Wiener models of direction-dependent dynamic systems

Analysis of systems with direction-dependent dynamics is currently limited to cases in which the dynamics in the two directions of the output are first order; results for such systems have been published for both pseudo-random maximum-length binary (MLB) and inverse-repeat maximum-length binary (IR-MLB) inputs. These relatively limited analytical results make it useful to examine alternative ways of modelling such systems and in this paper, Wiener models are considered for this purpose. Methods for optimising the Wiener model parameters by matching the system and model cross-correlation functions, outputs, and discrete Fourier transforms of the outputs are considered, and the results are compared. These methods are also applied to a first-order direction-dependent system with a maximum-length ternary (MLT) input, for which no analytical results are currently available, and to a second-order system with an IR-MLB input.     

基于双秘钥的高强度公钥密码体制的研究

该文研究双秘钥公开钥密码体制,它不同于一般的只有一个秘密密钥的公开钥密钥体制,而是使用两个秘密密钥。要攻破它必须同时解决两个数学难题(素因子分解和求离散对数),从而大大增加了破译难度。     

A Dynamic Dictionary for Priced Information with Application

In this paper we design a dynamic dictionary for the priced information model initiated by Charikar et al. Assume that a set S consisting of n elements is given such that each element has an associated price, a positive real number. The cost of performing an operation on elements of S is a function of their prices. The cost of an algorithm is the sum of the costs of all operations it performs. The objective is to design algorithms which incur low cost. In this model we propose a dynamic dictionary, supporting search, insert and delete, for keys drawn from a linearly ordered set. As an application we show that the dictionary can be used in computing the trapezoidal map of a set of line segments, a fundamental problem in computational geometry.     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.     

The complexity of some reachability problems for a system on a finite group

We consider a system defined as the product of a finite set of periodic systems on cyclic groups. It is of interest to determine if certain subgroups and unions of subgroups of the state set are reachable from a specified initial state, and in particular to determine the computational complexity of verifying such reachability. These questions are motivated by certain problems that arise in the modelling and control of discrete event systems and certain forms of periodic scheduling. Our main result is that deciding whether or not the union of a certain set of subgroups is reachable or not is NP-complete.     

GPS/电子地图组合定位技术在侦察机器人系统中的应用

介绍了GPS／电子地图组合定位技术在侦察机器人系统中的应用。对GPS接收机进行二次开发，在WINDOWS环境下，基于VB6．0，利用通信控件捕获基准站和移动站的RTCM SC－104格式差分GPS电文。分析比较了几种GPS定位算法的优劣，根据侦察机器人系统的特点，选择逆向伪距差分算法对电文进行处理，并与电子地图相结合进行精确定位。对逆向伪距差分算法的实现做了详细介绍并对实验结果做了简要分析，在电子地图的管理上，提出了将监视区域二次划分的方法，利用BMP位图，对侦察机器人所在的方位进行可视化精确定位。     

Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification

Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.     

Fast and accurate pricing of discretely monitored barrier options by numerical path integration

Barrier options are financial derivative contracts that are activated or deactivated according to the crossing of specified barriers by an underlying asset price. Exact models for pricing barrier options assume continuous monitoring of the underlying dynamics, usually a stock price. Barrier options in traded markets, however, nearly always assume less frequent observation, e.g. daily or weekly. These situations require approximate solutions to the pricing problem. We present a new approach to pricing such discretely monitored barrier options that may be applied in many realistic situations. In particular, we study daily monitored up-and-out call options of the European type with a single underlying stock. The approach is based on numerical approximation of the transition probability density associated with the stochastic differential equation describing the stock price dynamics, and provides accurate results in less than one second whenever a contract expires in a year or less. The flexibility of the method permits more complex underlying dynamics than the Black and Scholes paradigm, and its relative simplicity renders it quite easy to implement.     

How disorder can diminish avalanche risks: effect of size distribution

We study the behavior of disk assemblies with a variable disorder distribution. The packing is first consolidated and then continuously tilted very slowly. The amount of displaced disks for each tilted angle is recorded. Large displacements of the disks can occur due to some local or global mechanical instabilities. The definition of neighboring disks is based on radical (extension of Voronoï) tessellation rules to decompose, in a unique and perfectly defined manner, the two-dimensional space for polydisperse disks. In this way, by comparing the characteristics of stability for one disk to the neighboring ones for local ordered cluster, we can predict the global amount of displaced disks. Some tilting cycles have been performed to check the correlation between the instability of the packing structure (collective displacements) with micro and macro order parameters.