基于IEEE 80211e EDCA的下行信道动态无竞争接入机制*

为了改善无线局域网络服务质量,IEEE工作组形成了增补标准IEEE 802.11e,但是其增强的接入机制仍然不能完全满足IPTV、视频点播等类似场景的应用。为此提出了针对这一类应用场景的一种新的简单易行的下行信道动态无竞争接入机制,该机制基于MAC层队列长度信息动态调整下行信道接入参数,能有效地保护下行流媒体数据的传输,通过仿真结果验证了该机制有效并且实用。     

一种新的H.264/AVC双螺旋FMO模式

灵活宏块排序FMO（Flexible Macroblock Ordering）是H.264/AVC标准提出的一项非常有效的容错机制,其中包括六种标准的FMO方式,其针对不同的视频序列以及信道特点各有优劣。通过仿真分析了标准中六种FMO方式的特点,提出了一种新的双螺旋FMO方式。仿真结果表明该方式在一定的编码码率条件下能取得更好的信道传输适应性,且容错效果优于大多数标准的FMO方式。     

无线链路多用户视频流传输QoS保证：延时限制及优先级丢包

To provide a certain level of Quality of Service (QoS) guarantees for multiuser wire-less downlink video streaming transmissions, we propose a multiuser scheduling scheme for QoS guarantees. It is based on the classic Queue-Length-Based (QLB)-rate maximum scheduling algorithm and integrated with the delay constraint and the packet priority drop. We use the large deviation principle and the effective capacity theory to construct a new analysis model to find each user’s queue leng-th threshold (delay constraint) violation prob-ability. This probability corresponds to the upper bound of the packet drop probability, which indicates a certain level of statistical QoS guarantees. Then, we utilize the priority information of video packets and introduce the packet priority drop to further improve the quality perceived by each user. The simu-lation results show that the average Peak Signal to Noise Ratio (PSNR) value of the priority drop is 0.8 higher than that of the non-priority drop and the PSNR value of the most badly damaged video frame in the priority drop is on an average 4 higher than that of the non-priority drop.     

基于深度强化学习的机械臂控制快速训练方法

人工智能在机器人控制中得到广泛应用,机器人控制算法也逐渐从模型驱动转变为数据驱动。深度强化学习算法可在复杂环境中感知并决策,能够解决高维度和连续状态空间下的机械臂控制问题。然而,目前深度强化学习中数据驱动的训练过程非常依赖计算机GPU算力,且训练时间成本较大。提出基于深度强化学习的先简化模型（2D模型）再复杂模型（3D模型）的机械臂控制快速训练方法。采用深度确定性策略梯度算法代替机械臂传统控制算法中的逆运动学解算方法,直接通过数据驱动的训练过程控制机械臂末端到达目标位置,从而减小训练时间成本。同时,对于状态向量和奖励函数形式,使用不同的设置方式。将最终训练得到的算法模型在真实机械臂上进行实现和验证,结果表明,其控制效果达到了分拣物品的应用要求,相比于直接在3D模型中的训练,能够缩短近52%的平均训练时长。     

无线局域网视频流组播优化机制

针对网络电视业务的802.11无线局域网数据传输问题,提出一种视频流组播优化机制,基于视频帧区分的方法,对关键帧I帧采用可靠组播机制传输,对非关键的B帧和P帧采用组播机制传输。仿真结果表明,该机制可以在丢帧率和数据延迟之间取得较好的折中,尤其是在无线网络带宽不足的情况下,能够有效提高数据传输后视频的观看质量。     

基于NS2的802.11n D信道模型实现

分析马尔科夫随机过程信道建模机制,研究802.11n信道建模方案,基于查找信道容量、误包率、信噪比三者之间对应关系表格的方法,在NS2软件中实现802.11n D信道模型仿真模块。通过比较NS2仿真结果和TGn参考模型验证该模块的准确性,为后续相关802.11n协议的研究与改进提供有效的物理层信道仿真基础。     

IEEE802.11无线局域网视频流传输综述

With relatively high transmission capacity and usually unconstrained connections, IEEE802.11 WLANs provide the ideal infrastructure for pervasive video content sharing and communications. However, the delivery of high-performance video streams over 802.11 WLANs remains a challenging task due to the inherent characteristics of compressed video and dynamic channels. In this paper, we present a brief survey of various recent innovations that have been developed to enhance the Quality of Service (QoS) performance for video over WLANs. Based on the application scenarios, the solutions have focused mainly on three network layers, that is, Application layer (APP), Media Access Control layer (MAC), and Physical layer (PHY). After reviewing the video compression technology, we first examine various single-layer solutions for video over WLANs. We then discuss several cross-layer solutions that take advantage of mutual interactions between different network layers. Finally, several technical issues beyond QoS performance, including energy and security, are also addressed. We conclude that the application of video over WLANs will continue to increase in future.     

基于人工神经网络的远距离高精度温度测量系统设计

该远距离高精度温度测量系统由数据拟合模块、红外测温模块、超声波测距模块、数据处理模块以及按键显示模块组成。系统通过数据拟合模块实现采集的大量“输入—目标”样本向量数据对人工神经网络(ANN)进行训练,并三阶拟合出修正函数。实际测量时,数据处理模块采用已获得的修正函数对超声波测距模块测量的距离系数d与红外测温模块测量的初步温度进行融合运算,最终输出被测单位修正后的温度。     

微波功率放大器芯片的热分析

建立适当模型对微波功率放大器芯片的单个晶体管进行温度分析,仿真结果表明,最高温度高于晶体管正常工作温度。从封装上采取措施,对芯片-粘接材料-基板这一基本结构进行分析,最后解决了温度过高问题。