半张量积低存储压缩感知方法研究

由于随机观测矩阵的随机性,存在数据存储量大、内存占用率高、数据计算量大以及难以面向大规模实际应用等问题.为此,提出了一种可有效降低随机观测矩阵所占存储空间的半张量积压缩感知（STP-CS）方法.利用该方法,构建低维随机观测矩阵,经奇异值分解（SVD）优化后对原始信号进行采样,并利用拟合0-范数的迭代重加权方法进行重构.实验利用2维灰度图像进行测试,并对重构图像的峰值信噪比,结构相似度等指标进行了统计和比较.实验结果表明,本文所述的STP-CS方法在不改变随机观测矩阵数据类型的前提下,可将观测矩阵减小至传统CS模型中观测矩阵所占内存空间的1/256（甚至更低）,同时仍保持很高的重构质量.     

Image compressed sensing based on wavelet transform in contourlet domain

Compressed sensing (CS) has been widely concerned and sparsity of a signal plays a crucial role in CS to exactly recover signals. Contourlet transform provides sparse representations for images, so an algorithm of CS reconstruction based on contourlet is considered. Meanwhile, taking into account the computation and the storage of large random measurement matrices in the CS framework, we are trying to introduce the wavelet transform into the contourlet domain to reduce the size of random measurement matrices. Several numerical experiments demonstrate that this idea is feasible. The proposed algorithm possesses the following advantages: reduced size of random measurement matrix and improved recovered performance.     

Time-varying channel estimation in OFDM systems based on multiple symbols BEM

In view of the problem of low frequency spectrum utilization for the time-varying channel estimation in orthogonal frequency division multiplexing(OFDM) systems based on basis expansion model (BEM),a new method based on multiple symbols BEM was proposed.Compared with the conventional method based on single symbol,the proposed method reduced the subcarriers for pilots.And by means of the baseline tilting technique,the new method mitigated the Gibbs phenomenon caused by Fourier series expansion.The theoretical analysis reveals that the channel model error is reduced due to the introduction of the baseline tilting technique when the normalized Doppler frequency is no more than $\frac{\frac{1}{2}-\frac{1}{M}}{1+\frac{N_g}{N}}$.Finally,the numerical simulation shows that compared with conventional methods,the proposed algorithm achieves significant improvement on the estimation error performance and bit error rate (BER) performance under the conditions of the corresponding time-varying channel.     

Data comparable encryption scheme based on cloud computing in Internet of things

The previously proposed short comparable encryption (SCE) scheme can infer the plaintext relationship by comparing the ciphertexts relationship as well as ensuring data security in Internet of things.Unfortunately,it will incur high storage and computational burden during the process of comparing ciphertexts and generating tokens.To this end,an efficient short comparable encryption scheme called SCESW was proposed,which was utilizing the sliding window method with the same size window.Formal security analysis shows that the scheme can guarantee weak indistinguishability in standard model as well as data security and integrity.The experimental results demonstrate that the storage of the SCESW scheme is $\frac{1}{t}(t＞1)$ times shorter than that of the SCE scheme and the efficiency of the SCESW scheme is superior to that of the SCE scheme.     

基于随机滤波的探地雷达成像方法

探地雷达是一种超宽带雷达系统,若按传统的奈奎斯特采样,雷达回波信号需要大量空间存储。压缩感知可以实现利用少量的测量值对稀疏信号进行重构,其中最为关键的是测量矩阵和重构算法的选择。本文将压缩感知应用于探地雷达成像,并利用随机滤波的思想选择测量矩阵,可以有效减少测量矩阵中非零值的个数。利用正交匹配追踪算法对信号进行重构,算法简单,降低了数据的存储量和运算复杂度,该算法同样可以对时间和空间上同时压缩的数据进行成像。最后,本文给出基于时间连续信号的GPR接收机一种CS实现方案。仿真结果表明,本文提出的成像方法可以以少量数据精确地对信号进行重构,并且运算量少。      

用信道编码构造压缩感知测量矩阵

压缩感知是近年来,针对稀疏信号和可压缩信号的处理而出现的一种信号处理理论。测量矩阵是压缩感知理论中的一个至关重要的环节,它对信号采样和重构算法有着重要的影响。虽然一般传统的随机测量矩阵重建信号效果比较好,但有硬件实现比较困难的问题,并需要大量的存储空间和其他缺陷。确定性测量矩阵的出现,正好弥补了这些缺点。在本文中,基于信道编码中校验矩阵特性的优势,获得了满足有限紧致特性要求的确定性测量矩阵构造方法。把校验矩阵的列向量标准化、线性组合扩展到方阵、置换列向量后构成的矩阵作为确定性测量矩阵。这种方法可以在构造完成一个信道编码校验矩阵后,很容易构造对应的测量矩阵。数值结果表明,在相同重建算法和压缩比下,这种方法的性能和随机测量矩阵大致相若,甚至有所改善。同时,本文提出方法的构造时间较少,重建时只需要运行一次,可以满足实时性需求。为压缩感知算法的实际应用提供了一种有效的测量矩阵构造方法。      

Saliency-guided compressive sensing approach to efficient laser range measurement

The acquisition of laser range measurements can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many range measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). A new saliency-guided CS-based algorithm for improving the reconstruction of range image from sparse laser range measurements has been developed. This system samples the object of interest through an optimized probability density function derived based on saliency rather than a uniform random distribution. Particularly, we demonstrate a saliency-guided sampling method for simultaneously sensing and coding range image, which requires less than half the samples needed by conventional CS while maintaining the same reconstruction performance, or alternatively reconstruct range image using the same number of samples as conventional CS with a 16 dB improvement in signal-to-noise ratio. For example, to achieve a reconstruction SNR of 30 dB, the saliency-guided approach required 30% of the samples in comparison to the standard CS approach that required 90% of the samples in order to achieve similar performance.     

Security analysis of weighted network anonymity based on singular value decomposition

The security of anonymous method based on singular value decomposition (SVD) in the privacy preserving of weighted social network was analyzed.The reconstruction method in network with integer weights and the inexact reconstruction method in network with arbitrary weighted were proposed.The $\frac{\epsilon }{N}$ -tolerance was definited to measure its safety.It was also pointed out that the upper bound of ε (the reconfigurable coefficient) obtained in current spectral theories was so conservative that lacks of guidance.The reconfigurable coefficients of random networks,Barabasi-Albert networks,small world networks and real networks were calculated by experiment.Moreover,the reconfigurable coefficients of double perturbation strategies based on SVD were also tested.Experimental results show that weighted social networks have different tolerances on spectrum loss,and there is a close relationship between its tolerance and network parameters.     

基于分块压缩感知的图像全局重构模型

已有的基于分块压缩感知（Block Compressed Sensing,Block CS）的图像重构模型采用相同的测量矩阵以块×块的方式获取数据,解决了传统CS方法中测量矩阵所需存储量较大的问题,但由于采用分块重构,没有考虑到图像的全局稀疏度,出现了大量的块效应。本文分析了图像分块重构产生块效应的三个主要原因:块稀疏度不均匀、频谱泄漏和块尺寸受限,提出了一种基于Block CS的图像全局重构模型。该模型在编码端采用高斯随机矩阵逐块作非相关测量;在解码端,引入排序算子,重新构造测量矩阵,该测量矩阵既适合于进行全局重构,又适合于分块测量的CS观测值,并仍与图像的稀疏矩阵高度不相关,所以其可充分利用图像的全局稀疏度进行CS重构。仿真实验表明,所提出的全局重构模型有效地消除了块效应现象,并且对块尺寸的变化有较强的鲁棒性。      

压缩感知理论及其应用前景

压缩感知理论是近年来提出的一种基于信号稀疏性的新兴采样理论。与通常的数据采样定理不同,该理论提出可以用远远少于传统采样定理所需的采样点数或观测点数恢复出原信号或图像。本文主要阐述了压缩感知中信号的稀疏表示、测量矩阵的设计及信号的重构算法等基本理论,论述了该理论的广阔应用前景。     

Construction of optimal zero correlation zone sequence set

The construction of zero correlation zone (ZCZ) sequence set was researched based on perfect sequences and orthogonal sequence set.With the method of constructing on finite field,the optimal zero correlation zone sequence sets were constructed,by changing the set O and R,multiple shift distinct ZCZ sequence sets could be obtained and the number of sets could be expended.Through the constructions,the length of ZCZ can be chosen flexibly to meet the requirements of different systems for channel delay under the condition of $p=\frac{Z}{N}$or$p^n=\frac{Z}{N}$,where p is a prime.     

交换超立方网络的(t, k)故障诊断度研究

故障诊断是网络系统修复的一个重要环节,PMC诊断模型是一种简单、易于理解的故障诊断模型。通过对以交换超立方网 为拓扑模型的多处理器系统进行结构分析,给出了该网络系统的一般化的故障诊断方法——(t,k)诊断方法,证明了在PMC模型下交换超立方网络 是 可诊断的,且是条件 可诊断的。结果表明,交换超立方网的(t,k)诊断度 大于其传统诊断度s+1,条件(t,k)诊断度 大于其传统条件诊断度4s?3。这些结果为交换超立方网络的故障诊断提供了重要的理论依据。     

基于分圆类构造卷积压缩感知测量矩阵

卷积压缩感知是近年来兴起的新型压缩感知技术。卷积压缩感知选用循环矩阵作为测量矩阵,其采样可以简化为卷积的过程,因此大大降低算法复杂度。该文基于分圆类构造适用于卷积压缩感知的测量矩阵,测量值通过利用确定性序列循环卷积信号,然后进行随机2次采样获得。该文构造的测量矩阵的相关性小于已有文献构造的测量矩阵的相关性。模拟仿真结果表明,该文构造的测量矩阵与同等条件下的随机高斯矩阵相比,可以更好地恢复稀疏信号;所构造的矩阵还可以应用于信道估计以及2维图像的重构。

     

An adaptive measurement scheme based on compressed sensing for wideband spectrum detection in cognitive WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.     

基于概率稀疏随机矩阵的压缩数据收集方法

测量矩阵设计是应用压缩感知理论解决实际问题的关键。该文针对无线传感器网络压缩数据收集问题设计了一种概率稀疏随机矩阵。该矩阵可在减少参与投影值计算节点个数的同时,让参与投影值计算的节点分布集中化,从而降低数据收集的通信能耗。在此基础上,为提高网络数据重构精度,又提出一种适用于概率稀疏随机矩阵优化的测量矩阵优化算法。仿真实验结果表明,与稀疏随机矩阵和稀疏Toeplitz测量矩阵相比,采用优化的概率稀疏随机矩阵作为压缩数据收集的测量矩阵可显著降低通信能耗,且重构误差更小。     

帧间自适应语音信号压缩感知

近年来提出的压缩感知是一种以低于传统奈奎斯特速率对信号采样可得到精确恢复的理论。该理论很快应用于简化传统的采样硬件、缩短采样时间、以及减少数据的存储空间。针对语音信号的传输问题,本文提出一种帧间自适应语音信号压缩感知的方法。在离散余弦变换域的语音信号具有稀疏性的前提下,以大量语音信号帧的分析统计为依据,提出一种基于语音帧能量分级和帧间位置惯性的语音信号自适应压缩感知算法。实验结果表明,能量自适应可以显著地提高语音信号的恢复质量,而位置自适应可以明显地减少语音信号的恢复时间,从而本文提出的算法可以用较少的恢复时间获得较好的恢复效果。      

Denoising recovery for compressive sensing based on selective measure

In order to reduce the effect of noise folding (NF) phenomenon on the performance of sparse signal recon-struction,a new denoising recovery algorithm based on selective measure was proposed.Firstly,the NF phenomenon in compressive sensing (CS) was explained in theory.Secondly,a new statistic based on compressive measurement data was proposed,and its probability density function (PDF) was deduced and analyzed.Then a noise filter matrix was constructed based on the PDF to guide the optimization of measurement matrix.The optimized measurement matrix can selectively sense the sparse signal and suppress the noise to improve the SNR of the measurement data,resulting in the improvement of sparse reconstruction performance.Finally,it was pointed out that increasing the measurement times can further enhance the performance of denoising reconstruction.Simulation results show that the proposed denoising recon-struction algorithm has a better improvement in the performance of reconstruction of noisy signal,especially under low SNR.     

随机间距稀疏三元循环相位掩膜矩阵可压缩成像

可压缩成像是一种新兴的基于压缩感知理论的新成像技术,其核心思想是如果空间场景是稀疏或可压缩,那么它可以用远少于经典的Nyquist采样数目的测量值捕获的足够信息重构原场景;构建合适的测量矩阵并易于使用物理实现压缩感知理论中对于图像的随机线性测量是可压缩成像理论实用化的关键之一。该文在研究Bernoulli和Circulant矩阵的基础上,提出一种新的随机间距稀疏三元循环相位掩膜矩阵。模拟实验结果表明,在可压缩双透镜成像系统单次曝光下,与Bernoulli和Bernoulli-Circulant相位掩膜矩阵相比,新相位掩膜矩阵的成像信噪比与之相当;但是该文提出的矩阵随机独立变元个数和非零元个数显著减少,易于数据存储与传输;更重要的是物理上更容易实现,重构时间是只有原来的约20%~50%。新的相位掩膜矩阵的研究对于可压缩成像理论的实际应用具有重要的意义。     

基于光滑0范数压缩感知的多光谱图像去马赛克算法

提出了一种基于压缩感知(CS)的多光谱滤波阵列(MSFA)的多光谱图像去马赛克算法(DMA)。 首先,通过将MSFA采样得 到马赛克图像的过程等效为CS理论中的感知矩阵采样的过程,并充分利用多光谱图 像的空间和谱间 相关性,通过在三维空间傅里叶基上对多光谱图像进行稀疏表示;然后由随机MSFA模式和CS 理论构造的测量矩阵对多光谱图像进行观测投影,最后采用CS重构算法求解0范 数下的最优化问 题,从而得到多光谱图像的稀疏表示系数。给出对算法性能的评估数据和Matlab仿真 图片。实验结果证明,本文算法的峰值信噪比(PSNR)值高于克罗内克CS(KCS)和组稀疏(GS)两种算法,且有效地减少了上述两种算法中出现的模糊现 象,改善了图像的视觉效果。