求和生成器的相关性分析

J. Dj. Golic运用线性序列电路逼进的方法来分析具有任意个输入的求和生成器.他猜想可以通过这种方法来获得所有具有最大相关系数的输入和输出线性函数对,但是他未给出证明.利用Walsh变换技术证明了当n是偶数的时候这个猜想成立.另外,还研究了求和生成器的相关系数总和,发现它与带1比特组合器的相关系数总和非常类似.     

多值加法型组合生成器的概率模型

建立多值加法型组合生成器的概率模型,以其输出序列作为随机变量序列。讨论该序列的齐次马氏性、遍历性、严平稳性、数字特征和有关大数性质,得到了输出序列与输入序列之间符合率的表达式。该表达式可以为密码学中多值密钥流钟控生成器的设计和安全性分析提供参考。     

带记忆组合生成器的条件相关性

利用隐马尔可夫模型对带记忆组合生成器概率模型的相关性问题进行了研究,得到快速计算记忆状态条件概率的公式。讨论了上述计算公式在限定条件下的应用。在此基础上对改进的加法生成器进行了条件相关攻击,与其它攻击方法相比,条件相关攻击的计算复杂度和所需密钥流长度达到了折中。     

BBS伪随机发生器的改进与应用

传统的BBS随机数生成器是逐个比特的生成器,因此速度比较慢。在本文,我们对传统的BBS生成器进行了改进,得到每次生成多位比特的高速BBS生成器。基于我们的生成器,我们构造了一个高速的概率加密系统。     

汉语句子联想生成器

在一定的语言环境中汉语词语之间存在着优先组合搭配关系,据此,在本文中利用互信息、数理统计和人脑联想记忆的相关理论,设计了一个汉语句子联想生成器,使用该生成器实现了一个医学影像学诊断报告书的联想生成器。     

1比特加法记忆独立二输入逻辑概率模型分析

在已有的带记忆密码逻辑的研究结果的基础上,给出了1比特加法记忆独立二输入逻辑概率模型,证明了其输出序列是独立均匀同分布的二元随机变量序列;得到了其输出序列单点提供的关于输入的信息量之和等于输出序列提供的关于输入的信息量.     

一种新的自缩控生成器

将钟控生成器与缩减生成器组合在一起构造了一种新的自缩控生成器,它仅由一个三元的线性反馈移位寄存器构成,文中讨论了这种新的自缩控序列的周期,线性复杂度和符号分布等性质。理论分析的结果表明自缩控序列在某些方面要优于另一自缩减序列。     

基于Chebyshev映射的快速伪随机序列发生器

为克服多比特量化算法抗逆向迭代弱的缺点,提出了一种基于Chebyshev映射的中间多比特伪随机序列量化算法。首先对理想条件下Chebyshev映射的统计特性进行分析,在此基础上分析了生成序列的平衡性与自相关、互相关性,证明了该序列为二元Bernoulli序列并且其自相关与互相关函数服从标准正态分布N（0,1／N）。同其他算法相比,该算法在有限精度条件下增强了混沌序列的抗逆向迭代能力,扩展了序列的周期,提高了长序列的自相关与互相关性能。     

椭圆曲线线性同余序列的格攻击

椭圆曲线线性同余序列发生器生成效率高,具有重要的密码学应用价值.基于格中最近向量问题的求解算法对椭圆曲线线性同余序列进行了攻击.证明了如果得到该序列连续三个点x坐标足够多的高位比特,可以在多项式时间内恢复整条序列,并给出了序列对于此类攻击免疫的条件.此结果可以指导实际应用中序列的选取.     

面向图像场景转换的改进型生成对抗网络

设计了新的生成器网络、判决器网络以及新的损失函数,用于图像场景转换.首先,生成器网络采用了带跨层连接结构的深度卷积神经网络,其中,多个跨层连接以实现图像结构信息的共享;而判决器网络采用了多尺度全域卷积网络,多尺度判决器可以区分不同尺寸下的真实和生成图像.同时,对于损失函数,该算法借鉴其他算法提出了 4种损失函数的组合,...     

An algebraic attack on the improved summation generator with 2-bit memory

Recently algebraic attacks on stream ciphers have received much attention. In this paper we apply an algebraic attack to the improved summation generator with 2-bit memory, which was presented by Lee and Moon in order to give the original summation generator correlation immunity. We show that the initial state of the generator can be recovered within O(n^5.6) bit operations from O(n²) regular output bits, where n is the total length of LFSRs. We could recover the initial key bits in practice within 3 minutes on a PC even for the case n=256. Our result is a good example that shows how powerful algebraic attacks are in the analysis of stream ciphers.     

关于一般存储器神经网络的学习收敛性研究

本文在一类称为一般存储器神经网络(General Memory Neural Network(GMNN))的统一框架下来研究学习收敛性。该一般模型类的结构由三部分组成：输入空间量化、存储器地址产生器、查表式某种组合输出。当产生的地址是固定有限的个数以及网络输出是线性求和时，可以证明GMNN能在最小平方误差意义下收敛。CMAC(Cerebellar Model Articulation Controller)、SLLUP(Single—Layer Look up Perceptrons)是该类模型的典型代表。本文的意义在于为构造新的基于局部学习的神经网络模型提供理论指导，最后给出了这种构造的两个例子——SDM(Sparse Distributed Memory)和SLLUP的两个推广模型。     

基于Matlab周期信号的分解与合成

分析三角形式傅里叶级数中周期信号的分解与合成,用Matlab软件对方波信号的分解与合成进行仿真。结果显示方波信号可以分解成直流分量和不同频率余弦分量（或正弦分量）的叠加;满足一定关系的直流信号和有限次谐波分量之和可以近似表示方波信号,对方波信号合成过程中误差和吉布斯现象进行分析。利用Matlab软件仿真可以直观地理解周期信号的分解与合成原理,它对于建立信号频谱的概念以及系统频谱分析都有非常重要作用。     

Theory and design of a digital stochastic computer random number generator

The theory and design of a random number generator suitable for use in a general purpose digital stochastic computer is considered. After a review of possible types of random number generator the paper considers the design of a system for the generation of negatively correlated pseudo-random binary sequences. Specifically it is shown that the use of correlated sequences reduces the adverse effects of pure randomness whilst retaining the property of zero correlation between sequences fundamental to the theory of stochastic computation.     

Cascaded RLS–LMS Prediction in MPEG-4 Lossless Audio Coding

This paper describes the cascaded recursive least square-least mean square (RLS-LMS) prediction, which is part of the recently published MPEG-4 Audio Lossless Coding international standard. The predictor consists of cascaded stages of simple linear predictors, with the prediction error at the output of one stage passed to the next stage as the input signal. A linear combiner adds up the intermediate estimates at the output of each prediction stage to give a final estimate of the RLS-LMS predictor. In the RLS-LMS predictor, the first prediction stage is a simple first-order predictor with a fixed coefficient value 1. The second prediction stage uses the recursive least square algorithm to adaptively update the predictor coefficients. The subsequent prediction stages use the normalized least mean square algorithm to update the predictor coefficients. The coefficients of the linear combiner are then updated using the sign-sign least mean square algorithm. For stereo audio signals, the RLS-LMS predictor uses both intrachannel prediction and interchannel prediction, which results in a 3% improvement in compression ratio over using only the intrachannel prediction. Through extensive tests, the MPEG-4 Audio Lossless coder using the RLS-LMS predictor has demonstrated a compression ratio that is on par with the best lossless audio coders in the field. In this paper, the structure of the RLS-LMS predictor is described in detail, and the optimal predictor configuration is studied through various experiments.     

广义互缩生成器

设计了一类称为广义互缩生成器的密钥流生成器.研究表明该类密钥流生成器所产生的序列具有如下良好特性：（1）大的周期;（2）高的线性复杂度;（3）生成的广义互缩序列族具有线性空间结构,形成Abel群;（4）广义互缩序列族内序列间互相关函数值可以由控制序列中1的数目来确定;（5）在一定条件下,序列的k-错线性复杂度显著增加.另一方面对新序列进行的安全性分析结果表明,与互缩序列相比,由较少的密钥量可以获得更好的安全性.     

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.     

基于ANSYS的大功率合路器电-热-固耦合优化

为研制功率为300 W,频段为1.2~1.4 GHz的大功率合路器,在传统Wilkinson合路器的基础上,基于ANSYS多物理场仿真,利用电磁场、热特性和结构耦合仿真,分析大功率合路器的热量分布和结构变形情况。对合路器进行结构优化,保证在电气性能不恶化的情况下减少热量和变形,提高Wilkinson合路器的耐高功率性能。