Expansion of a matrix in terms of external products of configuration vectors

The paper proposes a neural-net iterative algorithm that allows us to represent any random symmetrical N×N matrix as a weighted Hebbian series of configuration vectors with a given accuracy. The iterative algorithm is shown to demonstrate the fastest convergence when the vectors of expansion are stable nods of the N-dimensional space corresponding to the extremums of the neural-net energy functional. It so proves that all conclusions about neural networks and optimization algorithms that are based on Hebbian matrices are true for any other type of matrix. The text was submitted by the author in English.     

知识获取技术及其在网络智能故障诊断中的应用

基于遗传算法提出了一种新型的机器学习方法,它利用遗传算法的全局寻优手段,为解决知识获取这一瓶颈问题另辟捷径。论述了该方法的原理及其实现过程,并且给出了网络故障诊断应用实例,证明了它的有效性。     

The election problem in asynchronous distributed systems with bounded faulty processes

Determining the “weakest” failure detectors is a central topic in solving many agreement problems such as Consensus, Non-Blocking Atomic Commit and Election in asynchronous distributed systems. So far, this has been studied extensively for several such fundamental problems. It is stated that Perfect Failure Detector P is the weakest failure detector to solve the Election problem with any number of faulty processes. In this paper, we introduce Modal failure detector M and show that to solve Election, M is the weakest failure detector to solve election when the number of faulty processes is less than ⌈n/2⌉. We also show that it is strictly weaker than P.

Adaptive channel allocation for large-scale streaming content delivery systems

Scheduling algorithms are used in content delivery systems to control the resource allocation rate. They not only improve system efficiency, but also increase user satisfaction. Lower renege rate and less waiting time for users are the main goals for a scheduling algorithm. Among existing algorithms, On-Demand strategy does not perform well, while rate control channel allocation policies performs much better. Pure-Rate-Control (PRC) and Multiple-Service-Class (MSC) belong to the rate control algorithms. MSC performs well, but a drawback is that it uses the Hot Index, which is hard to decide and has significant effects on the performance. In order to solve this problem and to improve the overall system performance, two new algorithms, Modified MSC(MMSC) and Adaptive Algorithm (AA), are proposed in this paper. Both of them solved the problem of MSC very well and improved the overall performance. For example, the renege rate of AA is about 5.4% less than that of MMSC, and about 9.8% less than that of MSC.

Parallel Implementation of a Class of Adaptive Signal Processing Applications

Recently, High Performance Computing (HPC) platforms have been employed to realize many computationally demanding applications in signal and image processing. These applications require real-time performance constraints to be met. These constraints include latency as well as throughput. In order to meet these performance requirements, efficient parallel algorithms are needed. These algorithms must be engineered to exploit the computational characteristics of such applications. In this paper we present a methodology for mapping a class of adaptive signal processing applications onto HPC platforms such that the throughput performance is optimized. We first define a new task model using the salient computational characteristics of a class of adaptive signal processing applications. Based on this task model, we propose a new execution model. In the earlier linear pipelined execution model, the task mapping choices were restricted. The new model permits flexible task mapping choices, leading to improved throughput performance compared with the previous model. Using the new model, a three-step task mapping methodology is developed. It consists of (1) a data remapping step, (2) a coarse resource allocation step, and (3) a fine performance tuning step. The methodology is demonstrated by designing parallel algorithms for modern radar and sonar signal processing applications. These are implemented on IBM SP2 and Cray T3E, state-of-the-art HPC platforms, to show the effectiveness of our approach. Experimental results show significant performance improvement over those obtained by previous approaches. Our code is written using C and the Message Passing Interface (MPI). Thus, it is portable across various HPC platforms. Received April 8, 1998; revised February 2, 1999.     

On the analysis of stochastic divide and conquer algorithms

This paper develops general tools for the analysis of stochastic divide and conquer algorithms. We concentrate on the average performance and the distribution of the running time of the algorithm. As a special example we analyse the average performance and the running time distribution of the (2k + 1)-median version of Quicksort. Online publication October 13, 2000.     

基于有限障碍的全牛顿步内点算法

针对线性规划问题,文中引入了一种与传统障碍函数不同的新的障碍函数,其在可行域边界上的取值是有限的.沿此有限障碍函数所确定的新的搜索方向,给出了短步长全牛顿步内点算法,结果证明该算法具有目前求解线性规划问题最好的复杂性界.     

用于高速列车移动网络的资源分配实时算法

误码率高、频繁中断的高速列车移动场景宜采用多宿主容迟网络。但其资源分配计算复杂,实时性较差。基于效用差分法设计初始化过程资源分配算法,以保证过程中每一步的效用单调不减,算法时间复杂度降至O(n)。     

A weighted H1 seminorm regularization method for Fredholm integral equations of the first kind

Many problems in mathematics and engineering lead to Fredholm integral equations of the first kind, e.g. signal and image processing. These kinds of equations are difficult to solve numerically since they are ill-posed. Therefore, regularization is required to obtain a reasonable approximate solution. This paper presents a new regularization method based on a weighted H¹ seminorm. Details of numerical implementation are given. Numerical examples, including one-dimensional and two-dimensional integral equations, are presented to illustrate the efficiency of the proposed approach. Numerical results show that the proposed regularization method can restore edges as well as details.