混沌跳频通信系统的设计与仿真

跳频通信系统的关键技术是跳频序列的同步。本文利用混沌信号Logistic序列作跳频码,取代跳频通信中的伪随机序列,并采用动态双频同步方案使同步头信号同步,解决了混沌跳频序列的同步难题,进而实现发射系统和接收系统的混沌同步。在Matlab7.0/Simulink动态仿真平台,搭建了一个基于该同步方案的跳频通信系统,仿真结果表明该同步方案用于跳频通信可获得满意的通信效果。     

基于混沌的跳频通信系统

利用混沌信号取代跳频通信中的伪随机序列,其关键问题是混沌序列的相关特性和发射系统接收系统的混沌同步。该文提出利用Chen’s混沌序列构造跳频码,分析了该混沌序列的相关特性,并与Lorenz混沌序列进行比较,并讨论了存在于扰下的同步性能。应用MATLAAB进行仿真,结果表明该混沌序列具有良好的相关特性和稳定的同步性能,应用于跳频通信可获得较好的效果。     

时空混沌混合跳扩通信系统

针对跳扩通信系统中直扩和跳频的二维性,提出了一种时空混沌跳扩通信系统.通过时空混沌,不仅可以增强系统的保密性,又能同时产生实时的直扩序列和跳频图案序列,减小了系统实现的复杂度.此外,文中产生的PN序列和跳频图案序列的周期易于控制,可以任意改变.使系统更加灵活,保密性更高.文中对新构建系统的误码率性能进行了分析,仿真结果表明,该系统具有理想的误码率性能,抗多址干扰能力强,具有非常广阔的应用前景.     

基于异步时钟序列的分布式认知跳频算法

为了解决传统跳频算法对动态、复杂电磁干扰环境适应能力不足的难题,以及克服现有跳频算法在分布式通信环境下对公共控制信道和统一时钟源的依赖,提出了一种基于异步时钟序列的分布式认知跳频算法。该算法通过建立认知跳频通信系统模型,分析其通信过程中面临的关键问题,设计了一种应用于分布式跳频通信环境的异步时钟结构序列。基于该序列的分布式认知跳频算法不依赖于任何公共控制信道和统一时钟源,能够在复杂电磁干扰环境下达到高效抗干扰通信的效果。验证实验结果表明,针对典型的窄带、宽带噪声干扰、部分频段干扰以及随机干扰,所提算法的抗干扰性能均显著优于传统的跳频通信算法以及现有的公用控制信道认知跳频算法。     

非重复宽间隔跳频序列的优化

跳频序列发生器是跳频通信系统的核心部件,寻求理想的跳频序列是研究跳频通信的重要课题之一.通过对RS码跳频序列相关技术的研究,改进了基于RS码宽间隔非重复跳频序列的优化方法,利用Matlab软件进行了仿真,并绘制出了某个跳频序列的汉明相关性.计算结果表明,该方法克服了对偶频带法产生的宽间隔的跳频序列随机性较差的缺点,提高了整个跳频序列的平均间隔,满足相邻频率值大于某个固定的常数,同时也满足宽间隔非重复跳频序列族的理论界限.     

一类跳频序列集的最优构造

跳频扩频是主要的扩频编码技术之一,跳频序列在跳频码分多址系统中的作用至关重要。分圆理论由于具有良好的特性,在组合设计、良好的二元随机序列设计中得到了广泛应用。基于分圆理论,构造了一类关于Peng-Fan界最优的跳频序列集,节省了一个频隙,丰富了跳频序列集的构造方法。结果表明,该构造方法简单易行,对跳频通信系统性能的提高具有一定的指导意义。     

跳频码序列混沌预测的性能研究

一般的跳频码序列预测研究是建立在理论跳频码序列模型上,通过证明该模型是混沌的,然后利用混沌时间序列预测技术对它进行预测。为了分析跳频码序列预测的性能,引入了实际的跳频通信系统跳频码序列模型,并指出跳频码序列是由混沌的信息跳序列和周期性的同步跳序列构成。在理论分析的基础上,对跳频码序列预测进行了仿真。结果表明了跳频码序列混沌预测在没有同步跳时能取得较高命中率,但在有同步跳的情况下,命中率不高,因此在实际应用中有很大局限性。     

CH-DSSS系统伪随机序列的生成策略

为有效提高军用通信系统的抗干扰和反侦察性能,可采用跳码直扩及扩展的跳码/跳频通信方案,系统直扩伪随机序列随时间跳变,序列的线性复杂度与非线性复杂度性能是系统抗干扰和抗截获的重要保证;以Berlekamp-Massey迭代算法对序列进行线性复杂度分析,以生成算法对序列进行Lempel-Ziv非线性复杂度测试,结果表明GOLD序列线性复杂度低,在跳码系统中应用会存在抗相关干扰和抗截获方面的隐患,而DFS结构的混沌序列在随机特性尤其是线性复杂度与非线性复杂度方面的性能良好,因而在跳码直扩系统中具有重要的应用价值.     

跳频预测干扰之跟踪模型的改进

在跳频通信的研究中,针对实现实时跳频跟踪干扰问题,跳频预测干扰是通信电子战中跳频干扰中一项有效的技术.传统的跳频预测干扰的跟踪模型是跳频码序列预测.跳频码序列预测技术在处理实际的跳频通信码序列时实时性差,命中率并不高.为解决上述问题,提出了改进的跟踪模型,跳频同步频率集预测.同时还建立了更客观的新的预测性能评价标准模型.进行仿真的结果表明,与跳频码序列预测相比,跳频同步频率集预测有更高的实时性,使跳频预测提高了跳频跟踪干扰的效率.     

基于概率的自适应跳频通信系统中的跳频图案产生方法

为了提高水声跳频通信系统的可靠性和保密性,提出了基于误码率映射和对伪随机数流的量化来产生跳频图案的方法,该方法将各个跳频频点的误码率映射为一个概率密度函数,由此得到量化向量,根据伪随机数发生器产生的满足特定统计规律的伪随机数流和特定的量化准则,对伪随机数流进行量化,生成跳频图案,进行跳频通信。构建基于概率的自适应跳频通信仿真系统,仿真结果表明基于概率的自适应跳频通信相对于传统的跳频通信或自适应跳频通信误码率更低,且保密性更强。     

基于去冗余特征和语义关系约束的零样本属性识别

基于生成式的零样本识别方法在生成特征时受冗余信息和域偏移的影响,识别精度不佳.针对此问题,文中提出基于去冗余特征和语义关系约束的零样本属性识别方法.首先,将视觉特征映射到一个新的特征空间,通过互相关信息对视觉特征进行去冗余处理,在去除冗余视觉特征的同时保留类别的相关性,由于在识别过程中减少冗余信息的干扰,从而提高零样本识别的精度.然后,利用可见类和不可见类之间的语义关系建立知识迁移模型,并引入语义关系约束损失,约束知识迁移的过程,使生成器生成的视觉特征更能反映可见类和不可见类之间语义关系,缓解两者之间的域偏移问题.最后,引入循环一致性结构,使生成的伪特征更接近真实特征.在数据集上的实验证实文中方法提高零样本识别任务的精度,并具有较优的泛化性能.     

Ground adaptive and optimized locomotion of snake robot moving with a novel gait

In this paper, we present a novel gait, forward head serpentine (FHS), for a two dimensional snake robot. The advantage of this new gait is that the head link remains in the forward direction during motion. This feature significantly improves snake robot potential applications. Genetic Algorithm (GA) is used to find FHS gait parameters. Relationship between FHS gait parameters and friction coefficients of the ground are developed. Next, robot speed is considered in the optimization. A fitness function covering robot speed and head link angular changes is defined. A general sinusoidal wave form is applied for each joint. GA is used to find gait parameters resulting in maximum speed while head link angular changes remain in an acceptable range. Optimal gait parameters are also calculated for different friction coefficients and relationships between them are developed. Experiments are also performed using a 5-link snake robot. It is shown that experimental and theoretical results closely agree.     

相干命题逻辑自然推理系统NR的自动证明*

给出了相干命题逻辑自然推理系统NR的自动证明算法。首先将待证命题公式A的子公式组成一个初始集合P,对其中的元素采用系统NR的推理规则得到新的命题公式加入P,当得到秩为0的A时命题得证;然后对A的证明树进行整理即得到演绎序列。对系统NR的大部分定理证明取得了良好的效果,算法生成的演绎序列清晰可读,接近手工推理。     

Toward a group delay reduction in digital filtering

Finite impulse response (FIR) filters are often used in digital signal processing applications because their linear phase properties do not introduce group delay distortion. While this property is desirable, we may also desire that the filter exhibit zero group delay to suppress the causal time offset between the input and output of the filter. We can minimize the causal delay by the use of recursive minimum phase filters but these introduce an objectionable group delay distortion. We desire both zero group delay and a nonrecursive impulse response (IR). This filter can be applied to input signals indirectly through a modified overlap and save FFT based circular convolution. The desired result is obtained in our proposed new filtering technique: the modified overlap and save method (MOSM). We accomplish this by redefining the time origin of the prototype filter's impulse response by circularly shifting the response in the FFT filter time vector till the symmetry point coincides with the vector's zero index.     

基于双重离散对数DAA协议的设计*

TCG 规范中的DAA(direct anonymous attestation)方案以CL 数字签名(Camenisch and Lysyanskaya signature scheme)为基础,结合了群签名和零知识证明等技术来实现直接匿名证明,其安全性是基于大数分解难解问题。随着计算机性能的提高和密码学的发展,基于大数分解难题问题很可能被攻击,相应的DAA方案也就变得不再安全,而基于椭圆曲线上的离散对数和素数域上的离散对数（双重离散对数）的算法可以使用较短的密钥,实现较高的安全性。正是基于双重离散对数     

Shift Walsh matrix and delay-differential equations

Shift Walsh matrix is first introduced. Them times mshift Walsh matrix is formed from them times mWalsh matrix by shifting the columns of the Walsh matrix to the right, dropping the lastk(m geq k > O)columns and assigning firstkcolumns of the new matrix as zero elements. Delay Walsh functions can be expanded in terms of Walsh functions using shift Walsh matrix. Therefore, linear delay-differential equations can he analyzed by Walsh series approximation. The method is most useful for time-varying systems.     

基于三分量预测误差差值扩展的可逆数据隐藏

针对传统差值扩展存在过分修改像素值、须嵌入定位图等缺陷,提出了一种新的彩色图像可逆数据隐藏算法。首先利用色彩分量之间的相关性减小差值,并根据扩展方向和差值的符号,用较小的差值扩展量修改部分色彩分量的像素值,扩展方向由可能产生上溢和下溢的像素数量决定;其次采用单向像素值调整的方法避免像素值溢出,并用少量的调整信息代替定位图,嵌入容量大幅提高;最后改进差值直方图平移技术控制嵌入容量和图像失真。提取端根据临界像素值的顺序定位调整的像素位置,用调整信息恢复调整的像素值,在提取信息的同时可无损地恢复原始图像。实验结果表明,所提出的算法在大幅提高嵌入容量的同时,仍能保持较高的图像质量。     

Video object tracking based on improved gradient vector flow snake and intra-frame centroids tracking method

Accurately tracking the video object in video sequence is a crucial stage for video object processing which has wide applications in different fields. In this paper, a novel video object tracking algorithm based on the improved gradient vector flow (GVF) snake model and intra-frame centroids tracking algorithm is proposed. Unlike traditional gradient vector flow snake, the improved gradient vector flow snake adopts anisotropic diffusion and a four directions edge operator to solve the blurry boundary and edge shifting problem. Then the improved gradient vector flow snake is employed to extract the object contour in each frame of the video sequence. To set the initial contour of the gradient vector flow snake automatically, we design an intra-frame centroids tracking algorithm. Splitting the original video sequence into segments, for each segment, the initial contours of first two frames are set by change detection based on t-distribution significance test. Then, utilizing the redundancy between the consecutive frames, the subsequent frames’ initial contours are obtained by intra-frame motion vectors. Experimental results with several test video sequences indicate the validity and accuracy of the video object tracking.     

基于评价网络近似误差的自适应动态规划优化控制

为了求解有限时域最优控制问题,自适应动态规划(ADP)算法要求受控系统能一步控制到零。针对不能一步控制到零的非线性系统,提出一种改进的ADP算法,其初始代价函数由任意的有限时间容许序列构造。推导了算法的迭代过程并证明了算法的收敛性。当考虑评价网络的近似误差并满足假设条件时,迭代代价函数将收敛到最优代价函数的有界邻域。仿真例子验证了所提出方法的有效性。     

Tracking the best hyperplane with a simple budget Perceptron

Shifting bounds for on-line classification algorithms ensure good performance on any sequence of examples that is well predicted by a sequence of changing classifiers. When proving shifting bounds for kernel-based classifiers, one also faces the problem of storing a number of support vectors that can grow unboundedly, unless an eviction policy is used to keep this number under control. In this paper, we show that shifting and on-line learning on a budget can be combined surprisingly well. First, we introduce and analyze a shifting Perceptron algorithm achieving the best known shifting bounds while using an unlimited budget. Second, we show that by applying to the Perceptron algorithm the simplest possible eviction policy, which discards a random support vector each time a new one comes in, we achieve a shifting bound close to the one we obtained with no budget restrictions. More importantly, we show that our randomized algorithm strikes the optimal trade-off $U = \Theta(\sqrt{B})$ between budget B and norm U of the largest classifier in the comparison sequence. Experiments are presented comparing several linear-threshold algorithms on chronologically-ordered textual datasets. These experiments support our theoretical findings in that they show to what extent randomized budget algorithms are more robust than deterministic ones when learning shifting target data streams.