A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.

RUAP:Random Rearrangement Block Matrix-Based Ultra-Lightweight RFID Authentication Protocol for End-Edge-Cloud Collaborative Environment

Cloud computing provides powerful processing capabilities for large-scale intelligent Internet of things(IoT)terminals.However,the massive realtime data processing requirements challenge the existing cloud computing model.The edge server is closer to the data source.The end-edge-cloud collaboration offloads the cloud computing tasks to the edge environment,which solves the shortcomings of the cloud in resource storage,computing performance,and energy consumption.IoT terminals and sensors have caused security and privacy challenges due to resource constraints and exponential growth.As the key technology of IoT,Radio-Frequency Identification(RFID)authentication protocol tremendously strengthens privacy protection and improves IoT security.However,it inevitably increases system overhead while improving security,which is a major blow to low-cost RFID tags.The existing RFID authentication protocols are difficult to balance overhead and security.This paper designs an ultra-lightweight encryption function and proposes an RFID authentication scheme based on this function for the end-edge-cloud collaborative environment.The BAN logic proof and protocol verification tools AVISPA formally verify the protocol’s security.We use VIVADO to implement the encryption function and tag’s overhead on the FPGA platform.Performance evaluation indicates that the proposed protocol balances low computing costs and high-security requirements.

Expression for the Group Inverse of the 2×2 Block Matrix

In this paper, we present a method how to get the expression for the group inverse of 2×2 block matrix and get the explicit expressions of the block matrix (A C B D) under some conditions.

AN IMPROVED FAST BLIND DECONVOLUTION ALGORITHM BASED ON DECORRELATION AND BLOCK MATRIX

In order to alleviate the shortcomings of most blind deconvolution algorithms,this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix.Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms,it has a constraint that the number of the source signals must be less than that of the channels.The improved algorithm deletes this constraint by using decorrelation technique.Besides,the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix.Simulation results demonstrate the validation and fast separation of the improved algorithm.

Carbon matrix/SiNWs heterogeneous block as improved reversible anodes material for lithium ion batteries

A novel carbon matrix/silicon nanowires(SiNWs) heterogeneous block was successfully produced by dispersing SiNWs into templated carbon matrix via a modified evaporation induced self-assembly method. The heterogeneous block was determined by X-ray diffraction, Raman spectra and scanning electron microscopy. As an anode material for lithium batteries, the block was investigated by cyclic voltammograms(CV), charge/discharge tests, galvanostatic cycling performance and A. C. impedance spectroscopy. We show that the SiNWs disperse into the framework, and are nicely wrapped by the carbon matrix. The heterogeneous block exhibits superior electrochemical reversibility with a high specific capacity of 529.3 mAh/g in comparison with bare SiNWs anode with merely about 52.6 mAh/g capacity retention. The block presents excellent cycle stability and capacity retention which can be attributed to the improvement of conductivity by the existence of carbon matrix and the enhancement of ability to relieve the large volume expansion of SiNWs during the lithium insertion/extraction cycle. The results indicate that the as-prepared carbon matrix/SiNWs heterogeneous block can be an attractive and potential anode material for lithium-ion battery applications.

ON OPTIMAL BACKWARD PERTURBATION ANALYSIS FOR THE LINEAR SYSTEM WITH BLOCK CYCLIC COEFFICIENT MATRIX

Based on the block style spectral decomposition,this paper deals with the optimal backward perturbation analysis for the linear system with block cyclic coefficient matrix.

Strictly non-blocking 4×4 silicon electro–optic switch matrix

The first path-independent insertion-loss（PILOSS） strictly non-blocking 4×4 silicon electro–optic switch matrix is reported. The footprint of this switch matrix is only 4.6 mm×1.0 mm. Using single-arm modulation, the crosstalk measured in this test is-13 dB --27 dB. And a maximum crosstalk deterioration of 6d B caused by two-path interference is also found.

A Novel Approach to Practical Matrix Converter Motor Drive System With Reverse Blocking IGBT

a Pole voltage waveforms (VA20 and VA40) for modulation index 0.4 (middle trace is A-phase voltage waveform) x-axis: 1 div.=10ms, y-axis: 1 div.= 100V b Normalized harmonic spectrum for pole voltage of Fig. 9a c A-phase current and phase voltage for modulation index 0.4 (reference space vector is in inner layer)

A Novel Approach to Practical Matrix Converter Motor Drive System With Reverse Blocking IGBT (To continue)

a Pole voltage waveforms (VA20 and VA40) for modulation index 0.4 (middle trace is A-phase voltage waveform) x-axis: 1 div.=10ms, y-axis: 1 div.= 100V b Normalized harmonic spectrum for pole voltage of Fig. 9a c A-phase current and phase voltage for modulation index 0.4 (reference space vector is in inner layer)

分块三角矩阵逆的存在性判定及其计算

研究了分块三角矩阵可逆性的判定及其逆矩阵的表示.首先,利用分块初等矩阵的性质给出了分块上(下)三角矩阵的可逆条件及其逆矩阵的表示.然后,利用置换矩阵的性质进一步给出分块次上(下)三角矩阵的可逆条件及其逆矩阵的表示.最后,通过实例对所提计算方法进行验证.

埃尔米特梁单元的分块对角与高阶质量矩阵

埃尔米特梁单元常用的集中质量矩阵,是由挠度自由度对应的一致质量矩阵元素通过行求和或节点积分构造.然而,数值结果表明该集中质量矩阵在求解包含自由端的梁振动问题时,会出现频率精度掉阶现象.本文首先从保障质量矩阵最优收敛性的数值积分精度出发,分别针对三次和五次梁单元,发展了质量矩阵的梯度增强节点积分方案.利用梯度增强节点积分方案,可以得到具有分块对角形式的单元质量矩阵,而其组装的整体质量矩阵除边界节点外仍然呈现对角形式.对于两种单元,其分块对角质量矩阵分别具有4阶最优精度和6阶次优精度.再者,将标准一致质量矩阵和具有同阶精度的梯度增强节点积分质量矩阵进行优化组合,建立了具有超收敛特性的高阶质量矩阵.最后,通过数值算例系统验证了三次和五次单元的分块对角与高阶质量矩阵的频率计算精度.

一类非静态细分矩阵链乘积的精确计算及应用

利用矩阵分块理论及特征分解技术对一类非静态细分模式的局部细分矩阵结构进行细致研究,得到细分矩阵链乘积的闭形式表达式,揭示该细分矩阵链乘积的代数结构。在此基础上提出一种可精确插值于极限曲线的细分算法,该算法在保持计算效率的同时,可显著提高细分结果的精度和质量。给出的造型实例表明了算法的有效性。

基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计

多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算法要求缺失数据随机分布于不完整的矩阵中,无法适用于整行缺失数据的恢复问题。为此,提出了一种基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计方法。首先,通过奇异值分解(Singular Value Decomposition,SVD)降低虚拟阵列输出矩阵的维度,以减少计算复杂度。然后,对降维数据矩阵建立基于块Hankel矩阵正则化的低秩矩阵填充模型,在该模型中将MIMO雷达降维数据矩阵排列成块Hankel矩阵并施加Schatten-p范数作为正则项。最后,结合交替方向乘子法(Alternate Direction Multiplier Method,ADMM)求解该模型,获得完整的MIMO雷达降维数据矩阵。仿真结果表明,所提方法能够有效恢复降维数据矩阵中的整行数据缺失,具有较高的DOA估计精度和实时性,在阵元故障率低于50.0%时DOA估计精度优于现有方法。

基于自适应矩阵的核联合稀疏表示高光谱图像分类

针对高光谱图像丰富的空间信息和光谱信息未充分利用的问题,提出了基于自适应矩阵的核联合稀疏表示高光谱图像分类的方法。在特征表示阶段,定义了自适应矩阵特征,通过结合自适应邻域块策略与非线性相关熵度量构成的特征来描述原始光谱像素,充分融合了形状可变的空间信息与非线性光谱信息。在分类阶段,考虑自适应矩阵和高光谱图像非线性,采用对数欧式核函数,构建了核联合稀疏表示模型,以获得重构误差。同时利用字典空间信息构建了矩阵相关性,引入平衡参数实现了稀疏重构误差与矩阵相关性的联合分类。在两个数据集上的实验结果表明,该算法充分利用了高光谱图像的空间信息、光谱信息,能够有效提高分类精度。

协方差驱动随机子空间辨识的参数敏感性分析

协方差驱动随机子空间辨识(Covariance-driven Stochastic Subspace Identification,SSI-cov)是近年来发展较为成熟的工作模态分析方法。针对其识别精度和效率对参数设置具有较高敏感性的问题,基于敏感性分析方法,利用奇异熵增量跳跃点明显程度、频率平均识别误差、阻尼比总变异系数、振型平均模态置信因子、运行时间5种评价指标以及稳定图,通过一经典五自由度层模型仿真算例,研究Toeplitz矩阵行块数、采样频率、数据长度对SSI-cov识别结果的影响规律。并给出既能满足精度要求又可控制程序运行时间的参数建议取值范围。最后,通过一缩尺三层框架模型在白噪声激励下实测数据对提出的根据SSI-cov改进参数设置进行验证,结果表明提出的各参数建议取值范围均较为合理。

5G系统中一种大规模MIMO信号检测加速算法

在多输入多输出(Multiple Input Multiple Output,MIMO)系统中,由于MIMO系统配置的大量天线,传统检测算法难以平衡由于算法复杂度高造成的系统性能损失。在保证达到较好性能的前提下,尽可能降低复杂度,提出一种基于分块矩阵和修正因子的MBSOR(Block-Modification Successive Over-Relaxation)检测算法,该算法对滤波矩阵进行分块使对角矩阵获得更多的信道信息,并针对迭代矩阵引入修正因子,以进一步加快收敛速度。通过该算法与传统逐次超松弛(Successive Over-Relaxation,SOR)算法模拟仿真,表明MBSOR算法在减少重复次数的同时,比SOR算法的误码率更逼近最优解。

基于广义柔度矩阵的井架钢结构损伤识别

钻机井架结构在生产过程中会因各种原因而出现不同程度的损伤及破坏,为解决复杂工作环境下石油钻井井架结构健康监测中损伤识别问题,提出了一种基于广义柔度矩阵的井架结构损伤识别方法。相比于柔度矩阵方法,广义柔度矩阵仅需前几阶低阶固有频率及相应的模态振型便可确保计算的精度。在构建损伤控制方程的过程中,采用矩阵分块模式求解方法,与传统方法中需将单元刚度矩阵扩充整体刚度矩阵维数相比较,所提出的求解方法只需针对单元刚度矩阵的行数进行相应扩充,而列数不变,因此从计算量的统计角度分析可大大降低构建损伤识别控制方程的计算量。最后,以实际工程中的K型井架钢结构为例,结果显示对于不同的损伤位置及程度,该方法均具有良好的识别效果,可用于其他大型结构的损伤诊断或健康监测。

广义S-α_(2)型块对角占优矩阵及其谱分析

利用G-函数的性质和不等式的放缩技巧给出了一类块对角占优矩阵新的判定条件,以及对应矩阵的特征值包含域.数值算例说明了新判定条件的有效性和广泛性.

基于改进矩量法的风电机无源干扰快速计算

准确快速计算风电机的散射电场,是解决风电机无源干扰的基本前提。采用传统矩量法求解电大尺寸风电机散射电场时,需要极为庞大的计算资源导致难以求解。为此,考虑风电机工作时叶片的周期对称性以及风电机叶片大曲率、复杂的异形曲面,改进了传统矩量法中求解风电机无源干扰的面元剖分方法,引入基于曲面三角面元的矩量法基函数,并且利用叶片结构的对称性简化阻抗矩阵填充过程,准确快速计算出了风电机表面感应电流产生的散射电场,实现了风电机无源干扰的快速计算。为验证所提方法的正确性及有效性,开展了基于电波暗室风电机缩比模型实验,结果表明,该方法计算的结果与实验测量结果较为一致。以实际金风GW82/1500型风电机为例,将所提方法计算的不同方位角下风电机叶片无源干扰水平与传统矩量法相比,表明该方法与其吻合良好,所提方法矩阵元素计算内存减少了34.64倍,矩阵元素求解效率提高了37.95倍。为后续广域空间下风电场中大规模风电机阵列动态电磁散射理论研究奠定了理论与技术基础。

基于Kaczmarz算法的磁粒子成像快速重建算法研究

目的:为了解决磁粒子断层成像中系统矩阵方法成像时间长、计算复杂的问题,提出一种基于Kaczmarz算法的磁粒子成像快速重建算法。方法:首先,分析经典Kaczmarz算法及其变体算法的收敛速度,并计算在任意矩阵下的迭代次数和计算时间;其次,比较欧氏距离和余弦距离对系统数据的区分能力,并运用基于余弦距离的K-means算法来增强块Kaczmarz算法的运算能力,缩短系统矩阵重建时间并最终实现磁粒子成像快速重建。最后,通过计算机仿真实验验证提出的算法的有效性。结果:提出的算法大幅缩短了重建时间,提高了重建图像的空间分辨力和质量。结论:提出的算法可以实现磁粒子成像的快速重建,并且在处理含有噪声的数据时具备较强的重建能力。