基于混合加密的 移动存储文件安全系统设计与实现

针对移动存储设备数据传递过程中面临的信息泄露、内容篡改等安全问题,基于多层嵌套混合加密思想,将ECC公钥密码体制与AES高级加密标准相结合,建立操作系统服务层的移动存储文件安全体系。设计了ECC密钥对存储格式、公钥离线交换机制和基于ECDH的密钥交换协议。将密钥信息以密文身份认证文件的形式随加密文件同时存储至移动存储设备中;解密时,以身份认证文件和ECC算法为依据进行解密。设计了二层混合加密和三层混合加密2种模式,使系统在安全和便捷方面具有一定的弹性,并在提高文件分发灵活度的同时,保证了系统的安全性。经严格测试,系统具有理想的安全性,能够实现对移动存储设备文件的有效防护。     

公开密钥数字签名算法研究

数字签名是密码学中的重要课题之一,它是传统文件手写签名的模拟,能够实现用户对以电子形式存放的消息的验证。当算法公开时,在计算上不可能由加密密钥求解解密密钥,因而可以将加密密钥公开,即公开密钥。RSA密码是一种利用因数分解的困难性而设计的公钥密码。本文详述了RSA加密算法的实现以及如何利用RSA实现数字签名。     

B样条快速求值算法及其在曲线拟合中的应用

B样条方法是数据插值、拟合与平滑的重要方法。通过对de Boor-Cox算法计算路径的分析,给出了一种基于向量扩展的B样条基函数求值运算方法。分析表明,该方法具有并行计算结构和递推计算结构,利于计算机实现。同de Boor-Cox递推运算方法比,它能够同步计算出k次B样条的所有k＋1个非零函数值,运算效率提高了2k＋1倍。在最小二乘法B样条曲线拟合算法中的应用验证了本文算法的有效性。     

基于C语言实现的数据加DES算法

DES体制是ISO颁布的数据加密标准，通过对DES算法的分析，提出了用C语言实现数据加密标准DES算法，通过循环或迭代，将简单的基本运算(例如左移、右移、模2加法等)和变换(选择函数、置换函数)构造成数据流的非线性变换(加密变换或解密变换)，从而实现对计算机数据进行密码保护。着重介绍了用C语言实现其加密的主要过程。     

基于DES和RSA的网络数据安全系统

利用密码学的方法来解决网络数据传输的安全性问题。分析了密码学的2种密码算法———传统密码体制的代表DES算法和公开密钥密码体制的代表RSA算法各自的优缺点,提出了一种利用2种密码算法的优点而避免其缺陷的数据加密方式———DES和RSA的混合加密以保证网络数据安全。     

基于粒子群算法-最小二乘支持向量机算法的磁化曲线拟合

磁化曲线是强非线性函数,提高磁化曲线的拟合精度对含有铁磁材料的电气设备建模准确性至关重要。提出了一种基于粒子群算法-最小二乘支持向量机(PSOLSSVM)算法的磁化曲线拟合方法。该方法用粒子群优化算法解决了最小二乘支持向量机(LSSVM)参数的选择问题。仿真结果显示PSOLSSVM算法能获得最优的LSSVM参数,且采用PSOLSSVM算法拟合的磁化曲线与实际测量的磁化曲线基本无偏差,拟合精度较高。     

密切弯曲激波乘波体技术的应用及有效性分析

将密切技术设计乘波体的应用推广至弯曲激波外锥流场中。针对不同激波形状(ICC)约束条件,在凸、凹激波曲外锥流场中,生成了四种构型的密切弯曲激波乘波体,采用数值模拟及理论分析的手段开展了密切弯曲激波乘波体技术应用的可行性验证及有效性分析。研究结果表明:(1)基于密切凸、凹激波外锥流场的乘波体乘波压缩面、出口截面压力分布、激波形状(ICC曲线)均与设计吻合,最大偏差小于5%,说明密切凸、凹激波外锥流场的方法设计乘波体是可适用的;(2)利用宽高比为0.5的超椭圆方程作为ICC控制曲线生成的乘波体,流场压力的理论解与数值解偏差可控制在1.6%以内,而宽高比为2的超椭圆方程作为ICC控制曲线生成的乘波体,流场压力偏差可控制在4%以内。     

多天线结构对无人机MIMO信道容量的影响

为进一步提高无人机(UAV)遥测链路传输容量,分析了无人机多输入多输出(UAV-MIMO)天线间隔、阵列等结构参数对信道容量的影响。建立了无人机MIMO基于散射体三维几何分布的单跳同心椭圆(GBSBCERS)信道模型,结合机载MIMO天线布局方案,给出了无人机MIMO信道矩阵及遍历容量的表达式。数值仿真表明,受无人机远距离通信的影响,信道容量与天线间隔呈正比变化;且相比于线阵布局,圆阵布局虽然受到无人机姿态变化的影响,但仍具有较高的信道容量。通过分析多天线结构对无人机MIMO信道容量的影响,对设计实现高速无人机数据链具有实际意义。     

分散信息滤波及其在组合导航中的应用

 本文提出一种分级式分敞信息滤波算法,并通过具体实例研究了它在组合导航系统中的应用。这种算法的特点是主滤波器的计算量小,量测修正只涉及到加(减)法运算。尤其适合于数据采样频率较高、反馈校正间隔较长的组合导航系统。     

干涉型光纤传感器相位解调技术研究

针对干涉型光纤传感器的信号解调方法,为消除光强波动对解调结果的影响,在相位生成载波(PGC)调制解调技术的基础上,提出了一种改进型微分交叉相乘(DCM)算法。改进型DCM算法利用滤波后的两路信号进行平方相加运算,再与传统算法中的差分结果进行相除运算来实现对待测信号的解调,可消除传统算法的解调结果中与光强有关的项,从而消除光强波动的影响。仿真分析结果验证了改进型DCM算法的可行性。实验结果表明改进型DCM算法较传统算法可显著提升解调信号的抗光强波动能力。     

Interval Kalman filtering

The classical Kalman filtering technique is extended to interval linear systems with the same statistical assumptions on noise, for which the classical technique is no longer applicable. Necessary interval analysis, particularly the notion of interval expectation, is reviewed and introduced. The interval Kalman filter (IKF) is then derived, which has the same structure as the classical algorithm, using no additional analysis or computation from such as H/sup /spl infin//-mathematics. A suboptimal IKF is suggested next, for the purpose of real-time implementation. Finally, computer simulations are shown to compare the new interval Kalman filtering algorithm with the classical Kalman filtering scheme and some other existing robust Kalman filtering methods.     

Efficient robust AMF using the FRACTA algorithm

The FRACTA algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists nonhomogeneous clutter, jamming, and dense target clusters. Further developments of the FRACTA algorithm are presented here in which the focus is on the robust, efficient implementation of the FRACTA algorithm. Enhancements to the FRACTA algorithm include a censoring stopping mechanism, an alternative data blocking approach for adaptive power residue (APR) censoring, and a fast reiterative censoring (RC) procedure. Furthermore, a coherent processing interval (CPI) segmentation scheme for computing the adaptive weights is presented as an alternative approach to computing the adaptive matched filter (AMF) weight vector that allows for lower sample support and reduced computational complexity. The enhanced FRACTA algorithm, denoted as FRACTA.E, is applied to the KASSPER I challenge datacube which possesses dense ground target clusters that are known to have a significant deleterious effect on standard adaptive matched filtering (AMF) processors. It is shown that the FRACTA.E algorithm outperforms and is considerably more computationally efficient than both the original FRACTA algorithm and the standard sliding window processing (SWP) approach. Furthermore, using the KASSPER I datacube, the FRACTA.E algorithm is shown to have the same detection performance as the clairvoyant algorithm where the exact range-dependent clutter covariance matrices are known.     

Partially Adaptive STAP using the FRACTA Algorithm

A partially adaptive space-time adaptive processor (STAP) utilizing the recently developed FRACTA algorithm is presented which significantly reduces the high computational complexity and large sample support requirements of fully adaptive STAP. Multi-window post-Doppler dimensionality reduction techniques are employed to transform the data prior to application of the FRACTA algorithm. The FRACTA algorithm is a reiterative censoring (RC) and detection algorithm which has been shown to provide excellent detection performance in nonhomogeneous interference environments. Two multi-window post-Doppler dimensionality reduction techniques are considered: PRI-staggered and adjacent-bin. The partially adaptive FRACTA algorithm is applied to the KASSPER I (Knowledge-Aided Sensor Signal Processing & Expert Reasoning) challenge datacube. The pulse repetition interval (PRI)-staggered approach with D=6 filters per Doppler bin is found to provide the best detection performance, outperforming the fully adaptive case while simultaneously reducing the runtime by a factor of ten. Using this implementation, partially adaptive FRACTA detects 197 out of 268 targets with one false alarm. The clairvoyant processor (the covariance matrix for each range cell is known) detects 198 targets with one false alarm. In addition, the partially adaptive FRACTA algorithm is shown to be resilient to jamming, and performs well for reduced sample support situations. When compared with partially adaptive STAP using traditional sliding window processing (SWP), the runtime of partially adaptive FRACTA is 14 times faster, and the detection performance is significantly increased (SWP detects 46 out of 268 targets with one false alarm).     

基于Census变换和改进自适应窗口的立体匹配算法

 针对现有立体匹配算法难以在幅度失真图像中获取高匹配精度的问题,提出了一种基于Census变换和改进自适应窗口的立体匹配算法。首先根据图像结构和色彩信息获得基于十字骨架的任意形状和大小的Census变换窗口;其次利用Census变换的Hamming距作为匹配代价,使用两次累加降低计算复杂度,采用局部优化得到初始视差;最后提出一种基于均值偏移的视差提精方法,有效地处理了不可信视差区域,获得高精度的视差图。实验表明,通过该算法获得的视差图与当前优秀的局部算法相比精度相当,特别是能很好地处理现有算法难以解决的幅度失真问题,适用于无人机视觉导航的应用。     

Generalized input-estimation technique for tracking maneuveringtargets

A new input estimation technique for target tracking problem is proposed. Conventional input estimation techniques assume that the target maneuver level is constant within the detection window, which has been the major drawback of the techniques. The proposed technique is developed to overcome this drawback by modeling the target maneuver as a linear combination of some basic time functions. The resulting algorithm has a generalized formulation including earlier works on input estimation. A detection performance of the proposed algorithm is analyzed by investigating the detection sensitivity according to the selection of maneuver models and other design parameters such as the detection window size, measurement noise level, and sampling step size. A computer simulation study shows that the estimation performance of the proposed algorithm is comparable to Bogler's input estimation method while the computation time is greatly reduced     

噬菌体基因文库控制元件测定的并行处理方法

在噬菌体基因文库的研究中,理解基因的表达机制占有重要的地位,其中最为重要的一个环节是控制元件的测定,目前大部分控制元件测定方法都是基于基因结构的概率模型。介绍了一种不依赖于任何实验数据和先验数据的噬菌体基因文库控制元件测定方法,采用字符串模式匹配算法扫描噬菌体基因序列,发现并提取出重复频度高的子序列（可能的控制元件）,但该方法计算复杂度为O（N^2）。为了提高对基因序列的控制元件测定效率,进一步提出了该算法在局域网环境下的并行处理解决方案,实验表明这种基于数据分割的并行处理方法极大的加快了序列处理速度,进而扩大了算法的应用范围。     

网格计算在工业过程控制中的应用前景

网格计算是一种新兴的解决大规模复杂问题的应用技术,充分利用现有资源提供高性能的计算能力。现在的生产过程越来越向大规模复杂化方向发展,过程控制需要新的控制模型和控制方法来解决这些问题。但现有的计算能力无疑又限制了控制技术的应用,将高性能的网格计算运用到过程控制当中可以提高控制模型和控制算法的计算速度,也将大大提高其控制品质和应用范围。     

Performance evaluation of digital beamforming strategies for satellite communications

Smart antennas are becoming one of the promising technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. A main component in a smart antenna system is beamforming. Because of the limitations of analog beamforming, digital beamforming will be employed in future satellite communication systems. We evaluate the performance of various digital beamforming strategies proposed in the literature for satellite communications: 1) single fixed beam/single user, 2) single fixed beam/multiple users, 3) single adaptive beam/single user, and 4) single Chebyshev dynamic beam/multiple users. Multiple criteria including coverage, system capacity, signal-to-interference-plus-noise ratio (SINR), and computation complexity are used to evaluate these satellite communication beamforming strategies. In particular, a Ka-band satellite communication system is used to address the various issues of these beamforming strategies. For the adaptive beamforming approach, subarray structure is used to obtain the weights of a large 2D antenna array, and a globally convergent recurrent neural network (RNN) is proposed to realize the adaptive beamforming algorithm in parallel. The new subarray-based neural beamforming algorithm can reduce the computation complexity greatly, and is more effective than the conventional least mean square (LMS) beamforming approach. It is shown that the single adaptive beam/single user approach has the highest system capacity.     

A parallel square-root algorithm for modified extended Kalmanfilter

A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n²) processing elements and uses O ((s+17)n) time-steps for a complete iteration at each instant, in contrast to the complexity of O((s+6) n³) time-steps for a sequential implementation, where s≈log n     

基于时频表示的跳频信号分析

针对跳频信号的非平稳性,提出了基于时频表示的跳频信号分析方法。该方法能够有效地描绘出跳频信号的频率随时间跳变的规律 (跳频图案 ),具有很高的时间 -频率分辨率,同时能够有效抑制或减小交叉项的干扰,计算时可采用递归算法,运算量较小,因此能够满足跳频系统的快速分析、跟踪、测量等要求。计算机仿真验证了应用时频表示法分析跳频信号的有效性和实用性。