压缩感知中贪婪重构算法研究

压缩感知理论是利用信号的稀疏性,通过少量的观测值就可以实现对该信号的精确重构。贪婪类算法是压缩感知重构步骤中广泛应用的一类算法。该文主要对该类算法中典型的三种算法在存在噪声环境中进行了综合分析比较。首先从理论方面分析了三种算法,给出了实现过程;然后在不同稀疏度情况下,对三种贪婪算法重构性能进行综合比较。根据理论分析结果和仿真结果,得出相应的结论。     

基于压缩感知的CoSaMP算法自适应性改进

压缩感知重构算法在实际应用中需要预知信号稀疏度,而信号的稀疏度通常是未知的.为此,改进压缩采样匹配追踪(CoSaMP)算法的自适应性,提出一种稀疏度自适应贪婪算法.对信号稀疏度进行初始估计,结合SAMP算法思想,以残差值比对为终止条件,在CoSaMP算法框架下进行稀疏度逐步增大的递归运算,实现精确重构.仿真实验结果证明,该算法重构精度高、抗噪能力强,同时具备稀疏度自适应的特点.     

基于压缩感知的遥感成像稀疏重构性能分析

压缩感知是一种新型的信息论，打破了传统的Shannon-Nyquist采样定理，能够以少量数据完成信号采样。稀疏重构是压缩感知由理论到实际的关键环节，为了将压缩感知有效地应用于遥感成像领域，研究了稀疏重构对遥感成像过程的影响。针对稀疏重构理论模型，分析了重构误差的成因；同时，针对典型的凸优化类算法和贪婪类算法，利用峰值信噪比指标对遥感图像重构误差进行评价。在仿真实验中，定量考察遥感图像在不同压缩采样率、不同重构算法下的稀疏重构性能。结果表明，稀疏重构算法能够成功重构遥感图像，各算法在不同压缩采样率下均表现出了较好的重构质量，整体上能够满足遥感成像应用，验证了压缩感知稀疏重构方法在遥感成像中应用的可行性。     

用于CS的广义稀疏度自适应匹配追踪算法

压缩感知理论的基本思想是原始信号在某一变换域是稀疏的或者是可压缩的,并将奈奎斯特采样定理中的采样过程和压缩过程合二为一。稀疏度自适应匹配追踪（SAMP）算法能够实现稀疏度未知情况下的重构,而广义正交匹配追踪算法每次迭代时选择多个原子,提高了算法的收敛速度。基于上述两种重构算法的优势,提出了广义稀疏度自适应匹配追踪（Generalized Sparse Adaptive Matching Pursuit,gSAMP）算法。针对重构图像的峰值信噪比、重构时间、相对误差等客观评价指标,以及主观视觉上对所提算法与传统的贪婪算法进行对比。在压缩比固定为0.5时,gSAMP算法的重构效果优于传统的MP、OMP、ROMP、SAMP以及gOMP贪婪类重构算法的效果。     

稀疏信号重构的罚函数神经网络模型

在压缩感知理论中,针对未知信号的稀疏性和信号非零元素位置的不确定性使得稀疏信号的重构比较困难,以及基于贪婪迭代方法的匹配追踪算法和基于凸松弛方法的基追踪算法对稀疏信号的重构概率不高的问题,提出一个罚函数神经网络模型.首先在感知矩阵满足有限等距性(RIP)的前提下,压缩感知问题可以转化为等价的l1-范数最小化问题.然后基于罚函数的思想构造能量函数,建立了解决稀疏信号重构的神经网络模型,并对其收敛性和优化能力进行了理论分析.仿真实验结果表明,仅需较少的观测数,稀疏信号的重构概率就能接近100％;特别是在不同的观测数下,所提出的神经网络模型与正交匹配追踪(OMP)算法、压缩采样匹配追踪(CoSaMP)算法及l1-正则化最小二乘法(l1-LS)相比,信号的重构概率分别平均提高了4.93个百分点、14.07个百分点和2.73个百分点.     

基于压缩感知理论的WSNs时序信号分段压缩算法

针对压缩感知理论(CS)应用在无线传感器网络中时序信号在传输过程存在压缩比率低、通信能耗高等问题,提出了一种时序信号分段压缩算法来解决在信号稀疏度未知及高稀疏度条件下,压缩感知数据重构算法中存在的重构效率低,重构精度差,影响网络生命周期的问题.该算法将采集数据中非零元素个数作为分段依据,通过减少段内非零元素组合数量来提高信号重构精度,同时利用了压缩感知理论特性实现了对信号的高压缩率.实验结果表明,在以混沌量子免疫克隆重构(Q-CSDR)算法为重构算法、在信号盲稀疏度及稀疏度高于40的条件下,能够以大于0.4的压缩比率对信号进行压缩,其重构信号的均方误差小于0.01,能够延长网络寿命2倍左右.     

联合弹性碰撞与梯度追踪的WSNs压缩感知重构

为提高压缩感知(Compressed sensing,CS)大规模稀疏信号重构精度,提出了一种联合弹性碰撞优化与改进梯度追踪的WSNs(Wireless sensor networks)压缩感知重构算法.首先,创新地提出一种全新的智能优化算法|弹性碰撞优化算法(Elastic collision optimization algorithm,ECO),ECO模拟物理碰撞信息交互过程,利用自身历史最优解和种群最优解指导进化方向,并且个体以N(0,1)概率形式散落于种群最优解周围,在有效提升收敛速度的同时扩展了个体搜索空间,理论定性分析表明ECO依概率1收敛于全局最优解,而种群多样性指标分析证明了算法全局寻优能力.其次,针对贪婪重构算法高维稀疏信号重构效率低、稀疏度事先设定的缺陷,在设计重构有效性指数的基础上将ECO应用于压缩感知重构算法中,并引入拟牛顿梯度追踪策略,从而实现对大规模稀疏度未知数据的准确重构.最后,利用多维测试函数和WSNs数据采集环境进行仿真,仿真结果表明,ECO在收敛精度和成功率上具有一定优势,而且相比于其他重构算法,高维稀疏信号重构结果明显改善.     

稀疏补分析子空间追踪算法

针对压缩感知理论的稀疏分析模型下的子空间追踪算法信号重构概率不高、重构性能不佳的缺点,研究了此模型下的稀疏补子空间追踪信号重构算法;通过选用随机紧支框架作为分析字典,设计了目标优化函数,改进优化了稀疏补取值方法,改进了算法迭代过程,实现了改进的稀疏补分析子空间追踪新算法(IASP).实验结果证明,所提算法的信号完全重构概率明显高于分析子空间跟踪(ASP)等5种算法的信号完全重构概率;对于含高斯噪声的信号,所提算法重构信号的整体平均峰值信噪比明显超过ASP等3种算法整体平均峰值信噪比(PSNR),但略低于贪婪分析追踪(GAP)等2种算法的整体平均峰值信噪比.所提算法可用于语音和图像信号处理等领域.     

压缩感知重构匹配类算法分析

压缩感知理论是一种利用信号的稀疏性或可压缩性而把采样与压缩融为一体的新理论体系,它成功地克服了传统理论中采样数据量大、资源浪费严重等问题。该理论的研究方向主要包括信号的稀疏表示、测量矩阵的设计和信号的重构算法。其中信号的重构算法是该理论中的关键部分,也是近年来研究的热点。本文主要对匹配追踪类重构算法作了详细介绍,并通过仿真实验结果对这些算法进行了对比和分析。     

一种基于块稀疏的1比特压缩感知重构算法

块稀疏信号作为一种典型的稀疏信号,在压缩感知重构算法中被广泛应用研究,但是普通的重构算法并不能挖掘其内部结构,这导致重构精度得不到提高。在此基础上,针对普通的1比特压缩感知重构算法在块稀疏信号的重构中不能表现出良好的重构性能的问题,提出了一种专门针对块稀疏信号的1比特压缩感知重构算法。该算法以每一个块为重构单元,在二进制迭代硬阈值算法模型下进行重构。实验数据表明,提出的BLOCK-BIHT算法的重构精度比BIHT算法提高了3dB。     

Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform

One of the research problems investigated these days is early fault detection. To this end, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a gearbox. In order to evaluate its technical condition, artificial neural networks were used. Early fault detection based on support vector machines is a relatively new and rarely employed method for evaluating condition of machines, particularly gearboxes. The available literature offers very promising results of using this method. In order to compare the obtained results, a multilayer perceptron network was created. Such standard neural network ensures high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this end, a wavelet transform was used. The presented results concern both a “raw” vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to improve significantly the accuracy of condition evaluation and the results obtained by the two networks are consistent with one another.     

并行格型结构实现——一维块时间递归实值离散Gabor变换

为了有效和快速地计算实值离散Gabor变换,本文提出了在临界抽样条件下,一维块时间递归实值离散Gabor变换系数求解算法和由变换系数重建原信号算法,并研究了并行格型结构实现这两种算法的方法。     

空气弹簧隔振台特性试验及信号处理算法研究

超精密测量对环境振动要求非常严格,其仪器设备中多安装隔振装置。为评估某重点实验室圆度仪中使用的仪用小型空气弹簧隔振台的隔振性能,利用压电式加速度传感器设计振动测试试验。根据振动测试中信号的实际情况,设计信号处理算法,对采集到的加速度信号进行预处理、积分运算、频谱分析,消除信号中低频趋势项和干扰噪声,还原实际振动状况,准确获取隔振系统振动位移曲线及其固有频率。试验表明,该空气弹簧隔振系统各项指标满足隔振要求。信号处理算法对振动测试中的加速度信号处理具有一定指导意义,也可作为故障诊断中加速度信号处理的参考。     

Quantification of condition indicator performance on a split torque gearbox

The requirement for higher energy density transmissions (lower weight) in helicopters has led to the development of the split torque gearbox (STG) to replace the traditional planetary gearbox by the drive train designer. This may pose a challenge for the current gear analysis methods used in health and usage monitoring systems (HUMS). Gear analysis uses time synchronous averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous components (gears or bearing) in close proximity may significantly reduce the fault signal (reduce signal to noise ratio) and therefore reduce the effectiveness of current gear analysis algorithms. In this paper, quantification of condition indicator performance on a split torque gearbox is reported. The vibration signatures are processed through a number of gear analysis algorithms to quantify the gear fault performance. The performance metric is separability.     

Tracking the condition number for RLS in signal processing

We apply a fast adaptive condition estimation scheme, calledACE, to recursive least squares (RLS) computations in signal processing.ACE is fast in the sense that onlyO(n) operations are required forn parameter problems, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet + 1. RLS algorithms for linear prediction of time series are applied in various fields of signal processing: identification, estimation, and control. However, RLS algorithms are known to suffer from numerical instability problems under finite word-length conditions, due to ill-conditioning. We apply adaptive procedures, linear in the order of the problem, for accurately tracking relevant extreme eigen-values or singular values and the associated condition numbers over timet. In this paper exponentially weighted data windows are considered. The sliding data window case, which involves downdating as well as updating, is considered else-where. Numerical experiments indicate thatACE yields an accurate, yet inexpensive, RLS condition estimator for signal processing applications.Research supported by the Air Force under Grant No. AFOSR-88-0285 and by the National Science Foundation under Grant No. DMS-89-02121.     

Optimal selection of wavelet basis function applied to ECG signal denoising

Over the years ElectroCardioGram (ECG) signal has been used to assess the cardiovascular condition of humans. In practice, real time acquisition and transmission of the ECG may contain noise signals superimposed on it. In general, the signal processing algorithms employed for denoising provide optimal performance and eliminate the high frequency noise between any two beats contained in a continuous ECG signal. Despite their optimal performance, the signal processing algorithms significantly attenuate the peaks of characteristics wave of the ECG signal. This paper presents a selection procedure of mother wavelet basis functions applied for denoising of the ECG signal in wavelet domain while retaining the signal peaks close to their full amplitude. The obtained wavelet based denoised ECG signals retain the necessary diagnostics information contained in the original ECG signal.     

CSMP:基于约束等距的压缩感知匹配追踪

压缩感知包括压缩采样与稀疏重构,是一种计算欠定线性方程组稀疏解的方法.大规模快速重构方法是压缩感知的研究热点.提出一种匹配追踪算法CSMP,采用迭代式框架和最佳s项逼近以逐步更新信号的支集与幅度.基于约束等距性质进行收敛分析,算法收敛的充分条件为3s阶约束等距常数小于0.23,松弛了匹配追踪重构s稀疏信号的约束等距条件,加快了收敛速度.为适用于大规模稀疏信号重构,提供了可进行随机投影测量子集与稀疏基子集选择的矩阵向量乘算子,可利用离散余弦变换与小波变换,避免了大规模矩阵的显式存储.在220随机支集的稀疏高斯信号,512×512Lenna图像上进行压缩采样与稀疏重构实验并与其他算法进行比较,结果表明所提算法快速稳健,适用于大规模稀疏信号重构.     

An Intelligent Online Monitoring and Diagnostic System for Manufacturing Automation

Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1)extracting appropriate features from sensor signals and 2)recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal patterns which will be incorporated into a novel condition monitoring system. The sensor-based intelligent system has been implemented in stamping operations as an example. We demonstrate that the proposed method is substantially more effective than the previous approaches. Its unique features benefit various real-world manufacturing automation engineering, and it has great potential for shop floor applications.     

基于空间复用的信号检测算法研究

为了在接收端恢复出发送端的原始数据,需要在接收端进行信号检测。对几种经典的传统信号检测算法进行了详细阐述和分析,并对各种算法进行了Matlab仿真和性能比较。由此得出,改进型的V-BLAST算法可以用于TD-LTE无线综合测试仪的开发。