LTE-Advanced CoMP技术的研究

从CoMP技术标准化进展入手,对下行CoMP技术基本原理进行了研究,针对几种典型的CoMP方案进行了对比分析,并针对3GPP定义的场景1给出了联合传输情况下的初步仿真结果。     

电信业务支撑系统云化策略与方案研究

介绍了在电信业务支撑系统中引入云计算技术的策略与建议,并给出了一个可供参考的整体设计方案。     

基于SIP的分布式多方会议系统的安全解决方案

分析并归纳了基于扩展SIP协议的分布式会议系统的安全需求,并且提出了一套可行、有效的安全系统结构和具体解决方案。     

WLAN（802.11n）组网技术研究

对802.11n关键技术进行分析,并讨论了在组网方案、网络布署、有线网络资源需求、电源配套等方面如何利用802.11n设备组成一个建设高性能的WLAN网络。     

LTE与2G/3G系统互操作研究

为确保现有移动用户良好的业务体验感知,LTE对不同系统间的互操作做了较为完备的规定,但带来了现网设备复杂的升级和高昂的投入。本文通过对其他运营商和技术制式实现方法的介绍,提出了LTE建设初期与2G/3G系统间互操作的要求。     

城域综合承载传送网的网络架构

随着业务和电信网络的IP化和宽带化,在城域（本地）网实现各种业务的综合承载传送已成为可能。根据各种承载业务的特点和网络现状,提出城域综合承载传送网络的目标网络架构、业务承载及网络演示方式。     

填充柱超临界CO2流体色谱制备拆分4种外消旋γ-内酯香料

填充柱超临界CO2流体色谱是近年发展的一种绿色高效色谱分离技术。本文采用填充柱超临界CO2流体色谱对外消旋γ-辛内酯、γ-癸内酯、γ-十一内酯、γ-十二内酯4种香料进行制备规模手性拆分。色谱柱Chiralpak AD 250mm×10mm10μm,柱温、柱压、改性剂等参数在前期分析研究基础上设定。调节流动相流速、样品浓度得较佳分离通量,并进行制备性分离,结果:γ-辛内酯,上样2mg/次,每次运行9min,180min分离40mg,回收率(以2个对映体计,下同)66.3%;γ-癸内酯,上样0.8mg/次,每次运行9min,180min分离16mg,回收率68.1%;γ-十一内酯,上样0.8mg/次,每次运行11min,220min分离16mg,回收率64.4%;γ-十二内酯,上样0.7mg/次,每次运行11min,220min分离14mg,回收率71.4%。上述分离制备所得对映体的光学纯度(e.e%)均为100%。     

7种面酱的电子鼻和电子舌辨别分析

采用电子鼻和电子舌技术,对煮制前后7种面酱香气和口感进行检测,并对所得数据进行PCA和SQC分析。结果表明:煮制前六必居甜面酱与天源面酱之间的香气非常相似,经煮制后,差异比较大。麦酱和馒头酱的香气在煮制前后都比较接近,其区别指数分别为46.77%和56.61%。十笏园面酱和六必居面酱煮制前的香气特点截然不同,而煮制后的香气比较接近。自制的馒头酱与麦酱虽然香气比较相近,但在口感方面的差别比较明显。煮制前,六必居面酱、十笏园面酱与自制麦酱的口感差别明显,但煮制之后差别不大。     

基于小波变换的列车广播声压自适应调整算法

随着社会交通水平的日益提高,对列车广播质量与稳定性要求也越来越高。列车噪声包括列车的振动噪声与乘客的语音噪声,由于乘客语音噪声特性与广播本身类似,因此在该噪声背景下采用传统信号处理方法进行去噪遇到很大困难。采用改进的自适应小波滤波器,对列车广播时的实际环境所采集的信号进行小波分解与重构,然后用一个以人耳主观舒适度为基础的SVM声功率参考模型,再通过前一时刻的广播、噪声声压级来修正当前时刻的广播声压级,在列车环境下达到了良好的语音去噪效果。     

Abrupt motion tracking via intensively adaptive Markov-chain Monte Carlo sampling

The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. While various particle filters and conventional Markov-chain Monte Carlo (MCMC) methods have been proposed for visual tracking, these methods often suffer from the well-known local-trap problem or from poor convergence rate. In this paper, we propose a novel sampling-based tracking scheme for the abrupt motion problem in the Bayesian filtering framework. To effectively handle the local-trap problem, we first introduce the stochastic approximation Monte Carlo (SAMC) sampling method into the Bayesian filter tracking framework, in which the filtering distribution is adaptively estimated as the sampling proceeds, and thus, a good approximation to the target distribution is achieved. In addition, we propose a new MCMC sampler with intensive adaptation to further improve the sampling efficiency, which combines a density-grid-based predictive model with the SAMC sampling, to give a proposal adaptation scheme. The proposed method is effective and computationally efficient in addressing the abrupt motion problem. We compare our approach with several alternative tracking algorithms, and extensive experimental results are presented to demonstrate the effectiveness and the efficiency of the proposed method in dealing with various types of abrupt motions.