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We present a network-level signaling mechanism for user access and service setup in light emitting diode(LED)-based visible light communication(VLC) networks and define the corresponding signaling messages.In this mechanism,lamp selection is an important step for realizing flexible user access and efficient resource allocation.Two basic selection schemes are proposed,and an enhanced bandwidth-based scheme is presented.Simulation results show the different advantages among these schemes.  相似文献   
2.
Indoor visible light communication (VLC) based on next generation environmentally friendly lighting is important in energy conservation. However, at present, the efficient characterization of VLC channels, including sophisticated reflection, has not yet been proposed. In this letter, we present a fast and comparably accurate channel characterization algorithm called independent reflecting element interaction characterization (IREIC), which can be used to describe optical power, illuminance, and impulse response.  相似文献   
3.
A room division multiplexing(RDM)-based hybrid visible light communication(VLC) network for realizing indoor broadband communication within a multi-room house is presented.The downlink information is transmitted by light-emitting diode lamps,whereas the uplink information is transmitted through WiFi.RDM is introduced to improve the VLC network throughput;in addition,the associated signaling localization and active handoff mechanisms are designed for implementation.The experimental platform demonstrates the effectiveness of the proposed hybrid architecture,along with the RDM and active handoff mechanisms.  相似文献   
4.
提出了一种基于全景环形透镜(PAL)的全景环带立体成像系统的立体信息提取方法。该成像系统由两组共轴PAL单元构成,其成像圆为两内外相接的圆环,能够实时提取360°水平视场角物体的立体信息。基于该系统的成像原理,针对非单视点成像系统特点建立相机模型对系统进行标定,并验证了标定结果。将尺度不变特征变换(SIFT)算法应用到对应点匹配上,充分利用本系统的共线约束来提高匹配的速度和准确度。应用三角测量原理从捕获的图像中提取有效深度信息。进行立体信息提取实验,给出了结果和误差分析。该系统能有效获取场景深度信息,3m距离范围误差率在±5.2%内,证明了该系统的可行性。  相似文献   
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介绍一种实验确定微电子器件单粒子翻转(SEU)灵敏体积(SV)厚度d的方法.用从低到高不同能量的重离子辐照微电子器件, 测量其SEU截面随离子射程的变化曲线σseu(r), 用数学计算从σseu(r)和LET(r)函数提取d. 利用测得的d和σseu(LET), 经过模型计算可得到更精确的空间SEU率的预言值.  相似文献   
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