Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

复杂体制雷达辐射源信号特征的集对分析

为解决复杂体制雷达辐射源信号特征的评价问题,提出了基于集对分析理论的复杂体制雷达辐射源信号特征分析模型.首先对复杂体制雷达辐射源信号特征评价指标体系的指标权重作层次分析,再通过集对分析理论对复杂体制雷达辐射源信号特征评估的决策矩阵进行同一度分析,最后联合指标权重矩阵和决策同一度矩阵对复杂体制雷达辐射源信号特征进行综合评判.仿真结果表明,该模型是有效、可行的,能够更好地实现对复杂体制雷达辐射源信号特征的分析和评估.     

安全仪表系统的可用性分析及其定量评估

安全仪表系统的误动作给生产带来较大经济损失,如何保证安全仪表系统既可靠又可用已提上议事日程。在分析安全仪表系统安全失效概率影响因素的基础上,采用可靠性框图法针对安全仪表系统常用表决结构的安全失效概率进行了定量研究,并给出了应用示例。研究结果表明,共因失效会增加安全仪表系统发生安全失效的概率;安全仪表系统降级工作不影响其可用性;在设备逐一测试和维修的情况下,可忽略由于测试和维修危险失效导致的安全仪表系统不可用;独立的未检测到的安全失效对安全仪表系统发生安全失效概率的贡献最大。     

全变分原理在地震数据去噪中的应用

地震图像资料的解释和后续处理中的重点环节是如何有效地降除地震资料记录中的噪声。高效的地震资料降噪算法在较好降除噪声的同时可以将图像的边缘信息予以较好的保留。论文首先全面地介绍了全变分去噪模型,同时详细介绍了参数的选取方法,最后进行实验和模型验证。结合小波阈值降噪方法进行比较,实验结果表明全变分降噪方法在很好地消除地震信号图像中的噪声,大面积提高信号剖面质量的同时,可以有效提高地震资料的信噪比。     

医疗大数据特征挖掘及重大突发疾病早期预警

通过运用新兴的尖端云计算技术,设计研发了健康云平台。采用分布式云存储技术存储大规模的异构多模态生理信号数据,将数据挖掘模型（L1-逻辑斯蒂）整合至MapReduce框架中,快速挖掘用户的健康信息以及重大疾病高风险因子,让用户能实时地了解自己的身体状况,同时对用户的异常状况给出预警信息,并通知其前往医院就医,实现了对重大突发疾病的早期预警。     

癫痫脑电的递归确定性分析

利用排序递归图的分析方法对癫痫脑电进行了确定性(DET)的分析,得出癫痫头皮脑电(EEG)的DET高于健康EEG。DET特征的差异性在局部导联上更明显,局部导联的DET特征可以作为癫痫疾病的自动诊断特征。通过分析发作阶段和发作间隙皮层脑电(ECoG)的DET,得出整个频带的DET差别不大,而在beta频带,发作阶段的确定性明显高于发作间隙的DET。Beta频带的DET特征可以作为癫痫发作的预测特征。研究结果为癫痫疾病的自动诊断和癫痫发作预测提供了理论依据。     

Acquired Predator Recognition by Fathead Minnows: Influence of Habitat Characteristics on Survival

In this study we conditioned fathead minnows (Pimephales promelas) to recognize the odor of a perch (Perca flavescens) by exposing them to perch odor coupled with minnow alarm cue. We then staged encounters between the predator and prey in order to assess whether the predator odor training had any effect on survival of the prey. We tested for a survival effect in the presence and absence of shelter. Our results indicate that fish trained with alarm signals to recognize predators gained a survival benefit during staged encounters with a predator and that habitat characteristics influenced the survival of conditioned fish.     

基于参数统计的数字信号类间识别

针对常用的4类数字信号MASK、MFSK、MPSK、16QAM,提出一种基于参数统计的识别算法。文章对延迟共轭相乘后的相位进行了详细的推导,并利用不同类信号的3个参数（基于瞬时幅度的统计参数A、延迟共轭相乘后的相位方差、差分运算后的包络方差）的差异实现了信号的类间识别。大量仿真实验及实际数据测试表明,本文方法简单,且在snr≥4dB时有较好的识别性能。     

时频联合分析评判火焰燃烧稳定性的实验研究

通过改变燃气量和一级空气量完成一系列燃烧工况。采用低成本的火焰检测装置采集得到各个工况火焰辐射的脉动信息,利用快速傅里叶变换算法将获得的时域信号衍生出频域上的功率谱信号。从时域信号的周期性能和频域信号中主峰频率的波动情况提取出反应火焰脉动性能的参数:火焰强度比方差和火焰闪烁频率,通过联合分析得到其随时间和燃烧状态变化的规律,实现对燃烧稳定性能的评判。