基于均匀圆阵相干信源的二维DOA估计

随着通信技术的不断发展,信号传输环境变得日益复杂。针对多径传播形成的高度相关和相干信号测向问题,提出了一种基于均匀圆阵相干信源的二维DOA估计方法。该方法利用均匀圆阵轴向虚拟平移解相干,通过去噪后利用虚拟子阵的自协方差矩阵和互协方差矩阵构造波达方向矩阵,利用该矩阵特征分解估计信号的俯仰角;然后将平滑后的自协方差矩阵与波束空间变换矩阵相乘,使圆阵的导向矢量具备范德蒙结构,最后用求根MUSIC算法估计出信号的方位角,完成了相干信号的二维DOA估计。该方法无需二维谱峰搜索,方位角和俯仰角自动配对,计算量小,分辨率高。仿真实验证明了所提方法的正确性。     

基于FMCW雷达的自适应生命信号提取方法

为了进一步提高调频连续波（FMCW）雷达自适应提取生命信号能力,通过提高距离分辨率提取被测目标位置距离维处的人体生命信号,利用改进的快速互补集合经验模态分解（IFCEEMD）对生命信号分解,从分解得到的若干个固有模态函数（IMFs）中利用筛选准则分离出呼吸、心跳信号。实验结果表明,所提出的方法能够快速、准确地提取出不同呼吸状态下的呼吸频率和心跳频率,并且有效地消除人体身体随机抖动带来的干扰。     

基于曲率和的主动声呐干涉条纹特征表征方法

干涉条纹特征是主动声呐目标识别所依据的重要特征之一。在浅水域环境中,声波能量传播特性较为复杂,信号会经过发射源到目标以及目标到接收器这2条不同的路径,目标的散射特性在声波的入射模式和散射模式之间相互作用,使得主动声呐干涉条纹呈现弯曲的结构。为对干涉条纹特征进行表征,提出一种基于曲率和的声呐特征提取方法。根据浅海声场主动声呐频率-距离干涉条纹图得到一段区间内的干涉条纹曲线散点曲率值,利用曲率求和的方法刻画条纹的弯曲程度,进而表征这一区间内的条纹特征。在理想波导环境下计算刚性球体目标在不同频率段下的曲率特征,通过统计平均值的方式得到平均曲率和为0.6左右,从特征表征结果可以看出,曲率和可通过统计离散点曲率强度描述干涉条纹特征,且在同一环境条件下,发射频率越大时曲率和强度越小。根据实测数据进行计算得到曲率和为0.512,实验与理论结果较为吻合,表明通过该方法可以获取主动声呐干涉条纹特征,进而实现目标识别和探测。     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

RFID Positioning and Physiological Signals for Remote Medical Care

The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease. The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use. The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations, and enables better care of the elderly and patients with mobility issues. It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies. The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear.     

基于多通道脑电信号的因果网络情绪识别

情绪是由大脑内多个通道共同作用产生的,格兰杰因果检验作为情绪识别的主流方法,在计算任意2个通道之间的因果关系时容易忽略其他通道的影响。面向多通道脑电信号,提出一种基于条件格兰杰因果检验（CGC）的因果网络情绪识别方法。利用CGC算法计算不同情绪下大脑全通道的因果关系,据此构建因果网络,并通过分析各通道的入/出度和介数拓扑属性找到关键通道,得到简化的因果网络进行情绪识别。将节点之间的因果连接关系作为特征分别输入SVM和KNN分类器进行分类训练,实验结果表明,简化网络的识别率分别为75.3%和78.4%,验证了所提方法的有效性。     

滚筒式采煤机煤岩截割力学特性及测试系统研究

为实现采煤机在煤岩截割过程中滚筒载荷受力的测试与分析,现场建立等比例大型采煤机力学特性分析研究实验平台,采用等效结构惰轮轴传感器测试方法,针对滚筒式采煤机在煤岩截割过程中采煤机滚筒载荷变化进行实时动态在线监测,采用无线信号发射装置实现测试数据的实时传输。现场截割实验结果表明,在采煤机截割煤岩过程中,滚筒x,y,z三向上的载荷峰值差分别为29.941,17.459和7.371kN,载荷变化显著,测试结果符合现场实际工况,为实现采煤机煤岩动态识别以及自动调高控制提供了重要的理论和数值依据。     

长寿命路面疲劳寿命预测的频域分析方法

笔者分别从沥青路面和水泥混凝土路面两个方面分析了路面疲劳寿命预测的相关方法。重点对疲劳寿命预测的频域分析方法进行了较为详细的分析,结合车辆–道路耦合振动系统模型,对路面不平整引起的车辆与道路之间的随机振动进行了分析,指出车辆–道路耦合振动的随机功率谱密度是进行路面疲劳寿命预测的一种手段和方法,并提出了相应的预测步骤,旨在为长寿命路面的设计和性能评价提供参考。     

Feed-direction ultrasonic vibration−assisted milling surface texture formation

The formation mechanism of surface texture for feed-direction ultrasonic vibration?assisted milling (UVAM) was investigated in this study by establishing a kinematic model and a pressing model of UVAM. The kinematic model showed that the cutter tip, which was supposed to be totally sharp, produced closed scratches by crossed trajectories. The variation trends of the interval for closed textures were of sine function. A comparative experiment was carried out by UVAM and conventional milling. A dividing line close to the X coordinate divides the surface feature into two parts. The pressing model showed that the tool minor cutting edge left clear traces with certain width because of the tool minor cutting edge angle. Scratches by tool minor cutting edge were intermittent and regularly varied when feed-direction vibration was introduced. All the surface feature changes are in the radial direction and the trajectory intersections shall always be the scratch grooves or ridges. The ratio between ultrasonic vibration frequency and spindle speed, tool radius, and the located cutter rotation angle affected the changing rule of scratches by tool minor cutting edge. The analytical models and the experimental results proved to each other reasonable.     

A Modified Region Approach for Multivariate Measurement System Capability Analysis

Measurement system capability analysis is to determine whether the measurement system is capable for use in quality control. The existing research has been extended from univariate to multivariate cases. Two approaches, the multivariate analysis of variance (MANOVA) and the weighted principal components (WPC), were advocated in literature. The MANOVA method is constructed based on the volume ratio that treats the volume of constant‐density contours as the variability estimations. However, it ignores the fact that the relative position change of multivariate measurement errors could affect the measurement system capability. The WPC method uses dimension reduction to reduce the complexity but is unable to build the precision‐to‐tolerance ratio because it does not include tolerance. In this paper, we propose a modified‐region‐based method to compute the precision‐to‐tolerance ratio, the percent of repeatability and reproducibility, and the signal‐to‐noise ratio. This method also incorporates the variance–covariance structure of the measurement errors when dealing with the constant‐density contours of tolerances, total variation, and process variation. The performance of the modified‐region‐based method is evaluated based on a dataset from the literature and a set of relevant simulation. The proposed method proves to be effective compared with other methods.Copyright © 2014 John Wiley & Sons, Ltd.