构造短码长不规则LDPC码

在给定长度和度数分布对情况下,不规则短码长LDPC码的性能差别很大。本文给出了一种低复杂度的基于树的贪婪搜索算法(GTS),此算法通过调整校验矩阵中环的分布情况,尽量减少短长度环,获得最大的平均最小环,从而构造性能很好的短码长不规则LDPC码。     

全气相化学激光体系的直流放电研究

为了提高化学激光体系(AGIL)中F原子的产率，采用直流放电引发方式，对棒式电极的放电特性进行了研究。在对NF3/He混合气体进行解离时，通过选择不同平衡电阻，得到了3 kW左右的放电注入功率。用光谱分析仪对F原子产率进行了测量。通过拟合计算可得:一个NF3分子能够解离出1.3～1.5个F原子。     

并行处理JPEG算法的优化

本文结合指令级并行32位定点处理器的结构特点,对JPEG算法中DCT、量化及Huffman编码等步骤,提出一些适于并行处理算法和数据结构的优化方法,以有效发挥其高速并行的性能.模拟结果表明本文的方法显著提高了JPEG算法并行实现的效率,适于图像压缩的一些实时应用.     

基于外推的SAR图像分辨率增强算法

提高合成孔径雷达(SAR)图像的分辨率对自动目标识别等具有重要意义.本文通过对图像的二维频谱进行外推,扩展了SAR系统的等效带宽以达到提高图像分辨率的目的.文中给出了实现外推算法的详尽步骤并进行了仿真验证,仿真结果证实了算法的有效性.     

用于自适应天线的稳健的RLS算法

抗干扰通信是电子战的重要组成部分，自适应天线不仅具有很强的抗干扰能力而且可以与其它通信抗干扰技术相兼容，自适应算法是自适应天线的核心。本文把应用数学学科研究的热门方法之一“稳健估计（RE）”应用于RLS算法中，得到稳健的RLS算法（RRLS），理论分析与计算机模拟结果都证明了RRLS算法基本保持了RLS算法的优点，同时在抗突出值干扰方面，优于RLS算法，提高了RLS算法的稳健性。     

基于进化编程的改进GLA算法

文中提出的矢量量化算法以种群为基础,通过变异算子提高种群的平均适应值,使其逃离局部最小点。降低了该算法对初始码书的敏感程度,然后使用选择算子对种群进行有针对性地操作。高斯－马尔可夫序列实验表明,该算法较好地实现了全局最优,并有助于克服对初始码本较为敏感的缺点。     

一种计算卷积码自由距离的快速算法

本文给出了一种计算卷积码自由距离的快速算法,该算法把遗传算法与模拟退火有机结合并根据随机概率来控制算法的运行,避免了优化迟钝与盲目现象,提出了该算法的收敛质量和速度,模拟结果表明：与已有算法相比,本算法具有更好可靠性,有效性和稳定性。     

Antiferromagnetic Potts Models on the Square Lattice: A High-Precision Monte Carlo Study

We study the antiferromagnetic q-state Potts model on the square lattice for q=3 and q=4, using the Wang–Swendsen–Kotecký (WSK) Monte Carlo algorithm and a powerful finite-size-scaling extrapolation method. For q=3 we obtain good control up to correlation length 5000; the data are consistent with ()=Ae ^{2 p} (1+a ₁ e ^– + ...) as , with p1. The staggered susceptibility behaves as _stagg ^5/3. For q=4 the model is disordered (2) even at zero temperature. In appendices we prove a correlation inequality for Potts antiferromagnets on a bipartite lattice, and we prove ergodicity of the WSK algorithm at zero temperature for Potts antiferromagnets on a bipartite lattice.     

A Shifted-Barrier Primal-Dual Algorithm Model for Linearly Constrained Optimization Problems

In this paper we describe a Newton-type algorithm model for solving smooth constrained optimization problems with nonlinear objective function, general linear constraints and bounded variables. The algorithm model is based on the definition of a continuously differentiable exact merit function that follows an exact penalty approach for the box constraints and an exact augmented Lagrangian approach for the general linear constraints. Under very mild assumptions and without requiring the strict complementarity assumption, the algorithm model produces a sequence of pairs converging quadratically to a pair where satisfies the first order necessary conditions and is a KKT multipliers vector associated to the linear constraints. As regards the behaviour of the sequence x ^k alone, it is guaranteed that it converges at least superlinearly. At each iteration, the algorithm requires only the solution of a linear system that can be performed by means of conjugate gradient methods. Numerical experiments and comparison are reported.     

On the Selection of Random Numbers in the EIGamal Algorithm

The EIGamal algorithm, which can be used for both signature and encryption, is of importance in public-key cryptosystems. However, there has arisen an issue that different criteria of selecting a random number are used for the same algorithm. In the aspects of the sufficiency, necessity, security and computational overhead of parameter selection, this paper analyzes these criteria in a comparative manner and points out the insecurities in some textbook cryptographic schemes. Meanwhile, in order to enhance security a novel generalization of the EIGamal signature scheme is made by expanding the range of selecting random numbers at an acceptable cost of additional computation, and its feasibility is demonstrated.