一种结合EWT和成分分析的无线电指纹提取方法

无线电台信号个体识别主要是提取无线电信号中的杂散成分,通过对杂散成分进行分析达到个体识别的效果。针对线电信号杂散成分具有非线性、非平稳性的特点,本文将经验小波变换(EWT)和信号成分分析结合起来,提出了一种新的信号特征提取方法。该方法首先利用EWT对信号进行自适应的分解处理,通过选取部分能够表征个体差异的信号成分进行特征值谱分析,并以信号成分的归一化特征值谱的差异为依据进行信号指纹特征的提取,再根据指纹特征对信号进行识别。仿真结果表明,该方法与HHT和局部积分双谱分析方法相比,具有更加优越的识别性能,并且具有更加优良的特征稳定性,同时受信噪比的影响较小。     

基于MOMEDA与双谱分析的滚动轴承早期故障诊断

滚动轴承早期故障阶段,故障特征微弱且环境噪声干扰严重,采集数据包含大量噪声信息,传统的包络谱分析难以提取故障特征信息。双谱分析理论上可以抑制高斯噪声,但很难从强背景噪声下提取出微弱故障特征。而多点最优调整的最小熵解卷积（Multipoint Optimal Minimum Entropy Deconvolution Adjusted,MOMEDA）方法能增强信号中的冲击特征,但其效果和故障信号周期区间等参数有关。利用MOMEDA与双谱分析进行信号处理,将提取到的信号高阶谱特征作为滚动轴承早期故障分类依据。利用MOMEDA方法对采集信号进行滤波处理,提取出有冲击特征的时域信号;对特征增强的信号进行双谱分析,从高阶谱中提取故障特征。经过仿真信号分析和实际轴承故障信号验证,该方法能有效地提取出滚动轴承早期故障特征,实现故障诊断。     

基于高阶统计量的心音信号分析

对高阶统计量方法应用于心音信号分析进行了研究。建立了心音信号的AR双谱模型,获得了心音的双谱幅度图,并采用模型参数作为特征参量对心音信号进行了二类模式识别。实验结果表明,高阶统计量在心音信号分析和处理中具有较大的应用潜力。     

基于a参数平均的AR谱估计在心率变异性研究中的应用

利用α参数平均的AR估计法分析研究心率变异性信号的AR谱。分析结果表明，这种方法（特别是在低信噪比场合）能够明显改善了HRV信号AR谱的测频性能。     

On efficient multistep non-linear time series prediction in chaotic diode resonator circuits by optimizing the combination of non-linear time series analysis and neural networks

An improved novel non-linear time series prediction method is presented based on optimizing the combination of non-linear signal analysis and deterministic chaos techniques with Artificial Neural Networks of the Multilayer Perceptron (MLP) type. The proposed methodology has been applied to the non-linear time series produced by a diode resonator chaotic circuit. Multisim is used to simulate the circuit and show the presence of chaos. The first stage of the proposed approach employs a non-linear time series analysis module applying the method proposed by Grasberger and Procaccia, involving estimation of the correlation and minimum embedding dimension as well as of the corresponding largest Lyapunov exponent in combination with a nearest neighbour-based non-linear signal predictor. The two previously mentioned modules are used to construct the first stage of a one-step/multistep predictor while a back-propagation MLP is involved in the second stage to enhance prediction results. The novelty of the proposed two-stage predictor lies on that the back-propagation MLP is employed as an error predictor of the nearest neighbour-based first-stage non-linear signal forecasting application following an efficient strategy for optimizing the combination of nearest neighbour prediction based on deterministic chaos techniques and MLP neural networks. This novel two-stage predictor is evaluated through an extensive experimental study and is favourably compared with rival approaches.     

短丝纤维卷绕牵伸齿轮箱故障信号的图像特征提取方法

针对短丝纤维卷绕牵伸齿轮箱故障信号不易提取的问题,提出了基于图像纹理信息的特征提取方法。通过对齿轮箱振动信号进行小波包双谱分析,获得具有稳定纹理信息的振动信号双谱图,采用基于小波变换对双谱图进行图像融合,提高图像的综合纹理特征。采用灰度共生矩阵的四个特征参数对振动信号的双谱图进行加权融合特征提取。在短丝生产线上对齿轮箱常见的齿轮破损和裂纹进行了实验分析,结果表明本文方法的故障识别率达到85%以上。     

焊接缺陷超声检测回波信号的双谱分析

针对焊接缺陷超声检测中信号处理的特征提取问题，应用高阶谱方法对三类压力容器焊接缺陷的超声回波信号进行了分析，在焊接缺陷超声检测中，回波信号的相位携带有被检对象重要的结构特征信息。高阶谱方法与常规的功率谱分析方法不同，它不仅有振幅而且包含有相位，能揭示常规功率谱分析所不能表现的重要信息。本文应用高阶累积量技术对缺陷回波信号进行双谱分析，提取出缺陷回波基于双谱的平均相位信息作为特征参量，取得了较好的识别结果。     

Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72–88% goodness of accuracy) outperformed the other classifiers.     

Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training of supervised learning using features obtained from electrocardiogram (ECG) image to correct the limitations of arrhythmia prediction by using demographic and electrocardiographic signal features. An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning (APSL) method, whose features are obtained from the image formats of the electrocardiograms used as input.     

基于双谱二次特征的通信信号识别算法研究*

针对卫星通信中辐射源信号分类识别问题,开展基于双谱二次特征的卫星通信信号分类识别算法研究。通过辐射源信号的对角切片双谱获取信号特征,利用Chirp-Z变换将双谱对角切片特征从频域扩展至复平面,并提出扩展的基于巴式（Bhattacharyya）距离的分离度准则作为信号双谱二次特征提取依据,提取出具有最强可分离度特征作为特征参数,并通过支持向量机进行分类识别仿真实验。实验表明,本文识别算法适用于不同类型的卫星通信信号,且对噪声变化不敏感,可实现较高的正确识别率。     

Detection of obstructive sleep apnoea using dynamic filter-banked features

There is a need for developing simple signal processing algorithms for less costly, reliable and noninvasive Obstructive Sleep Apnoea (OSA) diagnosing. One of the promising directions is to provide the OSA analysis based on the heart rate variability (HRV), which clearly shows a non-stationary behavior. So, a feature extraction approach, being capable of capturing the dynamic heart rate information and suitable for OSA detection, remains an open issue. Grounded on discriminating capability of frequency bands of HRV activity between normal and OSA patients, features can be extracted. However, some HRV normal spectrograms resemble like pathological ones, and vice versa; so, prior to extract the feature set, the energy spatial contribution contained in each sub?band should be clarified. This paper presents a methodology for OSA detection based on a set of short-time feature banked features that are extracted from the spectrogram of the HRV time series. The methodology introduces the spectral splitting scheme, which searches for spectral components with alike stochastic behavior improving the OSA detection accuracy. Two different splitting approaches are considered (heuristic and relevance-based); both of them performing minute-by-minute classification comparable with other outcomes that are reported in literature, but avoiding more complex methods or more computed features. For validation purposes, the methodology is tested on 1-min HRV-segments estimated from 50 Physionet database recordings. Using a parallel combining k-nn classifier, the assessed dynamic feature set reaches as much as 80% value of accuracy, for both considered approaches of spectral splitting. Attained results can be oriented in research focused on finding alternative methods used for less costly and noninvasive OSA diagnosing with the additional benefit of easier clinical interpretation of HRV-derived parameters.     

Automatic sleep staging using empirical mode decomposition,discrete wavelet transform,time-domain,and nonlinear dynamics features of heart rate variability signals

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time–frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time–frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time–frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV segments classified by the LD classifier. A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging. Moreover, time–frequency features are more informative than others. In addition, a separability measure and classification results showed that HRV signal features, especially nonlinear features, extracted from 5-min segments are more discriminative than those from 0.5-min segments in automatic sleep staging.     

Detecting Chaos in HRV Signals in Human Cardiac Transplant Recipients

A statistical approach to chaos identification in time series is presented. The method is applied to numerical data generated by chaotic systems and to heart rate variability (HRV) signals of normal subjects and heart transplant recipients. This method compares the short-term predictability for a given time series to an ensemble of random data which has the same Fourier spectrum as the original time series. The short-term prediction error is computed as a discriminating statistic for performing statistical hypothesis testing. The results suggest that HRV signals of the transplant recipients recorded 3 months after the transplantations show the same signature of chaos as that of the HRV signals for normal subjects.     

Increasing sensitivity in the measurement of heart rate variability: The method of non-stationary RR time-frequency analysis

A novel method of the time-frequency analysis of non-stationary heart rate variability (HRV) is developed which introduces the fragmentary spectrum as a measure that brings together the frequency content, timing and duration of HRV segments. The fragmentary spectrum is calculated by the similar basis function algorithm. This numerical tool of the time to frequency and frequency to time Fourier transformations accepts both uniform and non-uniform sampling intervals, and is applicable to signal segments of arbitrary length. Once the fragmentary spectrum is calculated, the inverse transform recovers the original signal and reveals accuracy of spectral estimates. Numerical experiments show that discontinuities at the boundaries of the succession of inter-beat intervals can cause unacceptable distortions of the spectral estimates. We have developed a measure that we call the "RR deltagram" as a form of the HRV data that minimises spectral errors. The analysis of the experimental HRV data from real-life and controlled breathing conditions suggests transient oscillatory components as functionally meaningful elements of highly complex and irregular patterns of HRV.     

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods.     

Long-term heart rate variability as a predictor of patient age

Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component analysis has been used to capture age-related influence on HRV and then three different models have been applied to predict subjects age: a robust linear regressor (RLR), a feedforward neural network (FFNN) and a radial basis function neural network (RBFNN). A good prediction of patient age has been obtained (using all principal components, the Pearson correlation coefficient between predicted and real age: RLR=0.793; FFNN=0.872; RBFNN=0.829), even if an overestimation in younger subjects and an underestimation in older ones may be observed. The important and complementary contribution of non-linear indexes to aging related HRV modifications has also been underlined.     

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.     

基于码元高斯拟合的PUE攻击检测策略*

针对仿冒主用户（PUE）恶意干扰并占用有效频段所造成的频谱资源稀缺问题,提出了一种基于高斯函数特征提取的PUE攻击检测方法。在论证码元上包络起伏特征可以作为细微特征提取的基础上,结合高斯拟合,提取出不同用户发射源的特征参数,利用模糊C均值聚类算法来区分主用户与仿冒攻击用户。仿真实验证明,该方法在不同信噪比下所提取出的两辐射源特征差异明显,稳定性高,可靠性好,能够快速有效地检测出PUE攻击用户。     

An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods

Early diagnosis of prediabetes is an effective solution to the rising cases of diabetes around the world. The heterogeneous physiological characteristics of the ECG signal recorded from the heart make it challenging to implement an efficient diagnostic system. Therefore, this paper proposes a new approach to handling the heterogeneous characteristics of heart rate variability (HRV) with an absolute magnitude deviation analysis and an integrated machine learning technique for prediabetes prediction. We conducted an oral glucose tolerance test to acquire a resting-state ECG signal and the corresponding blood glucose value. We analyzed the HRV pattern from the ECG signal with a block-sliding window technique. We proposed a hybrid model to classify normal and prediabetes based on the extent of the absolute deviation of HRV values and avoiding a single point of failure. We adopted the model from the classification and regression tree (CART) and neural network (NN) algorithms. The experimental results reveal that when the blood glucose level increases, the maximum and range values of CARTHRV decreases while the minimum value increases. The proposed hybrid model had a better performance than the two methods with 100% sensitivity, specificity, and F1-score measures against CART and NN that recorded?<?100% for the same number of prediabetes in the training and test sets. The outcome from the analysis shows that the changes in blood glucose can be observed in ECG signals. The fast approximation of the proposed method to 100% accuracy suggests that it is possible to achieve the diagnosis of prediabetes and overcome the discrepancies in physiological signals among individuals.

     

基于形状标记和双谱分析的图像形状特征提取

针对严重滑动磨粒、疲劳剥块和层状磨粒等磨粒的图像识别问题, 提出了基于形状标记和双谱分析的图像形状特征提取方法. 首先根据中心距离函数、累积角函数、最远点距离函数和三角形区域表示等4种形状标记方法, 将二维磨粒图像转换为一维信号表示; 然后对一维信号进行双谱分析, 得到形状的归一化双谱; 最后在归一化双谱域内, 根据双谱积分和双谱矩计算双谱不变量, 得到图像的76维形状特征, 涵盖了形状的整体特征、角度变化信息、角点信息和轮廓细节信息等. 为了有效评价所提方法的有效性, 在MPEG-7 CE Shape-1 Part B数据集和Swedish leaf数据集上进行了形状识别能力实验与抗噪声能力实验. 实验结果表明, 所提方法能够有效提高双谱分析用于形状识别时的识别准确率和抗噪声能力.