Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

中厚板多层多道焊视觉测量与工艺规划

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到的特征点进行坐标转换,采用视觉测量获得焊道轮廓和尺寸信息,来修正机器人的运动路径;根据焊缝特征信息分析工艺参数对焊道成型的影响,确定焊道层数、各焊道的工艺参数以及焊枪的偏移量,完成多层多道焊接工艺规划;最后基于搭建的机器人焊接视觉系统在12 mm母材上进行V形坡口多层多道焊接试验。结果表明:该方法下坡口填充良好,焊道尺寸平均测量误差小于0.2 mm,满足多层多道焊接工业应用需求。

基于CGA的MPI程序分支覆盖测试套件生成

针对程序的分支覆盖测试,元启发式搜索技术已经被广泛应用于测试数据生成中。然而,当前的研究成果主要适用于串行程序。因此,为覆盖消息传递接口(Message Passing Interface,MPI)程序的分支,该文研究基于协同进化遗传算法(Co-evolutionary Genetic Algorithm,CGA)的测试套件生成方法(简称为:CGA生成法),该方法具有不受不可行分支影响的优势。首先,基于收集覆盖信息的探针,定义最小归一化分支距离,并以此设计出相应的适应度值函数;然后,使用CGA生成进化个体,并基于设计的适应度值函数,计算这些个体的适应值;最后,基于计算的适应值,选择子种群中代表个体,以构成合作种群。所提CGA生成法应用于7个基准MPI程序,并与其他多种方法进行比较。实验结果表明,CGA生成法的覆盖率通常高于其他搜索算法。

面向关隘地形的分层调度多机器人路径规划

针对关隘地形下多机器人路径规划存在的高度耦合性,引入调度次序优化思想构造了分层的多机器人路径求解与优化算法。首先,在优化层采用遗传算法生成所有机器人的调度次序,并根据适应度对调度次序进行迭代和进化。其次,根据优化层制定的调度次序,在规划层采用改进的层级协作A*(IHCA*)算法进行路径求解,并将求解结果返回上层以对该次序的适应度进行更新。最后,通过上下层相互配合,逐步提升问题求解成功率并降低路径耗时。此外,提出搜索熔断机制,避免原始HCA*算法在关隘地形中陷于反复无效搜索的状态,可进一步提升求解效率。研究结果表明,所提算法在高耦合关隘环境下具有较高的求解成功率,且路径总耗时更少。

天基信息传输高效低复杂度LDPC译码算法研究

军事卫星通信由于需满足信息实时性、传输速率高、通信容量大以及符合星间链路信道的时变特性等要求,通常采用高编码增益、高吞吐量的信道编码方案。低密度奇偶校验(Low-Density Parity-Check,LDPC)码由于具备接近Shannon极限的优异纠错性能和可并行计算的特性成为卫星通信主导信道编码标准之一。目前卫星通信接收机的译码器模块设计仍存在诸如无法实时在线判断迭代停止、系统吞吐量受限、大量判决电路影响核心译码电路的低功耗和实时性等问题。考虑上述问题,以因子图模型为基础,针对空间数据系统咨询委员会(Consultative Committee for Space Data Systems,CCSDS)标准深空通信码型,将校验节点归一化满足概率进化图案与LDPC译码器状态紧密耦合,给出可实时在线判断迭代停止的最优停止准则,实现高性能、低复杂度的停止准则译码算法设计。当优先考虑高吞吐量时,误码率(Bit Error Rate,BER)性能退化0.13 dB,中低信噪比平均迭代次数(Average Number of Iteration,ANI)降低50%以上;当优先考虑纠错性能时,BER性能仅退化0.02 dB,同时大幅降低ANI。该译码算法为高效低复杂度LDPC译码器设计提供有效解决方案。

基于DeFi消息传递机制的跨链交易关联方法

跨链桥作为一种新型区块链基本服务,实现了区块链间的资产和数据流通,极大地促进了区块链去中心化金融(DeFi)的建设。然而在实际应用过程中,由于跨链桥的交易不透明与资产难追踪问题,许多犯罪分子开始将跨链技术作为一种洗钱手段,在链间进行非法资产的转移。因此,跨链交易追踪问题正逐渐成为学术界与相关监管部门关注的焦点。现有方法主要通过规则匹配的启发式算法来实现跨链交易的关联,这种方法虽能有效匹配特定区块链间的交易,但仅面向中心化金融(CeFi)跨链桥服务,且高度依赖于中心化的内部应用程序编程接口(API),难以广泛支持多种跨链桥的交易关联。为解决以上问题,提出一种面向DeFi跨链桥服务的交易关联方法。根据跨链桥消息传递机制获取源链的存款事件日志,并基于专家规则提取关键跨链信息,利用数据爬取技术构造目标链交易搜索空间。在此基础上,通过多种业务规则实现跨链交易的关联。实验结果表明,该方法在Celer cBridge、Multichain和Poly Network这3座跨链桥的真实交易数据集(以太坊为源链,币安智能链为目标链)上准确率分别达到93.6%、95.3%和91.6%,具有较好的关联效果。

面向物理信息系统的分布式攻击图生成算法

物理信息系统包含类型多样的物理设备,现有的攻击图生成技术不适用于物理信息系统.传统的漏洞扫描技术难以检测到物理设备的漏洞,并且随着系统规模的增加,攻击图的计算会出现状态空间爆炸问题.为此,本文提出了一种面向物理信息系统的分布式攻击图生成算法.首先,针对物理设备漏洞识别较难的问题,提出了一种基于属性标记实体的方法扩展实体漏洞信息,并据此对物理信息系统进行攻击建模;其次,针对状态空间爆炸问题,提出了一种分布式攻击图生成算法,并且利用消息传递机制消除图部分的重复遍历,进一步提高了生成效率.实验结果表明,与其他相关技术相比,本文技术具有更高的生成效率.

基于阈值和区间筛选的SCMA多用户检测算法

在5G通信用户信息高吞吐量和大规模连接应用场景下,稀疏多址接入(sparse code multiple access,SCMA)可用于改善5G智慧场景通信质量。为了降低SCMA系统检测端算法复杂度,基于Max-log-MPA算法,提出了一种基于单阈值门限和区间范围的多参数组合设定的消息传递算法。在用户信息值迭代运算前利用门限阈值进行判断,当满足设定条件后方可参与算法的迭代过程,迭代计算过程中再次设定区间参数进行过滤筛选,使得通信场景所需的用户节点概率值参与计算,用户信息得到译码,2部分参数均可一定程度筛选用户信息值,从而达到降低算法复杂度的目的。仿真结果表明,提出的算法收敛速度和算法复杂度明显优于消息传递算法(message passing algorithm,MPA),误比特率(bit error ratio,BER)性能损失也可得到保证,在误码率性能和算法复杂度权衡之间有灵活的参数组合方案,具有很强的适应性。

自适应差分阈值的心电信号R峰检测算法

针对传统差分阈值算法的不足,设计了一种自适应差分阈值算法实现对R峰的准确检测。该算法能够依据R峰高幅值、高斜率的特征自适应更新信号检测阈值,并且根据R-R峰之间的心率间隔特征,设计回溯判定机制提升算法的灵敏度和适用范围。文中选用MIT-BIH心率失常数据库中的心电数据作为信号样本,采用带通滤波和小波变换相结合的方法完成对心电信号的去噪,依据自适应阈值检测实现对心电信号的R峰特征提取。从实验结果来看,自适应后的差分阈值法将心电信号的检测准确率提高到了99.59%,灵敏度达到了99.64%,减少了R峰检测过程中漏检、误检情况的出现,提高了检测的准确性。

融合模型求解与深度学习的可见光通信非线性均衡器

沃尔特拉非线性后均衡器(Volterra Series Nonlinear Post-Equalizer,VS-NPE)可以补偿可见光通信(Visible Light Communication,VLC)的非线性失真和多径效应,但其结构复杂且均衡精度有限。在VS-NPE内核求解基础上,提出一种基于阈值自学习近似消息传递(Learned Threshold Approximate Message Passing,LTAMP)网络的非线性均衡器。修正样本观测矩阵以克服其列高度相关的缺陷;在改进近似消息传递(Approximate Message Passing,AMP)算法迭代的基础上,将算法每一次迭代的计算过程映射为一层特殊的神经网络,经逐层展开后构建出完整的LTAMP均衡器。所提方法融合了模型求解和深度学习的优势,可从样本中学习最佳的AMP参数,以克服其对噪声敏感且输出不稳定的缺陷,进而提升内核求解稳定性与计算精度。仿真结果表明,与稳固阈值AMP算法相比,所提方法在误码率为1×10^(-3)时能取得2 dB的信噪比增益,且对样本噪声具有较强的自适应性,展现出优异的非线性失真补偿能力。

免疫分析仪精密移液控制技术设计

目的为实现免疫分析仪精密自动检测,精密自动移液技术至关重要。本课题主要研究免疫分析仪精密移液控制技术,作为免疫分析仪重要部件,其控制精度与自动化程度直接决定最终检测结果的准确性及检测效率,是系统的关键组成部分。方法本系统通过PID闭环负反馈自动控制技术来实现免疫分析仪精密移液控制,通过单片机伺服电机驱动控制技术、高精度传感器检测及低通滤波技术等方法设计免疫分析仪精密移液控制技术。结果本文系统性的给出了免疫分析仪精密移液控制系统软硬件及控制算法设计,设计并解决免疫分析仪精确移液控制技术设计问题,提高加样系统电机控制精度及加样精度,使得加样精度达到加样量10μL时±5%或200μL时±3%。结论本系统可以很好的解决当前免疫分析仪精密移液控制设计技术问题,从而提高免疫分析仪控制精度、移液加样精度及检测效率。

融合负采样和消息传递的GCN推荐算法

近年来,图卷积神经网络(Graph Convolutional Networks,GCN)在推荐领域有广泛的应用,LightGCN通过对传统GCN的简化,省略特征变换和非线性激活的过程,对GCN的研究提供了新思路。为了解决推荐算法负采样问题和消息传递对GCN收敛的影响,提出了SNGCN模型,改变了直接从数据中采样原始负样本的采样策略,利用正例混合和样本混合两个步骤合成硬负样本;其次,SNGCN利用约束损失逼近多层图卷积的极限。最终该模型在4个公开基准数据集上得出的实验结果表明,其Recall和NDCG两项指标均比对比的推荐算法有所提升。

QIM digital watermarkingbased on LDPC code and messagepassingunder scalingattacks

Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.

Speech Enhancement Based on Approximate Message Passing

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).

Multi-Deme Parallel FGAs-Based Algorithm for Multitarget Tracking

For data association in multisensor and multitarget tracking, a novel parallel algorithm is developed to improve the efficiency and real-time performance of FGAs-based algorithm. One Cluster of Workstation （COW） with Message Passing Interface （MPI） is built. The proposed Multi-Deme Parallel FGA （MDPFGA） is run on the platform. A serial of special MDPFGAs are used to determine the static and the dynamic solutions of generalized m-best S-D assignment problem respectively, as well as target states estimation in track management. Such an assignment-based parallel algorithm is demonstrated on simulated passive sensor track formation and maintenance problem. While illustrating the feasibility of the proposed algorithm in multisensor multitarget tracking, simulation results indicate that the MDPFGAs-based algorithm has greater efficiency and speed than the FGAs-based algorithm.

Embedding and Extracting Digital Watermark Based on DCT Algorithm

The principle of digital watermark is the method of adding digital watermark in the frequency domain. The digital watermark hides the watermark in digital media, such as image, voice, video, etc., so as to realize the functions of copyright protection, and identity recognition. DCT for Discrete Cosine Transform is used to transform the image pixel value and the frequency domain coefficient matrix to realize the embedding and extracting of the blind watermark in the paper. After success, the image is attacked by white noise and Gaussian low-pass filtering. The result shows that the watermark signal embedded based on the DCT algorithm is relatively robust, and can effectively resist some attack methods that use signal distortion to destroy the watermark, and has good robustness and imperceptibility.

Message Passing Based Detection for Orthogonal Time Frequency Space Modulation

The orthogonal time frequency space(OTFS)modulation has emerged as a promis⁃ing modulation scheme for wireless communications in high-mobility scenarios.An efficient detector is of paramount importance to harvesting the time and frequency diversities promised by OTFS.Recently,some message passing based detectors have been developed by exploiting the features of the OTFS channel matrices.In this paper,we provide an overview of some re⁃cent message passing based OTFS detectors,compare their performance,and shed some light on potential research on the design of message passing based OTFS receivers.

引入必经点约束的智能汽车全局路径规划

目前对于智能车全局路径规划的研究多数只针对从起点到终点的情况。针对该问题,本文中融合改进A*和模拟退火算法,设计了一种引入必经点约束的全局路径规划算法。首先,基于A*算法计算关键节点间的最短路径并保存。然后,基于启发式算法中的模拟退火算法对过必经节点的全局路径进行迭代随机优化。接着,基于真实高精度地图对算法的有效性以及时间复杂度进行实验分析。结果表明,设计的算法在求解质量和求解速度方面都有较好的表现。最后,通过实车实验,进一步验证了算法的有效性和适应性。

基于改进蝙蝠算法的混合储能系统容量优化配置

针对光伏发电在微网中不稳定、功率波动较大的问题,利用超级电容和蓄电池的混合储能系统对微电网中功率波动部分进行平抑。根据光伏功率波动率和储能响应特性,提出基于低通滤波器分解的功率分配方法,采用改进的蝙蝠算法对混合储能进行容量优化配置,以混合储能装置全生命周期费用最小为目标,以系统的功率平衡、储能的荷电状态以及负荷缺电率等指标为约束条件,建立一种混合储能系统容量优化配置模型,实现混合储能系统经济最优。算例仿真结果表明,改进的蝙蝠算法不仅优化了蓄电池的工作状态,降低了储能系统的全生命周期费用,而且加快了收敛速度。