一种机械振动信号的结构化随机测量矩阵构造方法

针对压缩采集在机械振动信号采集的过程中,现有随机测量矩阵不易硬件实现、确定性测量矩阵重构误差较大的问题。将高斯序列的优点和循环原理的优点相结合,提出一种高斯分布循环测量矩阵,其是一种结构化随机测量矩阵。高斯分布循环测量矩阵的第一行元素由服从高斯分布的序列生成,通过循环移位生成剩余的所有行向量;随机取出除第一行的其他所有行的部分元素,每个元素再乘不同的随机数或者同一个随机数,并放回原位置;基于高斯分布循环测量矩阵得到的机械振动信号压缩测量值采用正交匹配追踪算法对原始振动信号进行重构。高斯分布循环测量矩阵的所有元素的随机性可以满足测量矩阵对随机性的要求,循环原理的内在确定性又可满足测量矩阵硬件实现的要求。仿真表明:高斯分布循环测量矩阵的感知性能略优于与高斯矩阵的性能,整体上基本相当。     

基于压缩感知的多中继协作通信系统稀疏信道估计算法研究

提出了一种基于压缩感知理论的多中继协作通信系统稀疏信道估计方法.采用正交匹配追踪（Orthogonal Matching Pursuit,OMP）压缩感知算法,对时域信道脉冲响应进行估计.对多中继协作通信系统进行稀疏建模;结合压缩感知理论构建观测矩阵,并给出卷积信道的稀疏表示;利用压缩信道感知算法重建了系统的卷积复合信道.仿真结果表明,与传统的最小二乘法（Least Square,LS）相比,采用压缩感知理论的信道估计算法,能利用较少的导频信号获得很好的信道估计性能,提高了频谱利用率.     

基于最优型确定性测量矩阵的振动信号数据压缩采集方法

针对目前机械振动信号频带越来越宽,依据奈奎斯特采样定律进行数据采集时,会得到海量的振动数据,对存储、传输和处理带来困难的问题,结合压缩感知理论,提出了一种基于最优型确定性测量矩阵的振动信号数据压缩采集方法。该方法的核心内容为测量矩阵的设计:以托普利兹(Toeplitz)矩阵为基础,建立正交对称托普利兹(Orthogonal Symmetric Toeplitz, OST)测量矩阵模型;为了降低测量矩阵与稀疏基之间的互相干性,应用阈值迭代收缩算法对该矩阵进行迭代,得到改进的OST矩阵;为了提高OST矩阵自身列独立性,采用奇异值分解(Singular Value Decomposition, SVD)算法继续优化OST矩阵,从而得到最优型确定性矩阵。仿真实验结果显示,提出的最优型测量矩阵与Toeplitz矩阵、高斯矩阵及优化前的OST矩阵相比,在重建振动信号时,重建精度较高,并且易于工程实现。     

基于改进频域压缩感知的轴承复合故障欠定盲提取

针对旋转机械复合故障频域盲提取算法的不足,为提高欠定盲提取分离结果精度,提出基于多尺寸多结构元素的闭-开组合形态滤波、遗传模拟退火聚类及频域压缩感知重构算法相结合的(C-OACMF-GASA-CS)故障特征欠定盲提取方法。利用形态滤波滤除背景噪声及提取冲击信号;利用遗传模拟退火算法估计混合矩阵;用估计矩阵重构传感矩阵,并用正交匹配追踪基频域压缩感知重构分离信号。实验仿真及双通道滚动轴承故障加速度振动信号分析结果表明,该方法能有效分离提取滚动轴承故障特征。     

基于深度压缩感知的语音增强模型

随着压缩感知的深入研究,压缩感知在语音增强方面的应用也备受关注。针对传统压缩感知语音增强算法中存在的不足,将压缩感知与深度学习结合构建名为基于深度压缩感知的语音增强模型(Speech Enhancement based on Deep Compressed Sensing, SEDCS)。基于压缩感知原理使用编解码模型代替压缩感知中语音信号稀疏过程,使用卷积神经网络代替测量矩阵实现语音信号观测降维过程,通过联合训练的方式实现语音增强。实验结果表明：该模型能够完成语音增强任务,并且与现有的压缩感知语音增强算法相比,该模型能取得较好的语音增强效果;相比利用深度学习的语音增强算法,该模型虽性能一般,但在模型泛化性能和测试阶段的增强时间效率上有一定提升。     

离散余弦小波包变换及语音信号压缩感知

针对语音信号压缩感知问题,在研究语音离散余弦变换(Discrete Cosine Transform,DCT)系数和小波包变换(Wavelet Packet Transform,WPT)特性的基础上构造了离散余弦小波包变换(Discrete Cosine Wavelet Packet Transform,DCWPT)。DCWPT首先获取语音信号的DCT域系数,结合语音频谱特性选取部分DCT系数进行WPT变换,从而得到比DCT系数更加稀疏的DCWPT系数。为将此变换直接用于压缩感知,构造了DCWPT的正交稀疏分解矩阵并分析了其稀疏表示性能。结合稀疏表示基优化了正交匹配追踪重构算法,提出了基于DCWPT的语音信号压缩感知框架。通过压缩重构对照实验,采用主客观评价指标,得出该方法优于传统基于DCT的语音压缩感知方法的结论。     

基于压缩感知的强鲁棒性彩色图像双水印算法

目的为了解决图像的鲁棒性和透明性之间的矛盾,依据压缩感知理论的计算保密性提出一种基于压缩感知的强鲁棒彩色图像双水印算法。方法首先将水印图像RGB分解后,对G,B分量分块压缩感知获得测量值,再对载体图像G,B分量NSCT分解,对低频分量非重叠分块后LU分解、奇异值分解,将每个分块的水印测量值按不同嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。最后用含水印图像R分量分块压缩感知测量值生成零水印,发往IPR中心注册保存。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类攻击。结论算法具有很强的鲁棒性和较好的透明性,实现较简单具有切实的可行性。     

广义轮换测量矩阵及其在水下回波信号压缩感知中的应用

根据压缩感知中测量矩阵的性质及要求，在轮换确定性测量矩阵的基础上，通过调整测量矩阵每一列元素的系数，增强列与列之间的相关性，得到广义轮换测量矩阵，并将其应用于水下回波信号的压缩感知观测中。通过无噪声下不同测量矩阵匹配度和相对误差随压缩比的变化，以及4、0、-3 dB三种信输入噪比下不同测量矩阵的输出信噪比、匹配度随压缩比的变化，分别对水下回波信号进行处理，比较其性能。仿真结果表明，相比部分哈达玛等确定性测量矩阵和以高斯为代表的随机测量矩阵，广义轮换测量矩阵在输出信噪比、匹配度、相对误差等方面有很大提高。同时广义轮换矩阵为确定性测量矩阵，便于工程实现。     

基于NSCT和压缩感知的数字图像水印算法

目的依据压缩感知理论具有很好的计算保密性,提出一种基于NSCT和压缩感知的数字图像水印算法,以解决图像的鲁棒性、不可感知性及保密性之间矛盾。方法首先将水印图像分块压缩感知获得测量值,然后再将载体图像NSCT分解,对低频分量Fibonacci置乱后非重叠分块,对每块进行LU分解、奇异值分解,将每个分块的水印测量值按不同的嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类的攻击。结论该算法既具有很好的鲁棒性又兼有较强的不可见性,具有切实的可行性。     

气体泄漏声源的时域压缩波达方向估计研究

针对现有压缩感知算法中对模拟信号压缩采样的不足以及传统测量矩阵存储量和计算量大等问题,提出一种基于模拟信号时域压缩的波达方向(DOA, Direction Of Arrival)估计算法,并应用于气体泄漏声源方位估计研究。首先建立气体泄漏声源DOA估计模型,然后设计一种直接非均匀随机欠采样方案,并构造相对应的形式简单的等效测量矩阵,实现对模拟声源信号的直接压缩采样,避免了传统测量矩阵数据量大及计算复杂度高等问题,最后采用子空间追踪算法进行重构,实现了较快的计算速度和较高的重构精度。理论分析和实验表明,该算法能成功实现气体泄漏声源的DOA估计,且在显著降低信号采样率和计算复杂度的同时,实现了较高的估计精度和分辨率,估计性能更佳。     

改进的Mean Shift算法在前视声呐运动目标跟踪中的应用

多波束前视声呐具有成像速度快、分辨率高的优点,是进行水下目标探测、跟踪和监控的重要设备。针对多波束前视声呐运动目标的跟踪问题,提出了一种改进的MeanShift算法。该算法利用经典的MeanShift算法实现目标的帧间定位,通过基于图像序列的背景消减法实现运动目标分割,根据分割后目标的位置和大小对Mean Shift跟踪框进行更新,并重新建立跟踪模型来迭代实现目标的准确定位和跟踪。实验结果表明,改进后的算法可实现目标跟踪框随目标大小和形状的更新,对目标的定位更加准确。因此,该算法具有应用于水下目标精确跟踪和定位的潜力。     

A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a cuckoo search (CS)-based memetic algorithm, called HCS, is proposed for the PFSSP. To make CS suitable for the PFSSP, a largest-ranked-value (LRV)-rule-based random key is used to convert the continuous position in CS into a discrete job permutation. The Nawaz-Enscore-Ham (NEH) heuristic is then combined with the random initialisation to initialise the population with a certain quality and diversity. After that, CS is employed to evolve nest vectors for exploration, and a fast local search is embedded to enhance the local exploitation ability. In addition, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is both effective and efficient.     

未知声速分布下的一种水下目标GPS定位修正算法

由于海洋中声速分布不均匀,要实现水下目标GPS的精确定位,必须进行误差修正。阐述了采用应答器工作模式的水下目标GPS系统定位原理,在水下目标GPS系统定位过程中,分析了一种基于欧氏几何原理的修正算法在声速分布未知时的定位修正效果和局限性,研究了算法出现盲区的原因,并据此对该算法进行了改进。仿真计算结果表明改进后的算法可有效减小声速分布未知时声速估计不准确造成的定位误差,提高了水下目标GPS的定位精度。     

Object localization in the presence of a strong heterogeneous background in fluorescent tomography

We propose a method for object localization in fluorescent tomography (FT) in the presence of a highly heterogeneous background. Existing approaches typically assume a homogeneous background distribution; thus, they are incapable of accurately accounting for the more general case of an unconstrained, possibly heterogeneous, background. The proposed method iteratively solves the inverse problem over a solution space partitioned into a background subspace and an object subspace to simultaneously estimate the background and localize the target fluorescent objects. Simulation results of this algorithm applied to continuous-wave FT demonstrate effective localization of target objects in the presence of highly heterogeneous background distributions.     

Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

Precise localization techniques for indoor Wi-Fi access points (APs) have important application in the security inspection. However, due to the interference of environment factors such as multipath propagation and NLOS (Non-Line-of-Sight), the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS (Received Signal Strength) is difficult to accurately measure. Therefore, the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper. The algorithm hierarchically divide the room where the target Wi-Fi AP is located, on the region division line, a modified signal collection device is used to measure RSS in two directions of each reference point. All RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate region. The location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy threshold. There are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable APs. The experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters, and the minimum localization error is 0.16 meters, which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.     

高速高精度的机器视觉定位的算法

针对IC装备的视觉系统在复杂条件下很难快速、精确定位这一问题,提出了基于小波变换和径向基函数（RBF）网络算法,此算法提取图像归一化小波系数作为特征矢量,对目标图像特征进行学习,得到RBF分类器,利用它在背景图像上进行目标定位。仿真实验表明,此算法可以实现在旋转、平移、尺度变化和噪声条件下的快速定位,定位精度达到亚像素级。     

基于概率图模型的单信标定位算法研究

随着人类对海洋认识的逐步深入，水下目标的位置信息成为了一种不可或缺的水下信息源，传统的水下定位方法已经不能满足不同场景的应用需求。无论是在测量信息多元化时的多元信息融合定位，还是测量信息不足时的时域信息联合定位时均存在局限性。为此文章提出了基于概率图模型的单信标定位算法，在信标稀疏或者信标通信信息缺失的情况下，通过联合目标时域上其他位置时刻的所有量测信息，从而实现目标定位。此外文章还分析了模型分辨率和测距误差对定位结果的影响，并通过湖试验证概率图方法的有效性，证明其能够在传统定位方法无法使用的单信标的定位跟踪过程中，估计出每一时刻目标的位置，描述目标的运动轨迹。平均定位精度达到1.203 5 m。     

Leveraging Logical Anchor into Topology Optimization for Indoor Wireless Fingerprinting

The indoor subarea localization has wide application space in dynamic hot zone identification, indoor layout optimization, store dynamic pricing and crowd flow trend prediction. The ubiquitous mobile devices provide the opportunity for wireless fingerprinting-based indoor localization services. However, there are two short board where the existing methods have been criticized. One is that a tagging approach requires a large number of professional surveys for wireless fingerprint construction, which weakens the scalability of the methods. The other is that the crowdsourcing-based methods encounter the cold boot problem in the system initial stage. To address these issues, the paper proposes a topology optimization approach leveraging the dynamic logical anchor selection into a subarea localization system. First of all, a new annular-based radio map construction strategy with the feedback selection of logic anchor is designed to release the pressure of site survey. The implementation of this strategy harnesses the characteristics of the indoor building structure and inter subarea overlapping recognition, without the topology and distribution of physical anchor (e.g., access points or POIs). Secondly, exploiting the probabilistic support vector machine algorithm, the target is localized in the corresponding subarea in a real-time pattern. Furthermore, the localization error is calibrated with an error recognition algorithm. Finally, massive experiments are implemented on a prototype system. The results show that the proposed method can decrease the overhead of the system initialization and achieve higher localization accuracy compared with the existing approaches.     

考虑疲劳效应的伤员应急手术调度模型及算法

为了解决城市突发事件应急救援中批量应急手术的调度问题,并考虑医生长时间连续手术对手术持续时间和挽救病人生命的成功率带来的恶化效应,提出了三阶段批量手术调度模型,将应急手术调度看作是存在并行机的流水车间调度问题。利用改进的飞蛾扑火算法对应急手术模型进行求解,并通过实证来测试模型和算法的有效性。为了验证算法的性能,将经典飞蛾扑火算法、粒子群算法和布谷鸟算法与其对比,取20次运行结果,得知最大手术完成时间均值中改进的飞蛾扑火算法为最小,调度模型给出的调度方案中,3个救治阶段在时间维度上保持连贯。仿真结果表明,改进的飞蛾扑火算法能很好地求解批量手术调度模型,获得较好的调度结果。     

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance.