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Features of underwater echo extraction based on the stationary wavelet transform and singular value decomposition

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Chinese Journal of Acoustics 2006年第1期25卷 26-35页

作者： LIU Jianguo LI Zhishun LI Qiang College of Marine Engineering Northwestern Polytechnical University Xi'an 710072

A feature extraction method of underwater echo is proposed, which uses the redundancy property of SWT （the stationary wavelet transform） and the steady property of SVD （Singular Value Decomposition）. Since using singular values of SWT coefficients matrix as feature vectors, with the K-L transform to multi-subspace signal obtained from SWT, it is a feature compression method essentially. In contrast to the method of sub-bands energy feature based on discrete orthogonal wavelet transform （DWT）, better results of lake trial data are acquired with this method：（1） Under the same sample and distance within-class, distance between-class is larger than former; （2） Correct recognition rates are also higher than former, whether the training and testing samples are chosen from the same lake trail or not; （3） Sample sets varying, the variation range of correct recognition rates is far less than the former. Thus this method can obtain more robust, effective features and better correct recognition results.

关键词： singular value decomposition stationary wavelet transform orthogonal wavelet transform feature compression method of feature extraction range of

SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

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Chinese Journal of Electronics 2015年第1期24卷 205-211页

作者： CHANG Xia JIAO Licheng LIU Fang SHA Yuheng Institute of Image Processing and Understanding School of Mathematics and Information Science Beifang University of Nationalities Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China International Research Center for Intelligent Perception and Computation Xidian University School of Computer Science and Technology Xidian University

The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.

关键词： Synthetic aperture radar(SAR) image despeckling stationary wavelet transform Nonsubsampled contourlet transform Scale mixtures of Gaussians model.

Multiscale classification and its application to process monitoring

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第6期11卷 425-434页

作者： Yu-ming LIU Lu-bin YE Ping-you ZHENG Xiang-rong SHI Bin HU Jun LIANG Institute of Industrial Control State Key Lab of Industrial Control Technology Zhejiang University Hangzhou 310027 China

Multiscale classification has potential advantages for monitoring industrial processes generally driven by events in different time and frequency *** this study, we adopt stationary wavelet transform for multiscale analysis and propose an applicable scale selection method to obtain the most discriminative scale *** using the multiscale features, we construct two classifiers:(1) a supported vector machine(SVM) classifier based on classification distance, and(2) a Bayes classifier based on probability *** the SVM classifier, we use 4-fold cross-validation and grid-search to obtain the optimal *** the Bayes classifier, we introduce dimension reduction techniques including kernel Fisher discriminant analysis(KFDA) and principal component analysis(PCA) to investigate their influence on classification *** tested the classifiers with two simulated benchmark processes:the continuous stirred tank reactor(CSTR) process and the Tennessee Eastman(TE) *** also tested them on a real polypropylene production *** performance comparison among the classifiers in different scales and scale combinations showed that when datasets present typical scale features, the multiscale classifier had higher classification accuracy than conventional single scale *** also found that dimension reduction can generally contribute to a better classification in our tests.

关键词： Multiscale analysis stationary wavelet transform Multi-class classifier Feature extraction Process monitoring

fMRI time series analysis based on stationary wavelet and spectrum analysis

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Progress in Natural Science:Materials International 2006年第11期16卷 1171-1176页

作者： ZHI Lianhe~ 1,2,3 , ZHAO Xia~4, SHAN Baoci~1**, PENG Silong~5, YAN Qiang~1, YUAN Xiuli~1 and TANG Xiaowei~ 1,6 (1. Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China 2. Graduate School of Chinese Academy of Sciences, Beijing 100049, China 3. Department of Physics, Zhoukou Normal University, Zhoukou 466100, China 4. Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas 78229, USA 5. National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China 6. Department of Physics, Zhejiang University, Hangzhou 310027, China) [a]Key Laboratory of Nuclear Analysis Techniques Institute of High Energy Physics Chinese Academy of Sciences Beijing China [b]Graduate School of Chinese Academy of Sciences Beijing China [c]Department of Physics Zhoukou Normal University Zhoukou China [d]Research Imaging Center University of Texas Health Science Center San Antonio Texas USA [e]National ASIC Design and Engineering Center Institute of Automation Chinese Academy of Sciences Beijing China [f]Department of Physics Zhejiang University Hangzhou China

The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method.

关键词： fMRI stationary wavelet transform spectrum analysis data analysis.

Edge preserving super-resolution algorithm using multi-stage cascaded joint bilateral filter

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第1期8卷 37-51页

作者： Gunnam Suryanarayana Ravindra Dhuli School of Electronics Engineering VIT University Vellore-632014TamilnaduIndia

Super-resolution(SR)algorithms address the inabilities of poor imaging devices,there by producing high quality images with enhanced *** propose a new SR approach which produces sharp high resolution(HR)image using its low resolution(LR)*** proposed method uses geometric duality for spatially adapting covariance-based interpolation(CBI).To preserve edge information,a multi-stage cascaded joint bilateral filter(MSCJBF)is proposed as an intermediary *** edges are incorporated in the high frequency subbands obtained by the stationary wavelet transform(SWT),through nearest neighbor interpolation(NNI)*** to the NNI process,the high frequency subbands undergo two-lobed lanczos interpolation to achieve the desired resolution *** quantitative and qualitative analysis for various test images prove the superiority of our method.

关键词： Joint bilateral filter image super-resolution covariance based interpolation stationary wavelet transform

A comparison analysis of multipath extract methods in short-baseline applications of GPS

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Journal of Southeast University(English Edition) 2014年第3期30卷 315-317页

作者：崔冰波陈熙源东南大学仪器科学与工程学院南京210096

In order to eliminate the multipath errors existing in static short-baseline applications, a novel de-noising method based on a singular spectrum analysis （named as DSSA） is introduced to extract multipath signals. The multipath error is extracted from the double difference （DD） residuals by DSSA and then applied to the correct multipath error in subsequent measurements based on the correlation among adjacent epochs. Methods based on discrete wavelet transform （DWT） and stationary wavelet transform （SWT） are introduced as comparisons of DSSA based on analysis of a simulated signal. Real baseline residuals are tested to verify different extract methods. Results show that compared with the SWT, the DSSA improves the root mean square （RMS） of the residual by 48.6% and achieves a time reduction of 75.3%.

关键词： multipath extraction singular spectrum analysis stationary wavelet transform

Predicting wavelet-transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

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Journal of Data Analysis and Information Processing 2023年第1期11卷 49-68页

作者： Luyandza Sindi Mamba Antony Ngunyi Lawrence Nderu Department of Mathematics Institute for Basic Sciences Technology and Innovation The Pan African University Nairobi Kenya Department of Statistics and Actuarial Sciences Dedan Kimathi University of Technology Nyeri Kenya Department of Computing School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the stationary wavelet transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

关键词： Optimized Gated Recurrent Unit Approximation Coefficient stationary wavelet transform Activation Function Time Lag

On a Feature Extraction and Classification Study for PPG Signal Analysis

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On a Feature Extraction and Classification Study for PPG Sig...