Linear and synchrosqueezed time–frequency representations revisited: Overview,standards of use,resolution, reconstruction,concentration, and algorithms

Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects of their use, reconsidering some conventions and introducing new concepts and procedures to advance their applicability and value. Furthermore, a detailed numerical and theoretical study of three specific questions is provided, relevant to the application of these methods, namely: the effects of the window/wavelet parameters on the resultant TFR; the relative performance of different approaches for estimating parameters of the components present in the signal from its TFR; and the advantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for calculating (S)WFT and (S)WT, are freely available for download.     

An emboli detection system based on Dual Tree Complex Wavelet Transform and ensemble learning

The traditional visual and acoustic embolic signal detection methods based on the expert analysis of individual spectral recordings and Doppler shift sounds are the gold standards. However, these types of detection methods are high-cost, subjective, and can only be applied by experts. In order to overcome these drawbacks, computer based automated embolic detection systems which employ spectral properties of emboli, speckle, and artifact using Fourier and Wavelet Transforms have been proposed. In this study, we propose a fast, accurate, and robust automated emboli detection system based on the Dual Tree Complex Wavelet Transform (DTCWT). Employing the DTCWT, which does not suffer from the lack of shift invariance property of ordinary Discrete Wavelet Transform (DWT), increases the robustness of the coefficients extracted from the Doppler ultrasound signals. In this study, a Doppler ultrasound dataset including 100 samples from each embolic, Doppler speckle, and artifact signal is used. Each sample obtained from forward and reverse blood flow directions is represented by 1024 points. In our method, we first extract the forward and reverse blood flow coefficients separately using DTCWT from the samples. Then dimensionality reduction is applied to each set of coefficients and both of the reduced set of coefficients are fed to classifiers individually. Subsequently, in the view that the forward and reverse blood flow coefficients carry different characteristics, the individual predictors of these classifiers are combined using ensemble stacking method. We compare the obtained results with Fast Fourier Transform and DWT based emboli detection systems, and show that the features extracted using DTCWT give the highest accuracy and emboli detection rate. It is also observed that combining forward and reverse coefficients using stacking ensemble method improves the emboli and artifact detection rates, and overall accuracy.     

Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method

Condition assessment is one of the most important techniques to realize the equipment's health management and condition based maintenance (CBM). This paper introduces a preprocessing model of the bearing using wavelet packet–empirical mode decomposition (WP-EMD) for feature extraction. Then it uses self-organization mapping (SOM) for the condition assessment of the performance degradation. To verify the superiority of the proposed method, it is compared with some traditional features, such as RMS, kurtosis, crest factor and entropy. Meanwhile, seventeen datasets from the bearing run-to-failure test are used to validate the proposed method. The analysis results from the bearing's signals with multiple faults show that the proposed assessment model can effectively indicate the degradation state and help us to estimate remaining useful life (RUL) of the bearings.     

Image quality/distortion metric based on α-stable model similarity in wavelet domain

Quality assessment is of central importance in numerous image processing tasks. State-of-the-art objective image quality assessment (IQA) algorithms are generally devised for specific distortion types or based on training procedure of large databases. In this work, we propose a general-purpose full-reference/no-reference (FR/NR) IQA framework for image distortions, nominated by Image Quality/Distortion Metric (IQDM). The leptokurtic and heavy-tailed behaviors of image wavelet coefficients are characterized by symmetric α-stable (SαS) density, and the statistical studies indicate that the model parameters may be altered because of the presence of distortion. This important priori knowledge of original image’s distribution is then used to gauge the distortion between degraded and reference SαS models in multi-scale wavelet sub-bands. We investigate the relationship between original and degraded parameters over scales, accordingly infer the original parameters from the degraded ones. A characteristic probability density function for SαS and its closed-form Kullback–Leibler distance are derived for FR/NR-IQDM using the model parameters. Extensive experiments and comparisons demonstrate that the proposed FR/NR-IQDM scheme is efficacious to most common types of distortion, and leads to a highly comparable performance to the benchmarks and prevalent competitors in consistency with subjective judgements.     

PAPR reduction based on entropy wavelet transform for Sniffer Mobile Robot

In this paper, the performance of wireless systems has been improved based on a proposition of a new peak detection algorithm based on the entropy of the wavelet transforms [1] and to be imposed to the Sniffer Mobile Robot (SNFRbot). This enhancement has been compared with a previously published work that uses a predistortion neural network (PDNN) architecture has been imposed to the SNFRbot. Moreover, the proposed method in this work has been applied to combat the Orthogonal Frequency Division Multiplexing (OFDM) system's drawback; namely peak-to-average power ratio (PAPR). Generally, the proposed algorithm consists of three stages; firstly, reconstruction based on wavelet transform, secondly, predetermined thresholding, and thirdly, imposing moving filter. This algorithm is called Multiple-Input Multiple-Output (MIMO)-OFDM system based entropy wavelet transform and to be imposed to SNFRbot work; SNFRbot-EWT.The performance of SNFRbot-EWT has been evaluated using a numerical analysis and a conducted simulation. The results have been compared with the our previously published work, SNFRbot-EWT gives an improvement reach to around 30% for the same bandwidth occupancy, which will reduce the cost and the complexity of the transceiver's structure.     

Semi-analytical formulation for the guided waves-based reconstruction of elastic moduli

In this study an inverse procedure based on the propagation of guided ultrasonic waves is proposed for the characterization of the elastic material constants of plates. The procedure consists of an optimization problem in which the discrepancy between the dispersion curves obtained through a semi analytical finite element (SAFE) formulation and numerical or experimental dispersion curves is minimized. The numerical dispersion curves were obtained from the application of the commercial finite element analysis software ANSYS. Finally experimental data were obtained by adopting a hybrid broadband laser/PZT ultrasonic set-up in a pitch-catch configuration. For both numerical and experimental data, the joint time-frequency analysis of the continuous wavelet transform was used.The optimization scheme proposed in this study is based on an improved version of the simplex search method. The scheme inputs an initial guess of the material parameters in the SAFE formulation. The values of these parameters are iteratively updated until the discrepancy between the SAFE-based group velocity dispersion curves and the numerical or experimental curves is minimized. The scheme is designed to minimize the discrepancy associated with the lowest symmetric and anti-symmetric order mode simultaneously.The validity of the SAFE method coupled to the inverse procedure scheme is tested to characterize the elastic material properties of a 2.54 mm thick aluminum plate. As the SAFE formulation is valid for waveguides of arbitrary cross-section the paper represents the first step toward the integration of an inversion scheme applicable into the SAFE algorithm to characterize the material properties of waveguides of complex geometries and various boundary conditions.     

A non-parametric segmentation methodology for oral videocapillaroscopic images

We aim to describe a new non-parametric methodology to support the clinician during the diagnostic process of oral videocapillaroscopy to evaluate peripheral microcirculation. Our methodology, mainly based on wavelet analysis and mathematical morphology to preprocess the images, segments them by minimizing the within-class luminosity variance of both capillaries and background. Experiments were carried out on a set of real microphotographs to validate this approach versus handmade segmentations provided by physicians. By using a leave-one-patient-out approach, we pointed out that our methodology is robust, according to precision–recall criteria (average precision and recall are equal to 0.924 and 0.923, respectively) and it acts as a physician in terms of the Jaccard index (mean and standard deviation equal to 0.858 and 0.064, respectively).     

基于二次样条小波变换的双向工频通信信号特征提取方法

本文介绍了双向工频自动通信系统的基本原理,提出了利用二次样条小波变换的奇异性检测特性实现双向工频通信信号特征提取的方法,仿真和实验结果证明该方法是有效和实用的。     

人工神经网络在近红外光谱建模中的应用及研究现状

对国内外近十年来人工神经网络在近红外光谱建模中的应用和研究进行了详细的综述,包括误差反向传播网络、径向基网络、支持向量机、自组织特征映射网、广义回归神经网络、概率神经网络、小波神经网络、模糊神经网络以及集成神经网络等的应用和研究。概括了这些网络的基本工作原理及优缺点。最后根据神经网络的发展方向和工农业的发展需求,提出了今后人工神经网络在近红外建模方面的发展方向。     

500kV输电线路故障诊断方法综述

对近几年国内外具有代表的中外文献进行了学习研究,重点论述了输电线路故障诊断的四种方法:阻抗法,神经网络和模糊理论等智能算法,小波理论,行波法。综合输电线路的四种故障诊断方法,建议采用小波熵原理对输电线路故障模型进行故障类型识别,运用基于小波熵的单端行波测距方法实现故障定位。