphology, and directional texture). Their objectives were to improve the sensitivity of detection and reduce the false-positive (FP) detection rate. The directional wavelet transform (DWT) method, which uses both multiorientation and multiresolution wavelet transforms to improve image preprocessing and segmentation of suspicious areas and to extract both morphologic and directional texture features, was evaluated with a previously reported image database containing 50 normal and 45 abnormal digitized screen-film mammograms. The mammograms contained all mass types and included 16 minimal cancers. This method was compared with the Markov random field (MRF) method to avoid issues related to case selection criteria. Free-response receiver operating characteristic curves were compared for both DWT and MRF methods. For the DWT method, the sensitivity was 98% and the FP detection rate was 1.8 FP findings per image. For the MRF method, the sensitivity was 90% and the FP detection rate was 2.0 FP findings per image. The CAD method applied to the full mammographic image is automatic and independent of mass type. The segmentation of masses as performed with this method may potentially allow visual interpretation according to American College of Radiology criteria.

  10. Implementation of Wavelet-Based Robust Differential Control for Electric Vehicle Application

    DEFF Research Database (Denmark)

    Daya, Febin; Padmanaban, Sanjeevikumar; Blaabjerg, Frede

    2015-01-01

    This research letter presents the modeling and simulation of electronic differential, employing a novel wavelet controller for two brushless dc motors. The proposed controller uses discrete wavelet transform to decompose the error between actual and reference speed. Error signal that is actually ...

  11. Wavelet-Based Color Pathological Image Watermark through Dynamically Adjusting the Embedding Intensity

    Directory of Open Access Journals (Sweden)

    Guoyan Liu

    2012-01-01

    Full Text Available This paper proposes a new dynamic and robust blind watermarking scheme for color pathological image based on discrete wavelet transform (DWT. The binary watermark image is preprocessed before embedding; firstly it is scrambled by Arnold cat map and then encrypted by pseudorandom sequence generated by robust chaotic map. The host image is divided into n×n blocks, and the encrypted watermark is embedded into the higher frequency domain of blue component. The mean and variance of the subbands are calculated, to dynamically modify the wavelet coefficient of a block according to the embedded 0 or 1, so as to generate the detection threshold. We research the relationship between embedding intensity and threshold and give the effective range of the threshold to extract the watermark. Experimental results show that the scheme can resist against common distortions, especially getting advantage over JPEG compression, additive noise, brightening, rotation, and cropping.

  12. A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation

    Science.gov (United States)

    Qian, Fang; Wu, Yihui; Hao, Peng

    2017-11-01

    Baseline correction is a very important part of pre-processing. Baseline in the spectrum signal can induce uneven amplitude shifts across different wavenumbers and lead to bad results. Therefore, these amplitude shifts should be compensated before further analysis. Many algorithms are used to remove baseline, however fully automated baseline correction is convenient in practical application. A fully automated algorithm based on wavelet feature points and segment interpolation (AWFPSI) is proposed. This algorithm finds feature points through continuous wavelet transformation and estimates baseline through segment interpolation. AWFPSI is compared with three commonly introduced fully automated and semi-automated algorithms, using simulated spectrum signal, visible spectrum signal and Raman spectrum signal. The results show that AWFPSI gives better accuracy and has the advantage of easy use.

  13. Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

    Science.gov (United States)

    Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

    2017-05-01

    In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

  14. Enhanced ATM Security using Biometric Authentication and Wavelet Based AES

    Directory of Open Access Journals (Sweden)

    Sreedharan Ajish

    2016-01-01

    Full Text Available The traditional ATM terminal customer recognition systems rely only on bank cards, passwords and such identity verification methods are not perfect and functions are too single. Biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. This paper presents a highly secured ATM banking system using biometric authentication and wavelet based Advanced Encryption Standard (AES algorithm. Two levels of security are provided in this proposed design. Firstly we consider the security level at the client side by providing biometric authentication scheme along with a password of 4-digit long. Biometric authentication is achieved by considering the fingerprint image of the client. Secondly we ensure a secured communication link between the client machine to the bank server using an optimized energy efficient and wavelet based AES processor. The fingerprint image is the data for encryption process and 4-digit long password is the symmetric key for the encryption process. The performance of ATM machine depends on ultra-high-speed encryption, very low power consumption, and algorithmic integrity. To get a low power consuming and ultra-high speed encryption at the ATM machine, an optimized and wavelet based AES algorithm is proposed. In this system biometric and cryptography techniques are used together for personal identity authentication to improve the security level. The design of the wavelet based AES processor is simulated and the design of the energy efficient AES processor is simulated in Quartus-II software. Simulation results ensure its proper functionality. A comparison among other research works proves its superiority.

  15. Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh

    OpenAIRE

    Mount, Nick J.; Tate, Nicholas J.; Sarker, Maminul H.; Thorne, Colin R.

    2013-01-01

    In this study continuous wavelet transforms are used to explore spatio-temporal patterns of multi-scale bank line retreat along a 204 km reach of the Jamuna River, Bangladesh. A sequence of eight bank line retreat series, derived from remotely-sensed imagery for the period 1987-1999, is transformed using the Morlet mother wavelet. Bank erosion is shown to operate at several characteristic spatial and temporal scales. Local erosion and bank line retreat are shown to occur in short, well def...

  16. An Extended Performance Comparison of Colour to Grey and Back using the Haar, Walsh, and Kekre Wavelet Transforms

    OpenAIRE

    Dr. H. B. Kekre; Dr. Sudeep D. Thepade; Adib Parkar

    2011-01-01

    The storage of colour information in a greyscale image is not a new idea. Various techniques have been proposed using different colour spaces including the standard RGB colour space, the YUV colour space, and the YCbCr colour space. This paper extends the results described in [1] and [2]. While [1] describes the storage of colour information in a greyscale image using Haar wavelets, and [2] adds a comparison with Kekre’s wavelets, this paper adds a third transform – the Walsh transform and pr...

  17. A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction

    Directory of Open Access Journals (Sweden)

    A. Wright

    2017-07-01

    Full Text Available The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT and discrete wavelet transform (DWT are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

  18. A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction

    Science.gov (United States)

    Wright, Ashley; Walker, Jeffrey P.; Robertson, David E.; Pauwels, Valentijn R. N.

    2017-07-01

    The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

  19. Research on two-port network of wavelet transform processor using surface acoustic wavelet devices and its application.

    Science.gov (United States)

    Liu, Shoubing; Lu, Wenke; Zhu, Changchun

    2017-11-01

    The goal of this research is to study two-port network of wavelet transform processor (WTP) using surface acoustic wave (SAW) devices and its application. The motive was prompted by the inconvenience of the long research and design cycle and the huge research funding involved with traditional method in this field, which were caused by the lack of the simulation and emulation method of WTP using SAW devices. For this reason, we introduce the two-port network analysis tool, which has been widely used in the design and analysis of SAW devices with uniform interdigital transducers (IDTs). Because the admittance parameters calculation formula of the two-port network can only be used for the SAW devices with uniform IDTs, this analysis tool cannot be directly applied into the design and analysis of the processor using SAW devices, whose input interdigital transducer (IDT) is apodized weighting. Therefore, in this paper, we propose the channel segmentation method, which can convert the WTP using SAW devices into parallel channels, and also provide with the calculation formula of the number of channels, the number of finger pairs and the static capacitance of an interdigital period in each parallel channel firstly. From the parameters given above, we can calculate the admittance parameters of the two port network for each channel, so that we can obtain the admittance parameter of the two-port network of the WTP using SAW devices on the basis of the simplification rule of parallel two-port network. Through this analysis tool, not only can we get the impulse response function of the WTP using SAW devices but we can also get the matching circuit of it. Large numbers of studies show that the parameters of the two-port network obtained by this paper are consistent with those measured by network analyzer E5061A, and the impulse response function obtained by the two-port network analysis tool is also consistent with that measured by network analyzer E5061A, which can meet the

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

    Energy Technology Data Exchange (ETDEWEB)

    Liao, T. W. [Louisiana State University; Ting, C.F. [Louisiana State University; Qu, Jun [ORNL; Blau, Peter Julian [ORNL

    2007-01-01

    Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the decomposition level, and the GA parameters are properly selected.

  1. Using Wavelet Bases to Separate Scales in Quantum Field Theory

    Science.gov (United States)

    Michlin, Tracie L.

    This thesis investigates the use of Daubechies wavelets to separate scales in local quantum field theory. Field theories have an infinite number of degrees of freedom on all distance scales. Quantum field theories are believed to describe the physics of subatomic particles. These theories have no known mathematically convergent approximation methods. Daubechies wavelet bases can be used separate degrees of freedom on different distance scales. Volume and resolution truncations lead to mathematically well-defined truncated theories that can be treated using established methods. This work demonstrates that flow equation methods can be used to block diagonalize truncated field theoretic Hamiltonians by scale. This eliminates the fine scale degrees of freedom. This may lead to approximation methods and provide an understanding of how to formulate well-defined fine resolution limits.

  2. Wavelet-Based Methodology for Evolutionary Spectra Estimation of Nonstationary Typhoon Processes

    Directory of Open Access Journals (Sweden)

    Guang-Dong Zhou

    2015-01-01

    Full Text Available Closed-form expressions are proposed to estimate the evolutionary power spectral density (EPSD of nonstationary typhoon processes by employing the wavelet transform. Relying on the definition of the EPSD and the concept of the wavelet transform, wavelet coefficients of a nonstationary typhoon process at a certain time instant are interpreted as the Fourier transform of a new nonstationary oscillatory process, whose modulating function is equal to the modulating function of the nonstationary typhoon process multiplied by the wavelet function in time domain. Then, the EPSD of nonstationary typhoon processes is deduced in a closed form and is formulated as a weighted sum of the squared moduli of time-dependent wavelet functions. The weighted coefficients are frequency-dependent functions defined by the wavelet coefficients of the nonstationary typhoon process and the overlapping area of two shifted wavelets. Compared with the EPSD, defined by a sum of the squared moduli of the wavelets in frequency domain in literature, this paper provides an EPSD estimation method in time domain. The theoretical results are verified by uniformly modulated nonstationary typhoon processes and non-uniformly modulated nonstationary typhoon processes.

  3. Simulasi Unjuk Kerja Discrete Wavelet Transform (DWT dan Discrete Cosine Transform (DCT untuk Pengolahan Sinyal Radar di Daerah yang Ber-Noise Tinggi

    Directory of Open Access Journals (Sweden)

    Raisah Hayati

    2014-03-01

    Full Text Available Detection of low signal and determination target locations is the basis and important in the system radar. Performance of radar can enhanced with enhancement signal-to-noise ratio in the receiver. In this research, will show a algorithm in radar signal processing, that is for extract the signal target in the place of noise. Discrete Cosine Transform (DCT and Discrete Wavelet Transform (DWT is the success full mathematic function in the signal processing in the last twenty years. In this research will simulate signal with DCT and DWT, analysis his performance in radar signal processing. DWT signal processing will analysis and compare with mother wavelet Haar, Daubechies-12, Coiflet-5 and Symlet-8. DCT signal processing will analysis and compare with same of window function with use in signal restrictions. Window function have influence signal resolution in domain frequency. Window function that use in this research Rectangular, Hamming, Hanning and Dolph-Chebyshev. The result of simulation and analysis Is: mother wavelet with DWT, wavelet Daubechies-12 and Symlet-8 give the best performance and mother wavelet Haar give bad performance. Wavelet Daubechies-12 give the biggest signal to noise ratio that is 32,0603 dB. Mother wavelet Symlet-8 give 32,6589 dB. Mother wavelet Haar give 14,6692 dB. Testing window function DCT, window Dolph-Chebyshev give the best performance, with give the best separation of signal. Analysis of signal reflection that accept of radar give the result that DWT is better performance than DCT in breaking of noise.

  4. PREVISIÓN DE CRISIS EPILÉPTICAS USANDO TRANSFORMADA WAVELET Y CORRELACIÓN CRUZADA PREVENTION OF EPILEPTICAL CRISIS USING WAVELET TRANSFORM AND CROSS-CORRELATION

    Directory of Open Access Journals (Sweden)

    Claudia C. Botero Suárez

    2007-07-01

    Full Text Available Este artículo describe la detección de actividad precrisis mediante la aplicación de la correlación cruzada junto con la transformada Wavelet. La transformada Wavelet es aplicada a los datos EEG puros para la reducción y pre-procesamiento de las señales. Esta técnica de extracción de características provee las señales simplificadas para ser procesadas por medio de la técnica de correlación cruzada. El análisis ha sido realizado con un grupo de datos tanto precrisis como intercrisis, (incluyendo crisis agudas inducidas y crisis espontáneas recurrentes, con el fin de determinar su sensitividad y especificidad (tasa de falsas predicciones. Son determinados, adicionalmente, el período de ocurrencia de crisis y el horizonte de previsión de crisis.This paper describes the detection of a pre-crisis activity through the application of Cross-Correlation together with the Wavelet Transform. The Wavelet Transform is applied in the data reduction and pre-processing of signals. This feature extract technique provides the simplified signals to process by means of the Cross-Correlation technique. The analysis with a group of pre-crisis and inter-crisis data (including both induced acute crises and recurrent spontaneous crises, to determinate its sensitivity and its specificity (False Prediction Rate has been done. The seizure occurrence period and the seizure prediction horizon are calculated additionally.

  5. Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance.

    Science.gov (United States)

    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. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Method of identifying the friction of rotors using the wavelet transform; Metodo para identificar el rozamiento de rotores utilizado la transformada wavelet

    Energy Technology Data Exchange (ETDEWEB)

    Jauregui Correa, Juan Carlos; Rubio Cerda, Eduardo; Gonzalez Brambila, Oscar [CIATEQ, A.C., Queretaro (Mexico)

    2007-11-15

    The modern processes of signal analysis that measure mechanical vibrations are based on the fast transform of Fourier (FFT), nevertheless, this method is not able to identify transient phenomena nor of nonlinear nature. Although many efforts have been made to try to identify these phenomena in the frequency spectra, it is not possible to correlate the spectra with the physical characteristics of this type of phenomena. Within these phenomena on the rubbing of a rotor against the housing or trunnion of a bearing, this phenomenon has a nonlinear behavior, as it is demonstrated in this paper. In the first part a method based on the of signal analysis type wavelets is presented and how this technique can be used to predict transient and nonlinear phenomena. Once defined the method, its application in the identification of the friction of rotors is demonstrated. With this, one demonstrates that the method presented in this paper allows to also identifying in real time the rubbing phenomenon and also that it can be used as an of analysis technique in the preventive maintenance systems. [Spanish] Los procesos modernos de analisis de senales que miden vibraciones mecanicas se basan en la transformada rapida de Fourier (FFT por sus siglas en ingles), sin embargo, este metodo no es capaz de identificar fenomenos transitorios ni de naturaleza no lineal. A pesar de que se han hecho muchos esfuerzos para tratar de identificar estos fenomenos en los espectros de frecuencia, no es posible correlacionar el espectro con las caracteristicas fisicas de este tipo de fenomenos. Dentro de estos fenomenos sobre el rozamiento de un rotor contra la carcasa o munon de una chumacera, este fenomeno tiene un comportamiento no lineal, como se demuestra en este trabajo. En la primera parte se presenta un metodo basado en el analisis de senales tipo wavelets y como esta tecnica puede utilizarse para predecir fenomenos transitorios y no lineales. Una vez definido el metodo, se demuestra su

  7. Study of spectro-temporal variation in paleo-climatic marine proxy records using wavelet transformations

    Science.gov (United States)

    Pandey, Chhavi P.

    2017-10-01

    Wavelet analysis is a powerful mathematical and computational tool to study periodic phenomena in time series particu-larly in the presence of potential frequency changes in time. Continuous wavelet transformation (CWT) provides localised spectral information of the analysed dataset and in particular useful to study multiscale, nonstationary processes occurring over finite spatial and temporal domains. In the present work, oxygen-isotope ratio from the plantonic foraminifera species (viz. Globigerina bul-loides and Globigerinoides ruber) acquired from the broad central plateau of the Maldives ridge situated in south-eastern Arabian sea have been used as climate proxy. CWT of the time series generated using both the biofacies indicate spectro-temporal varia-tion of the natural climatic cycles. The dominant period resembles to the period of Milankovitch glacial-interglacial cycle. Apart from that, various other cycles are present in the time series. The results are in good agreement with the astronomical theory of paleoclimates and can provide better visualisation of Indian summer monsoon in the context of climate change.

  8. Instrument-independent analysis of music by means of the continuous wavelet transform

    Science.gov (United States)

    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.

  9. Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

    Science.gov (United States)

    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.

  10. A Wavelet Perspective on the Allan Variance.

    Science.gov (United States)

    Percival, Donald B

    2016-04-01

    The origins of the Allan variance trace back 50 years ago to two seminal papers, one by Allan (1966) and the other by Barnes (1966). Since then, the Allan variance has played a leading role in the characterization of high-performance time and frequency standards. Wavelets first arose in the early 1980s in the geophysical literature, and the discrete wavelet transform (DWT) became prominent in the late 1980s in the signal processing literature. Flandrin (1992) briefly documented a connection between the Allan variance and a wavelet transform based upon the Haar wavelet. Percival and Guttorp (1994) noted that one popular estimator of the Allan variance-the maximal overlap estimator-can be interpreted in terms of a version of the DWT now widely referred to as the maximal overlap DWT (MODWT). In particular, when the MODWT is based on the Haar wavelet, the variance of the resulting wavelet coefficients-the wavelet variance-is identical to the Allan variance when the latter is multiplied by one-half. The theory behind the wavelet variance can thus deepen our understanding of the Allan variance. In this paper, we review basic wavelet variance theory with an emphasis on the Haar-based wavelet variance and its connection to the Allan variance. We then note that estimation theory for the wavelet variance offers a means of constructing asymptotically correct confidence intervals (CIs) for the Allan variance without reverting to the common practice of specifying a power-law noise type a priori. We also review recent work on specialized estimators of the wavelet variance that are of interest when some observations are missing (gappy data) or in the presence of contamination (rogue observations or outliers). It is a simple matter to adapt these estimators to become estimators of the Allan variance. Finally we note that wavelet variances based upon wavelets other than the Haar offer interesting generalizations of the Allan variance.

  11. Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2016-07-01

    Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

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

    Science.gov (United States)

    Daqrouq, K; Dobaie, A

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB.

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

    Directory of Open Access Journals (Sweden)

    K. Daqrouq

    2016-01-01

    Full Text Available An investigation of the electrocardiogram (ECG signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF obtained from the percentage energy (PE of terminal wavelet packet transform (WPT subsignals. In addition, the average framing percentage energy (AFE technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD, logarithmic difference signal ratio (LDSR, and correlation coefficient (CC. The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN environment, where the recognition rate was 81.48% for 5 dB.

  14. A zero-crossing technique for the multidetermination of thiamine HCl and pyridoxine HCl in their mixture by using one-dimensional wavelet transform.

    Science.gov (United States)

    Dinç, Erdal; Baleanu, Dumitru

    2003-04-01

    A new zero-crossing technique based on one-dimensional wavelet transform (WT) was developed and applied on a commercial vitamin product and binary mixtures containing thiamine HCl and pyridoxine HCl in the presence of the interference of the analysed signals. We selected from the data of the UV-Vis absorption spectra a signal consisting of 1150 points corresponding to the concentration range 8-32 mg/ml for both vitamins and we subjected it to one-dimensional continuous WT Mexican (MEXICAN) and Meyer (MEYER). Since the peaks of the transformed signals were bigger than original ones a zero crossing technique was applied to obtain the regression equations. The validity of Beer-Lambert law was assumed for the transformed signals. An appropriate scale setting was choosing to obtain an alternative calibration for each method. The basic concepts about wavelet method were briefly explained and MATLAB 6.5 software was used for one-dimensional wavelet analysis. The obtained results were successfully compared among each other and with those obtained by other literature methods. The developed method is rapid, easy to apply, not expensive and suitable for analysing of the overlapping signals of compounds in their mixtures without any chemical pre-treatment. Copyright 2003 Elsevier Science B.V.

  15. Optimal Wavelets for Speech Signal Representations

    Directory of Open Access Journals (Sweden)

    Shonda L. Walker

    2003-08-01

    Full Text Available It is well known that in many speech processing applications, speech signals are characterized by their voiced and unvoiced components. Voiced speech components contain dense frequency spectrum with many harmonics. The periodic or semi-periodic nature of voiced signals lends itself to Fourier Processing. Unvoiced speech contains many high frequency components and thus resembles random noise. Several methods for voiced and unvoiced speech representations that utilize wavelet processing have been developed. These methods seek to improve the accuracy of wavelet-based speech signal representations using adaptive wavelet techniques, superwavelets, which uses a linear combination of adaptive wavelets, gaussian methods and a multi-resolution sinusoidal transform approach to mention a few. This paper addresses the relative performance of these wavelet methods and evaluates the usefulness of wavelet processing in speech signal representations. In addition, this paper will also address some of the hardware considerations for the wavelet methods presented.

  16. Multiscale seismic characterization of marine sediments by using a wavelet-based approach

    Science.gov (United States)

    Ker, Stephan; Le Gonidec, Yves; Gibert, Dominique

    2015-04-01

    We propose a wavelet-based method to characterize acoustic impedance discontinuities from a multiscale analysis of reflected seismic waves. This method is developed in the framework of the wavelet response (WR) where dilated wavelets are used to sound a complex seismic reflector defined by a multiscale impedance structure. In the context of seismic imaging, we use the WR as a multiscale seismic attributes, in particular ridge functions which contain most of the information that quantifies the complex geometry of the reflector. We extend this approach by considering its application to analyse seismic data acquired with broadband but frequency limited source signals. The band-pass filter related to such actual sources distort the WR: in order to remove these effects, we develop an original processing based on fractional derivatives of Lévy alpha-stable distributions in the formalism of the continuous wavelet transform (CWT). We demonstrate that the CWT of a seismic trace involving such a finite frequency bandwidth can be made equivalent to the CWT of the impulse response of the subsurface and is defined for a reduced range of dilations, controlled by the seismic source signal. In this dilation range, the multiscale seismic attributes are corrected from distortions and we can thus merge multiresolution seismic sources to increase the frequency range of the mutliscale analysis. As a first demonstration, we perform the source-correction with the high and very high resolution seismic sources of the SYSIF deep-towed seismic device and we show that both can now be perfectly merged into an equivalent seismic source with an improved frequency bandwidth (220-2200 Hz). Such multiresolution seismic data fusion allows reconstructing the acoustic impedance of the subseabed based on the inverse wavelet transform properties extended to the source-corrected WR. We illustrate the potential of this approach with deep-water seismic data acquired during the ERIG3D cruise and we compare

  17. The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition.

    Science.gov (United States)

    Handojoseno, A M Ardi; Shine, James M; Nguyen, Tuan N; Tran, Yvonne; Lewis, Simon J G; Nguyen, Hung T

    2012-01-01

    Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as "feeling like their feet have been glued to the floor while trying to walk" and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG.

  18. Discrimination between monomorphic and polymorphic ventricular tachycardia using cycle length variability measured by wavelet transform analysis.

    Science.gov (United States)

    Sierra, G; Gómez, M J; Le Guyader, P; Trelles, F; Cardinal, R; Savard, P; Nadeau, R

    1998-07-01

    The objective of this study was to assess the capability of wavelet transform (WT) analysis to differentiate between monomorphic (MVTs) and polymorphic ventricular tachycardias (PVTs) in a canine model and to relate these results to epicardial isochronal maps on a beat-by-beat basis. Unipolar electrograms were simultaneously recorded from the surface of both ventricles with a 127-lead sock electrode array in 24 open-chest anesthetized dogs. The sampling frequency was 500 Hz. Atrioventricular block was induced by formaldehyde injection into the atrioventricular node. The left anterior descending coronary artery was occluded for 60 minutes under ventricular pacing (140 stimuli/min) followed by reperfusion. Ventricular tachycardias were obtained during reperfusion and during left stellate ganglion stimulation. After visual selection, a total of 97 segments of 2,048 samples (4.096 seconds) were extracted and classified as 67 MVTs and 30 PVTs. A parameter based on the cycle length variability was defined in the second scale of the WT decomposition, normalized by its mean value. Similar assessment of cycle length variability was performed based on the detection of the point of most rapid change in potential with a negative slope in excess of -0.5 mV/ms in each individual electrogram to test the accuracy of the results obtained with the WT parameter. The WT parameter correctly identified 97% MVT and 83.3% PVT segments, for an overall accuracy of 92.8%. Beat-by-beat epicardial maps of MVT displayed a cluster of sites of initial activation close to the reperfusion area, while the sites of breakthrough from beats during PVT were much more dispersed over both ventricles. A strong and significant correlation was found between the number of electrodes with the earliest epicardial activation and the WT parameter (r = .78, P accuracy of the results obtained, a comparison was performed between the WT parameter (0.082 +/- 0.007) and the cycle length variability, estimated as the

  19. The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

    Directory of Open Access Journals (Sweden)

    Rogelio Ramos

    2017-01-01

    Full Text Available The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI, embedded systems, and the discrete wavelet transform (DWT. The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

  20. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available 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.