并行多边形绘制技术综述

多边形绘制是应用最为广泛的计算机图形绘制方法．并行技术在提高多边形绘制系统的性能方面起着重要的作用．并行多边形绘制技术的基础是算法内在的可并行性，按并行流水线的组织方式可分为全图像深度合成、前分布拼接合成和中分布拼接合成三种，负载平衡和图像合成是影响多边形绘制系统性能的关键问题．并行多边形绘制系统的实现方式包括专用图形硬件实现、并行机实现和集群机实现．结合作者的工作，对并行多边形绘制技术进行了探讨．     

基于散列和归并技术的有效并行排序方法

本文提出一个在共享存储多处理机系统上实现的快速、有效的并行排序算法：将长度为ｎ的待排序数据划分成ｐ个长度为ｎ／ｐ的子序列，引入散列技术并行地对这ｐ个子序列的数据进行二次散列排序，这一阶段所需的平均时间为Ｏ（ｎ／ｐ）；最后并行地将ｐ个有序子序列归并成一个长度为ｎ的有序序列，归并阶段所需的时间为Ｏ（ｎ－ｎ／
／ｐ）。整个排序算法的并行执行代价为Ｏ（ｎｐ）。本排序方法可以拓以网络并行机群环境。     

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio (SNR) adaptive sorting algorithm using the time-frequency (TF) sparsity of frequency-hopping (FH) signal is proposed in this paper.Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition;then the SNR adaptive pivot threshold setting method is used to find the TF single source.The mixed matrix is estimated according to the TF matrix of single source.Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source.Theoretical analysis and simulation results show that this algorithm has good effectiveness and performance.     

Application Aware Topology Generation for Surface Wave Networks-on-Chip

The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency and power consumption over the conventional wireless NoC. Therefore, this paper studies how to optimize the network performance and power consumption by giving the packet-switching fabric and traffic pattern of each application. Compared with the conventional method of wire-linked, which adds wireless transceivers by using the genetic algorithm （GA）, the proposed maximal declining sorting algorithm （MDSA） can effectively reduce time consumption by as much as 20.4% to 35.6%. We also evaluate the power consumption and configuration time to prove the effective of the proposed algorithm.     

基于RobotStudio的芯片智能分拣生产线设计

为了缩短芯片智能分拣生产线开发周期,利用虚拟仿真技术在RobotStudio软件中搭建仿真工作站;该工作站以ABB IRB360并联工业机器人为控制核心,利用Solidworks建模软件完成相关模型建立,利用RobotStudio软件的Smart组件、机械装置等功能实现芯片下料、芯片传输、相机检测等过程,设计工业机器人的路径和程序完成分拣工作;最后将仿真工作站的代码直接下载至现场工业机器人,并根据仿真节拍调节现场各模块工作速度,实现芯片的智能分拣;实验结果表明,工业机器人能够根据相机的引导正确且高效地分拣芯片.     

工程建设标准客观优先度评价研究

在工程建设标准的制定和修订过程中，如何科学地确定编制顺序一直是困扰标准管理部门的难题，采用传统方式“按需制标”，具有盲目性、主观性，容易造成标准在制定和修订过程中的互相重复、滞后与矛盾。为了保证标准制定和修订工作的协调性，指导标准体系中各标准的制修订顺序，引入复杂网络拓扑结构，将同一标准类型下的标准构成功能团，根据相互之间的关系形成复杂网络，采用剥落算法对其网络节点重要性排序，进而确定标准制修订的优先度。通过对建筑给水排水工程规范标准修订的实证分析，总结工程建设标准的网络拓扑优先度评价的算法模型，实现制定和修订顺序的自动计算，从而更加科学地指导标准的制定和修订工作     

A microfluidic toolbox for cell fusion

Cellular fusion is a key process in many fields ranging from historical gene mapping studies and monoclonal antibody production, through to cell reprogramming. Traditional methodologies for cell fusion rely on the random pairing of different cell types and generally result in low and variable fusion efficiencies. These approaches become particularly limiting where substantial numbers of bespoke one‐to‐one fusions are required, for example, for in‐depth studies of nuclear reprogramming mechanisms. In recent years, microfluidic technologies have proven valuable in creating platforms where the manipulation of single cells is highly efficient, rapid and controllable. These technologies also allow the integration of different experimental steps and characterisation processes into a single platform. Although the application of microfluidic methodologies to cell fusion studies is promising, current technologies that rely on static trapping are limited both in terms of the overall number of fused cells produced and their experimental accessibility. Here we review some of the most exciting breakthroughs in core microfluidic technologies that will allow the creation of integrated platforms for controlled cell fusion at high throughput. © 2015 Society of Chemical Industry     

ActiveSort: Efficient external sorting using active SSDs in the MapReduce framework

In the last decades, there has been an explosion in the volume of data to be processed by data-intensive computing applications. As a result, processing I/O operations efficiently has become an important challenge. SSDs (solid state drives) are an effective solution that not only improves the I/O throughput but also reduces the amount of I/O transfer by adopting the concept of active SSDs. Active SSDs offload a part of the data-processing tasks usually performed in the host to the SSD. Offloading data-processing tasks removes extra data transfer and improves the overall data processing performance.In this work, we propose ActiveSort, a novel mechanism to improve the external sorting algorithm using the concept of active SSDs. External sorting is used extensively in the data-intensive computing frameworks such as Hadoop. By performing merge operations on-the-fly within the SSD, ActiveSort reduces the amount of I/O transfer and improves the performance of external sorting in Hadoop. Our evaluation results on a real SSD platform indicate that the Hadoop applications using ActiveSort outperform the original Hadoop by up to 36.1%. ActiveSort reduces the amount of write by up to 40.4%, thereby improving the lifetime of the SSD.