Throughput and delay analysis of multi-channel wireless infrastructure networks

Wireless infrastructure networks (WINs) provide ubiquitous connectivity to mobile nodes in metro areas. The nodes in such backbone networks are often equipped with multiple transceivers to allow for concurrent transmissions in multiple orthogonal channels. In this study, we develop an analytical model for the estimation of the delay and throughput performance of wireless infrastructure networks employing slotted ALOHA channel access and slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) over multiple channels. The analytical model, which takes into account the correlation due to multi-hop transmissions, approximates the performance observed through simulations accurately.     

多通道相位一致性高温超导滤波器组

利用高温超导薄膜材料的低插损特性,实现了具有相位一致性的新型高温超导高性能三通道滤波器组,该器件置于接收机前端系统的低噪声放大器前可以使接收机前端系统同时具有带外抗干扰特性和带内低损耗特性,并具有极高的灵敏度。     

一种多信道无线数据采集系统

通过对嵌入式技术以及无线传输收发技术的研究,提出了一种基于多信道无线数据传输技术和CSMA技术的压力监测无线多机通信系统设计方案。该系统克服了传统有线压力监测通信系统的局限性,使用极为方便。给出了该系统测量数据无线传输的原理和设计方法。实践表明,系统具有推广和应用价值。     

多通道小波在掌纹图象几何变换与消噪中的应用

提出了三通道小波变换的思路，解决了实际应用中获得的掌纹图象，由于采样问题而产生严重几何变形的校正问题，采用QMF的方法构造出多通道小波滤波器组，三通道小波分解后进行二插值重建对数字图象进行重采样，从而达到图象几何变换的目的，小波变换有分频的特性，在对图象进行几何变换处理的同时，也对采样效应引起的噪声（Morie条纹）进行了消除，掌纹图象经过处理后的效果令人满意，从而证明方案可行。     

无线环境中的数据广播调度理论分析及算法研究

数据广播是无线环境中一种独特的数据发布方式。首先系统地归纳了周期广播、on-demand广播和混合广播3种广播方式的理论分析模型及其特点,然后针对单信道和多信道对各种广播方式的调度算法进行了分类、比较,并突出了理论模型对调度算法研究的指导价值。最后指出今后的研究方向。     

基于LabVIEW串口通信的多路数据采集系统

本文介绍了一种基于LabVIEW软件平台的单片机多路数据采集系统的设计原理和过程。本设计选用ATmega 128单片机组成的系统作为前端数据采集系统进行多路数据采集,通过RS-485串口通信将数据传输至上位机;利用LabVIEw强大的数据处理和显示功能对采集的数据进行实时处理、分析、显示和存储。     

基于PXI总线的飞机电气多通道分布式测试系统

设计了一种基于工业以太网和PXI总线技术的飞机电气参数多通道分布式测试系统,提供了软硬件设计方案,满足了现场以及远程多通道数据同时测量的要求,并实现了测试数据的实时采集和后期回放。     

A hybrid channel assignment strategy to QoS support of video-streaming over multi-channel ad hoc networks

Currently multi-channel multi-interface ad hoc networks (multi-channel ad hoc networks) have received amount of interest, especially under the context of real-time traffics, such as video applications. Channel assignment is one of the key challenges in multi-channel ad hoc networks. In this paper, based on a representative hybrid channel assignment strategy named as HMCP, a statistic link load based hybrid channel assignment strategy, SLL-HCA, is presented to obtain a better channel assignment metric than that in HMCP. SLL-HCA is based on the HMCP protocol and adopts the statistic link load metric to ensure load balancing in a two-hop neighborhood, and to prevent both the hidden node problem and the exposed node problem. In addition, an enhanced strategy, VE-SLL-HCA, by setting harsher channel assignment conditions and reserving lower interference of routing path for video-streaming traffic than other non-video traffic, is proposed to improve the QoS support of video-streaming over multi-channel ad hoc networks. Simulation results show that SLL-HCA achieves better throughput performance than HMCP in the presence of background traffic including CBR traffic or VBR traffic; and, the PSNR QoS metric for video-streaming is enhanced when using VE-SLL-HCA compared with SLL-HCA and HMCP, which confirms the validity of the proposed strategy for video-streaming traffics. Moreover, the simulation results also indicate that non-video traffic unlikely suffers an unacceptable performance in terms of throughput.     

基于分布式压缩感知的高分宽幅SAR动目标成像技术

高分宽幅SAR动目标成像对目标跟踪具有重要的意义,常规天基多通道SAR技术要实现高分宽幅动目标成像需要通道数量巨大,系统复杂度过高,而且图像在方位向存在成对回波,形成虚警。针对上述问题,该文提出了一种基于分布式压缩感知的高分宽幅SAR动目标成像技术,在通道数量较大时,通道数量相比常规高分宽幅动目标成像构型通道数量约降低1倍,利用动目标稀疏特性和杂波背景非稀疏特性构建分布式压缩感知观测模型,采用先方位1维分布式压缩感知重建再距离方位2维分布式压缩感知重建,实现杂波背景和稀疏动目标的重建,并抑制多通道SAR动目标成像中的成对回波。结合RADAR-SAT数据的仿真试验结果验证了该技术的有效性。      

Deeply fusing multi-model quality-aware features for sophisticated human activity understanding

Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently. Nowadays, human action recognition is still a tough challenge due to intra-class and inter-class, environment and temporal-level differences of the same action. Algorithms based on the single visual channel cannot achieve satisfactory performance. Thus, in this paper, we propose a novel action recognition framework towards sophisticated activity understanding, focusing on intelligently combining multimodel quality-related action features. Specifically, we first design a multi-channel feature fusion (MCFF) algorithm to capture visual appearance, motion and acoustic patterns from each video frame, where image-level labels are characterized by choosing high quality multimodel features. Subsequently, we design an adaptive key frame selection algorithm that can be applied to characterize human action from human action video stream. Thereafter, we engineer a multimodel feature based on an auxiliary human action retrieval system to achieve sophisticated activity understanding. Extensive experimental evaluations have demonstrated that the effectiveness and robustness of our proposed method.