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.     

3种本质安全评价方法在EDC过程中的应用研究

简要叙述ISI、i-safe和IBI 3种典型的本质安全评价方法,说明其含有的参数及指数的计算方法;然后使用这3种方法评价了直接氯化法、氧氯化法、改进的氧氯化法3个EDC生产流程.评价结果表明:不同方法评价相同的几个过程,得到的各过程安全性的高低是一致的:直接氯化法最高、氧氯化法次之、改进的氧氯化法安全性最差:评价同一个流程时,ISI法评价得到的数值最大,i-Safe法次之,IBI法最小.结合评价的过程从参数的个数及覆盖面的大小、参数取值的主观性程度、获取所需信息的难易程度和结果的灵敏性4方面比较和分析3种评价方法,最后总结得到3种方法中IBI法的优势最大.     

Stabilization of all-pole unstable delay processes by simple controllers

The stabilization of a class of all-pole unstable delay processes of arbitrary order with single unstable pole by means of simple controllers is investigated in details. Complete stabilizability conditions are established and the computational methods for determining stabilizing controller parameters presented. They provide theoretical understanding of such a stabilization problem and can also serve as practical guidelines for actual controller design.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Online learning of dynamic multi-view gallery for person Re-identification

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.     

A content-adaptive video quality assessment method for online media service

Multimedia Tools and Applications - Video quality assessment is an important issue for Internet Content Providers (ICPs) to improve their service. Some research has been done on objective video...     

An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers

This study provides evidence supporting the use of the update strategies for the support vector regression (SVR) model. Firstly, the fitting and interpolation method (FIM) is presented to select SVR parameters, and three infill strategies are adopted to search for update points. Secondly, the infill strategy and parameter selection method are illustrated by test functions that illustrate their dependability. The distribution of update points, the sample density and the proportion of update points are discussed. Finally, the adaptive SVR surrogate model is applied to optimize the protective effect of railway wind barriers. The result shows that the parameter selection method has high stability. On the whole, the accuracy of the adaptive SVR model using a suitable infill strategy will be improved with an increasing proportion of update points if the final number of training points is identical. The optimization result shows an optimal porosity of 0.117 when the height of the railway wind barrier is 2.05 m (full scale).

     

一种中文文档的非受限无词典抽词方法

本文提出了一种非受限无词典抽词模型,该模型通过自增长算法获取中文文档中的汉字结合模式,并引入支持度、置信度等概念来筛选词条。实验表明:在无需词典支持和利用语料库学习的前提下,该算法能够快速、准确地抽取中文文档中的中、高频词条。适于对词条频度敏感,而又对计算速度要求很高的中文信息处理应用,例如实时文档自动分类系统。     

基于多媒体多智能Agents系统的群体决策支持系统集成化设计研究

分析了Ｄeｓａｎｅｔｉｓ＆Ｇａｌｌｕｐｅ群体决策支持系统（Ｇｒｏｕｐ Ｄｅｃｉｓｉｏｎ Ｓｕｐｐｏｒｔ,简称ＧＤＳＳ）初步设计的通用模式,提出了将多媒体技术、智能Ａｇeｎｔs技术、数据仓库技术Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术等有机地集成于ＧＤＳＳ的集成化设计方案,并就其关键技术功能结构方案设计进行了研究．