一种改进LS信道估计算法在稀疏多径水声信道中的应用

为了消除水声正交频分复用调制(Orthogonal Frequency Division Multiplexing,OFDM)系统中噪声对稀疏多径信道估计的影响,提出了一种改进的最小二乘(Least-Square,LS)信道估计算法。该方法在传统基于离散傅里叶变换(Discrete Fourier Transform,DFT)插值的信道估计结构上进行了改进,得到了基于阈值探测的DFT插值信道估计方法,该方法将小于阈值的时域信道响应置零,探测最有效信道抽头,有效消除噪声干扰的影响。仿真结果验证了DFT插值在稀疏多径水声信道估计中的实用性;得到了当循环前缀长度与信道长度越接近时信道估计性能(Bit Error Rate,BER)越好的结论;确定了在两种调制方式下算法的阈值系数范围;且该新算法与已有算法所需的信噪比可低约2 d B,解决了用循环前缀长度来近似信道真实长度的实际问题。     

基于压缩感知的OFDM水声通信信道二次估计算法

该文主要研究OFDM水声通信信道二次估计技术,针对传统信道二次估计算法跟踪估计缓慢时变水声信道存在误码遗传的缺点,提出了一种基于压缩感知的信道二次估计算法。该算法结合水声信道的稀疏特性,利用经过编码校验后的大多数可靠信息,二次匹配追踪信道频域响应,作为系统均衡器的反馈输入信息和次时段信道初始信息,有效地抑制了系统的误码遗传。通过Matlab仿真实验和水池试验,验证了基于压缩感知的信道二次估计算法的可靠性和高效性。     

基于最大相关熵准则的水下生物脉冲噪声消除方法

近海生物噪声表现为脉冲噪声,这一类噪声显著降低了水声通信系统的性能。结合水声信道的稀疏特性,提出一种基于最大相关熵准则的级联滤波器算法,该算法实现了脉冲噪声的消除及稀疏信道估计。第一级自适应滤波器完成对噪声的消除工作,第二级滤波器对去噪后的信号进行稀疏信道估计。该方法的优势在于无需知道信号和噪声的相关信息。仿真结果表明,提出的方法比单一的滤波器结构和传统信道估计算法性能更加优越。     

多载波水声信道估计技术及其研究进展

简述了水声信道与正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统之间的必然联系,概述了基于导频符号的信道估计算法、自适应信道估计算法、子空间信道估计算法以及压缩感知稀疏信道估计方法在水声通信中的应用现状,总结了各种算法的特点,比较了各种算法的优缺点,并指出了未来水声信道估计的发展方向。     

基于卡尔曼滤波的时变水声信道估计

为了能同时利用时变水声信道的簇状稀疏特性和时间相关性,构造了一种时变水声信道模型,并基于该模型对传统的卡尔曼滤波压缩感知算法进行改进。该方法主要利用前一时刻估计的信道状态响应来确定当前时刻信道的候选支撑集,并以此构造时变水声信道的状态转移方程。通过卡尔曼滤波迭代的方法计算候选支撑集上的系数,最后通过阈值法滤除误差原子。仿真结果表明：该方法能有效地利用水声信道间的时间相关性来提高信道估计的性能,同时由于水声信道存在簇状稀疏特性,因此经该方法也具有一定的鲁棒性。     

OFDM水声通信系统的LS-OMP信道估计

对于正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)水声通信系统,最小二乘(Least Squares,LS)信道估计方法受噪声影响较大,并且使用的导频数量较多,影响通信效率。而基于压缩感知理论的正交匹配追踪(Orthogonal Matching Pursuit,OMP)信道估计方法可以充分利用水声信道的稀疏特性,同时能够有效地抑制系统噪声,但控制迭代运算次数的相关参数(稀疏度或误差容忍值)是OMP算法的关键条件。针对上述问题,提出了利用少量导频随机分布的LS和OMP联合的信道估计方法,该方法首先利用少量导频采用LS方法估计出OMP算法的误差容忍值,再利用OMP算法恢复数据子载波的信道信息。理论分析和仿真结果同时表明,与传统的LS算法或OMP算法相比,新算法能够在数据恢复的同时有效抑制系统噪声,应用稀疏特性及较少量的导频,进一步提高了系统的频谱效率,对时变稀疏水声信道具有更好的适应性。     

基于gOMP算法的FBMC水声通信信道估计方法

传统基于训练序列及块状导频结构的滤波器组多载波（Filter Bank Multicarrier,FBMC）信道估计方法花费额外的频谱资源,这在频谱资源较为紧张的水声通信环境中具有一定的局限性。针对这一问题并结合水声信道稀疏性的特点,文章提出了一种基于压缩感知的离散导频结构FBMC信道估计方法。首先基于等效导频能量最大化的思想,设计了一种新的离散导频结构来解决FBMC系统信道估计时存在的固有虚部干扰问题;然后配合该结构,提取出导频处的接收信息并利用重构效果优良的压缩感知gOMP算法对水声信道进行重构。该方法在保证水声信道估计精度的同时有效提高了FBMC系统的频谱利用率,改善了水声通信的性能。仿真结果表明,文中所提方法相较于传统方法在估计精度和频谱利用率方面具有一定的优越性。     

基于稀疏约束的DCD滑动窗RLS水声信道估计算法研究

水声信道具有稀疏性的特点,因此高精度低复杂度的稀疏信道估计算法对水声通信具有重要意义。基于自适应滤波算法的信道估计问题本质上是线性回归模型参数的求解问题,传统的最小二乘（Least Square,LS）、最小均方（Least Mean Square,LMS）及递归最小二乘（Recursive Least Squares,RLS）算法在估计稀疏信道时不仅复杂度较高,而且在求解线性回归模型时,因忽略自变量的多重共线性而使稀疏信道估计精度降低。针对上述问题,首先,在经典RLS算法的代价函数中加入信道系数的范数对其进行约束,从而提高了稀疏信道估计的精度,然后,采用滑动窗的方式对其代价函数进行处理以减少算法的计算量。在此基础上又引入二分坐标下降（Dichotomous Coordinate Descent,DCD）算法搜索单次迭代中使代价函数最小的解,进一步降低了算法的复杂度。仿真结果表明,文中所提的算法相较于经典算法在估计精度和复杂度方面具有一定的优越性。     

基于深度神经网络的水声FBMC通信信号检测方法

针对传统水声滤波器组多载波（Filter Bank Multi-Carrier,FBMC）通信接收端需经过信道估计和均衡才可恢复出发送符号,系统复杂度高且信道估计精度不佳等问题。文章将深度神经网络融入到水声多载波通信当中,提出一种基于深度神经网络的水声FBMC信号检测方法。在训练阶段通过大量的数据迭代、调试超参数和优化算法来改善深度神经网络参数,使其具有预期的估计效果。利用训练完成的深度神经网络模型取代传统FBMC通信系统接收端的信道估计、均衡等模块,自适应地学习水声信道状态信息,同时避免了固有的虚部干扰影响。在测试阶段直接将频域序列作为网络的输入来预测发送的二进制序列,仿真结果表明所提出的基于深度神经网络的FBMC信号检测方法相比传统信道估计算法有更好的误码率性能。     

一种信道估计动态更新的逐幸存路径处理算法

水声信道复杂的时变特性会影响水声通信系统的性能,在设计时需要进行特殊考虑。利用逐幸存路径处理算法在无需插入训练序列的情况下即可同时进行信道估计和数据检测的特点,提出了一种信道估计动态更新的算法。该算法通过比较当前时刻各条幸存路径的分支量度来判断信道变化的情况,从而决定信道估计更新的时刻,达到动态更新信道估计的目的。仿真结果表明,所提出的基于信道估计动态更新的逐幸存路径处理算法能有效地跟踪信道变化的情况,在时变信道中使用能获得较好的性能。     

Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model

In this paper, we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. By exploiting the internal block structure, the BSBL method solves the ill‐posed inverse problem more efficiently than other methods that do not consider block structure. Simulation experiments were conducted on a realistic head model obtained by segmentation of MRI images of the head. Two definitions of blocks were considered: Brodmann areas and automated anatomical labeling (AAL). The experiments were performed both with and without the presence of noise. Six different noise levels were considered having SNR values from 5 dB to 30 dB with 5dB increment. The evaluation reveals several potential findings—first, BSBL is more likely to produce better source localization than sparse Bayesian learning (SBL), however, this is true up until a limited number of simultaneously active areas only. Experimental results show that for 71‐channel electrodes setup BSBL outperforms SBL for up to three simultaneously active blocks. From four simultaneously active blocks SBL turns out to be marginally better and the difference between them is statistically insignificant. Second, different anatomical block structures such as Brodmann areas or AAL does not seem to produce any significant difference in EEG source localization relying on BSBL. Third, even when the block partitions are not known exactly BSBL ensures better localization than SBL as soon as block structure persists in the signal. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 46–56, 2017     

Sparse matrix approximation method for an active optical control system

We develop a sparse matrix approximation method to decompose a wave front into a basis set of actuator influence functions for an active optical system consisting of a deformable mirror and a segmented primary mirror. The wave front used is constructed by Zernike polynomials to simulate the output of a phase-retrieval algorithm. Results of a Monte Carlo simulation of the optical control loop are compared with the standard, nonsparse approach in terms of accuracy and precision, as well as computational speed and memory. The sparse matrix approximation method can yield more than a 50-fold increase in the speed and a 20-fold reduction in matrix size and a commensurate decrease in required memory, with less than 10% degradation in solution accuracy. Our method is also shown to be better than when elements are selected for the sparse matrix on a magnitude basis alone. We show that the method developed is a viable alternative to use of the full control matrix in a phase-retrieval-based active optical control system.     

传统陶瓷艺术作品的三维数字化重建及应用的研究与实践

目的 研究获取传统陶瓷艺术作品完整彩色点云数据,并高效进行三维重建的技术方法,探索陶艺作品数字内容虚拟展示平台的构建及推广利用的有效途径.方法 采用拍照式三维扫描设备采集陶艺作品的三维彩色点云数据,研究并提出了融合、修补等后处理的新方法,得到完整的优化彩色点云信息.通过网格化处理获得陶瓷艺术品的高保真三维网格模型和简化网格,利用虚拟现实技术开发数字化展示平台.结果 获取了石湾陶瓷传统艺术作品的三维彩色点云数据,完成了点云优化、修复,生成了高精度的彩色网格模型并进行了数字化的推广与宣传.结论 提出的三维重建技术和虚拟展示方法为我国陶瓷艺术作品的保护、传承与推广作出了积极的探索,为利用现代科技进行文化遗产数字化保护作出了新的尝试.     

A Shared- and distributed-memory parallel general sparse direct solver

An important recent development in the area of solution of general sparse systems of linear equations has been the introduction of new algorithms that allow complete decoupling of symbolic and numerical phases of sparse Gaussian elimination with partial pivoting. This enables efficient solution of a series of sparse systems with the same nonzero pattern but different coefficient values, which is a fairly common situation in practical applications. This paper reports on a shared- and distributed-memory parallel general sparse solver based on these new symbolic and unsymmetric-pattern multifrontal algorithms.     

Sparse electrocardiogram signals recovery based on solving a row echelon‐like form of system

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two‐stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon‐like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.Inspec keywords: electrocardiography, matrix algebra, medical signal processingOther keywords: sparse electrocardiogram signal recovery, row echelon‐like form of system, noise environment, biological data analysis, personalised medicine, dictionary learning algorithm, transformation matrices, sparse biomedical signal recovery     

Optimization of the internal admittance load of an inductivevoltage divider for low ratio error

The distributed capacitances between the strands of an inductive voltage divider degrade the voltage ratio. A calculation scheme to convert the distributed capacitances into equivalent concentrated capacitances at the taps is given. An optimized connection scheme for the strands is calculated to produce concentrated capacitances which are as equal and as small as possible. Capacitors are added at the taps for optimum equalization. The relative error of the output voltage for a 120-V:10-V divider has been reduced to less than 1×10^-7 in phase and less than 1 μrad in quadrature, at a frequency of 50 Hz     

Active metameric security devices using an electrochromic material

In order to increase the anticounterfeiting performance of interference security image structures, we propose to implement an active component using an electrochromic material. This novel device, based on metamerism, offers the possibility of creating various surprising optical effects, it is more challenging to duplicate due to its complexity, and it adds a second level of authentication. By designing optical filters that match the bleached and colored states of the electrochromic device, one can obtain two hidden images-one appearing when the device is tilted, and the other one disappearing when the device is colored under an applied potential. Specifically, we present an example of a filter that is metameric with the colored state of the electrochromic device, demonstrate how the dynamic nature of the device offers more fabrication flexibility, and discuss its performance. We also describe a design methodology for metameric filters based on the luminous efficiency curve of the human eye: this approach results in filters with a lower number of layers and hence lower fabrication costs, and with a lower color difference sensitivity under various illuminants and for nonstandard observers.     

Stochastic modelling of Rice fading channels with temporal, spatial and spectral correlation

A statistical characterisation of a small-scale fading mobile radio channel is presented and a fading simulator for the simulation of multiple input-multiple output multicarrier systems is introduced. The derivation of the equivalent lowpass channel as a function of time, transmit and receive antenna positions and subcarrier frequency, leads to a tapped delay-line model with time, space and frequency-selective taps. The taps are modelled as a sum of a deterministic line-of-sight or dominant scattered path and a zero-mean Gaussian part composed of a number of unresolvable scattered paths, and are therefore Rice fading. The Gaussian parts can have the desired temporal and spatiospectral correlations generated by time-correlation shaping filtering and a space-frequency correlation transformation, respectively. The structure and details of the simulator are presented, and the simulator is shown to achieve good accuracy while retaining a reasonable computational complexity.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.