基于SIMD结构的多标准LDPC译码器的VLSI实现

近年来,支持多标准的LDPC译码器已逐渐成为研究的热点.与传统译码器相比,所设计的LDPC译码器具有以下优点:1.实现了一个码率、码长可配置结构,进而可以支持多种标准;2.采用了一种改进型TPMP算法,使译码器的存储器容量大大减少,避免了因分块LDPC码的非规则性所造成的数据冲突问题;3.采用基于SIMD处理器的硬件结构,实现了硬件的高度规整性,易于芯片布局布线;4.设计了一个6级可配置流水线,可分时构造校验节点处理单元和变量节点处理单元,提高了硬件利用率和系统数据吞吐率.用这种架构实现了一个同时支持CMMB和DTMB两个标准的多标准LDPC译码器;芯片规模为75万门,时钟频率为220MHz,数据吞吐率为300Mbps.     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

试析Web程序中常见字符串问题的处理

从PHP实现字符串的正确输出入手,分析了PHP中相关字符串处理函数在Web程序开发中发挥的重要作用。     

Evolutionary level set method for structural topology optimization

This paper proposes an evolutionary accelerated computational level set algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization (ESO) and level set method (LSM). Traditional LSM algorithm is largely dependent on the initial guess topology. The proposed method combines the merits of ESO techniques with those of LSM algorithm, while allowing new holes to be automatically generated in low strain energy within the nodal neighboring region during optimization. The validity and robustness of the new algorithm are supported by some widely used benchmark examples in topology optimization. Numerical computations show that optimization convergence is accelerated effectively.     

综合业务和区分业务比较研究

为了在Internet上提供有服务质量等级和保证的服务,现在已经提出的在传输层上的实现方案包括综合业务（Integtated Services）和区分业务（Differentiated Services)。本文分析了两种方案的体系结构、工作原理和服务类型,比较了二者在设计思想、实现机制、QoS特性等方面的差异和优缺点,介绍了两种模型相互结合的扩展体系结构及最新研究进展。     

大纯滞后过程的采样控制及其工程实现中的若干问题

提出了采样控制机制的新观点,介绍控制器参数确定的新方法,分析了采样控制的质量,指出了在工程实施应考虑的若干问题。     

一个基于加权和组合降维的web文本分类系统

设计了一个web文本分类系统,采用了基于统计分析和粗糙集组合的方法进行降维;降维时考虑了属性的位置信息,采用加权方式标注属性的不同重要性,以达到提高分类速度和分类准确度的目的。     

Equilibria and their bifurcations in a recurrent neural network involving iterates of a transcendental function.

Some practical models contain so complicated mathematical expressions that it is hard to determine the number and distribution of all equilibria, not mentioning the qualitative properties and bifurcations of those equilibria. The three-node recurrent neural network system with two free weight parameters, originally introduced by Ruiz, Owens, and Townley in 1997, is such a system, for which the equation of equilibria involves transcendental function tanh(x) and its iterates. Not computing coordinates of its equilibria, in this paper, we display an effective technique to determine the number and distribution of its equilibria. Without full information about equilibria, our method enables to further study qualitative properties of those equilibria and discuss their saddle node, pitchfork, and Hopf bifurcations by approximating center manifolds.     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.