嵌入式系统设计的新思路—SOC

90年代以来,SOC（Sytem On Chip)已成为微电子芯片技术发展的热点,SOC技术的出现表明了微电子技术由传统的IC（电路集成）向IS（系统集成）发展,它的出现拓展了嵌入式系统的设计思路,提高了设计的功能与效率。本文以便携式MP3播放器的设计为例,从一个方面展示了SOC技术的特点和优势。     

新型的单线式数字测温器件DS1820的优良性能及其应用

对 DS18 2 0测温器件的主要性能特点、组成原理及实际应用作了简要说明和论述。DS182 0的使用极大地简化了测温器件与计算机的接口电路 ,该测温器件的优良性能特别适合在楼宇自控、过程控制等计算机控制系统中发挥其最大的优势。     

分布交互三维视景行为-特征建模方法研究

分布交互三维视景提出了两个新的问题;更有效的建模方法和如何解决视景质量与Ｉｎｔｅｒｎｅｔ传输速度的矛盾？针对以上两个问题,提出了一种更为有效的分布交互三维视景“行为－特征建模”方法及其相应的分布交互三维视景体系结构。     

IP电话系统中的消回声处理

分析了IP电话系统中回声的特点,并针对其回声具有路径延尺、延尺抖动大等特点,提出了一种基于延尺分析的自适应回声消除器设计的新思想和相关算法。介绍了自适应回声消除器的基本结构及各个功能模块,例如：变阶自适应滤波器,语音状态检测器,延尺分析器和NLMS算法控制器。本文还详细论述了以TMS320C6201DSP为硬件平台实现能运用在IP电话网关中的自适应回声消除器及其在IP电话系统中的连接关系。在文章的最后,列举了自适应回声消除器的性能测试结果,并分析了自适应回声消除器的优点,证明了自适应回声消除器能很好地解决IP电话系统中的回声消除问题。     

工作流管理系统技术现状及其发展趋势

随着计算机技术的发展和应用,工作流管理技术势必成为企业集成业务流程、节约运作成本的重要环节,工作流技术正经历迅猛发展,新型的工作流管理系统不断出现,有必要对工作流技术的发展现状及其趋势做一个总结,本文主旨也正在于此。     

工作流管理系统技术现状及其发展趋势

随着计算机技术的发展和应用,工作流管理技术势必成为企业集成业务流程、节约运作成本的重要环节,工作流技术正经历迅猛发展,新型的工作流管理系统不断出现,有必要对工作流技术的发展现状及其趋势做一个总结,本文主旨也正在于此。     

互补网络中多级混合过滤系统的研究

针对适合互补结构网络信息特点的过滤机制进行研究,提出一种改进的用户兴趣模型,在研究内容过滤和协作过滤的基础上．建立适合互补结构网络的多级混合过滤系统。通过对多级混合过滤模型的实验,表明多级混合过滤模型克服了单独使用内容过滤或协作过滤方法的缺点,提高了过滤的准确度。     

Bias and Controversy in Evaluation Systems

Evaluation is prevalent in real-life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, audio) sharing, and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential "bias" of reviewers or "controversy" objects. The statistical approach of averaging deviations to determine bias and controversy assumes that all reviewers and objects should be given equal weight. In this paper, we look beyond this assumption and propose an approach based on the following observations: (1) evaluation is "subjective", as reviewers and objects have varying bias and controversy respectively, and (2) bias and controversy are mutually dependent. These observations underlie our proposed reinforcement-based model to determine bias and controversy simultaneously. Our approach also quantifies "evidence", which reveals the degree of confidence with which bias and controversy has been derived. This model is shown to be effective by experiments on real-life and synthetic datasets.     

Identification of tubulin drug binding sites and prediction of relative differences in binding affinities to tubulin isotypes using digital signal processing

Microtubules are involved in numerous cellular processes including chromosome segregation during mitosis and, as a result, their constituent protein, tubulin, has become a successful target of several chemotherapeutic drugs. In general, these drugs bind indiscriminately to tubulin within both cancerous and healthy cells, resulting in unwanted side effects. However, differences between beta-tubulin isotypes expressed in a wide range of cell types may aid in the development of anti-tubulin drugs having increased specificity for only certain types of cells. Here, we describe a digital signal processing (DSP) method that is capable of predicting hot spots for the tubulin family of proteins as well as determining relative differences in binding affinities to these hot spots based only on the primary sequence of 10 human tubulin isotypes. Due to the fact that several drug binding sites have already been characterized within beta-tubulin, we are able to correlate hot spots with the binding sites for known chemotherapy drugs. We have also verified the accuracy of this method using the correlation between the binding affinities of characterized drugs and the tubulin isotypes. Additionally, the DSP method enables the rapid estimation of relative differences in binding affinities within the binding sites of tubulin isotypes that are yet to be experimentally determined.     

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.