无线多媒体传感器网络部署控制算法

已有的无线多媒体传感器网络（WMSNs）研究针对传感器放置在目标区域内的情况进行,且没有考虑节点通过云台的转动获得的整个可能感知区域对覆盖率的影响。本文首先针对节点放置点高于目标区域的应用进行研究,综合考虑节点感知区域和可能感知区域,建立了延时和无延时感知模型,并针对不同的感知模型提出了传感器网络部署控制算法（IVPDCA）,算法中改进了虚拟势场算法,定义了节点质量的概念来表示节点间覆盖重叠的大小,建立受力模型,使得节点在合力作用下进行重新部署,同时关闭冗余节点,既延长了网络寿命,又提高了区域覆盖率。仿真结果验证了算法的有效性。     

面向智能工业物联的灵活地址可信协议架构

IP网络在过去50年里取得了瞩目成绩,已经成为人们生产和生活过程中不可或缺的一部分。然而,随着互联网的广泛应用,IP协议体系的一些原始设计缺陷也日益暴露,难以满足未来网络的发展需求。讨论了面向2030年的互联网业务场景和关键技术挑战,提出了面向智能工业物联的灵活地址可信协议架构。新协议架构在传统IP协议全球可达、高生存性的基础之上,构建万物万网互联、确定性服务、内生安全可信、高通量传输和用户可定义等新能力,并基于这些新能力使能更多新型网络业务。     

循环-循环块相位掩膜矩阵压缩双透镜成像

压缩成像(CI)是压缩传感(CS)理论的一个重要应用领域。本文在循环矩阵和分块矩阵的基础上提出一种新的循环-循环块相位掩膜矩阵可压缩双透镜成像方法,实现对图像的随机获取。模拟实验结果表明,新的相位掩膜矩阵CI可以在显著减少测量的同时,有效捕获图像信息重建原始图像;对于64pixel×64pixel的标准Lena和Cameraman图像,在只有原像素数58.6%的无噪声测量情况下,获得的重建信噪比(SNR)分别超过26dB和30dB。新相位掩膜矩阵的研究为确定性测量在CI领域的应用提供了更多的支撑,在拥有原循环和循环确定性测量优点的同时还拥有自身的块结构特点,可以进一步减少物理实现成本,并为新的照相机设计提供若干理论、计算和技术支撑。     

端到端确定性网络架构和关键技术

新型业务如远程控制、虚拟/增强现实等对网络提出了更高的带宽和时延要求,传统网络已经难以满足。面向6G的网络演进,需要端到端确定性网络技术保障。针对当前技术标准研究缺乏对于端到端时延保障的全局考虑,指出了确定性网络需要同时解决单跳和全局的时延问题,提出了端到端确定性网络架构,以及跨网络域和跨协议层的确定性网络技术方案。最后,针对当前确定性网络应用部署的难点,提出了相应的推进建议。     

基于规则模板的正则表达式分组算法

采用规则分组的方法解决确定型有限自动机(Deterministic Finite Automata,DFA)状态爆炸问题,随着分组数目的增加,匹配效率大大降低.本文提出正则表达式的输入驱动特性理论,并基于此提出了基于规则模板的分组算法——模板有限自动机.模板有限自动机算法基于规则模板对规则集进行分组,各分组分别构建匹配引擎.理论分析和实验表明,与典型的DFA改进算法相比,预处理时间和存储空间有2～3个数量级别的缩减,且匹配效率没有明显降低.     

A NEW DETERMINISTIC FORMULATION FOR DYNAMIC STOCHASTIC PROGRAMMING PROBLEMS AND ITS NUMERICAL COMPARISON WITH OTHERS

A new deterministic formulation, called the conditional expectation formulation, is proposed for dynamic stochastic programming problems in order to overcome some disadvantages of existing deterministic formulations. We then check the impact of the new deterministic formulation and other two deterministic formulations on the corresponding problem size, nonzero elements and solution time by solving some typi     

Diffusion and propagation in triangular Lorentz lattice gas cellular automata

The diffusion process of point particles moving on regular triangular and random lattices, randomly occupied with stationary scatterers (a Lorentz lattice gas cellular automaton), is studied, for strictly deterministic scattering rules, as a function of the concentration of the scatterers. In addition to the normal and various kinds of retarded diffusion found before on the regular square lattice, straight-line propagation through the scatterers is observed.     

Accelerations for a variety of global optimization methods

Optimization methods for a given class are easily modified to utilize additional information and work faster on a more restricted class. In particular algorithms that use only the Lipschitz constant (e.g. Mladineo, Piyavskii, Shubert and Wood) can be modified to use second derivative bounds or gradient calculations. The algorithm of Breiman & Cutler can be modified to use Lipschitz bounds. Test cases illustrating accelerations to various algorithms are provided.     

Universal quantification for deterministic chaos in dynamical systems

A cell dynamical system model for deterministic chaos enables precise quantification of the round-off error growth, i.e., deterministic chaos in digital computer realizations of mathematical models of continuum dynamical systems. The model predicts the following: (a) The phase space trajectory (strange attractor) when resolved as a function of the computer accuracy has intrinsic logarithmic spiral curvature with the quasiperiodic Penrose tiling pattern for the internal structure. (b) The universal constant for deterministic chaos is identified as the steady-state fractional round-off error k for each computational step and is equal to 1/τ² ( = 0.382) where τ is the golden mean. k being less than half accounts for the fractal (broken) Euclidean geometry of the strange attractor. (c) The Feigenbaum's universal constantsa and d are functions of k and, further, the expression 2a² = πd quantifies the steady-state ordered emergence of the fractal geometry of the strange attractor. (d) The power spectra of chaotic dynamical systems follow the universal and unique inverse power law form of the statistical normal distribution. The model prediction of (d) is verified for the Lorenz attractor and for the computable chaotic orbits of Bernoulli shifts, pseudorandom number generators, and cat maps.     

A new algorithm for multi-dimensional dynamic programming problems

The new algorithm presented here solves medium size multi-dimensional dynamic programming problems in a relatively short computational time with no fast-memory restraints. The algorithm converges to the global optimal solution under some differentiability and convexity assumptions.The procedure is to solve a succession of dynamic programming problems, the state sets of which are limited to only a very small subset of the original state space. The interrelated definition of state sets for successive subproblems facilitates an algorithmic convergence while moving the subsets to contain the optimal states at the end.