A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.     

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.     

基于位置语义和查询概率的假位置选择算法

针对传统位置隐私保护方案中未充分考虑攻击者拥有背景知识而导致的隐私泄露问题,基于位置语义和查询概率提出一种假位置选择算法。在假位置集中的位置之间满足语义差异性、查询概率相近且地理位置尽量分散的条件下,避免了攻击者结合背景知识过滤假位置,同时保证了查询结果的精确性。仿真实验验证了所提算法能有效保护用户的位置隐私。     

基于预测和滑动窗口的轨迹差分隐私保护机制

为解决轨迹差分隐私保护中存在的隐私预算与服务质量等问题,提出了一种融合预测扰动的轨迹差分隐私保护机制。首先,利用马尔可夫链和指数扰动方法预测满足差分隐私和时空安全的扰动位置,并引入服务相似地图检测该位置的可用性;如果预测成功,则直接采用预测位置替代差分扰动的位置,以降低连续查询的隐私开销并提高服务质量。在此基础上,设计基于w滑动窗口的轨迹隐私预算分配机制,确保轨迹中任意连续的w次查询满足ε-差分隐私,解决连续查询的轨迹隐私问题。此外,基于敏感度地图设计一种隐私定制策略,通过自定义语义位置的隐私敏感度,实现隐私预算的量身定制,从而进一步提高其利用率。最后,利用真实数据集对所提方案进行实验分析,结果显示所提方案提供了更好的隐私保护水平和服务质量。     

基于电磁耦合的无线能量传输技术应用及展望

电磁耦合能量传输作为一种摆脱传统导线连接的能量传递方式,具有灵活、便捷、安全系数高等优点。在工农业生产和生活的各个领域都具用巨大的应用价值和潜力。针对电磁耦合能量传输技术优点和应用潜力,了解电磁耦合的能量传输技术研究现状,推进该技术的进一步走向实践和应用显得尤为重要。通过分析最新文献和研究成果,归纳总结出研究现状和发展趋势;研究结果表明:电磁耦合的能量传输技术的研究重点主要是如何提高传输功率、传输效率及传输距离等;该研究工作能够为电磁耦合的无线能量传输技术研究提供支撑。     

高铁基于毫米波的自适应波束分合传输方案

向拥有较宽连续频谱的高频频段扩展带宽成为未来高铁无线通信系统提升容量的有力手段,不过,需要采用大规模天线波束赋形技术克服高频频段路径损耗严重的缺陷。在高铁双车载台方案中,可以通过大规模天线阵列形成双波束传输提高系统容量。在对其传输性能进行分析后发现,双波束传输优化配置与列车的位置有关。基于此,论文提出自适应波束分合传输方案,当列车距离基站较远时,为了避免波束间的严重干扰,双波束合成为一个波束来覆盖两个接收端,实现接收分集,提高接收信号质量;当列车距离基站较近时,分裂成双波束,实现空间复用,提高系统容量及传输可靠性。数值仿真结果表明,所提方案可以适应列车运行位置,提升传输性能。     

多运动站异步观测条件下的直接定位算法

针对多运动站无源融合定位系统中的异步观测问题,该文提出一种基于空间谱分析的直接定位算法。首先利用子空间分析理论建立异步观测直接定位模型,构造多站协同定位联合空间谱函数,采用多级搜索方法实现定位解算。然后推导了该算法的定位误差协方差矩阵及克拉美罗下限。最后对该算法在异步观测条件下的定位性能进行蒙特卡洛仿真。仿真结果表明算法定位精度能够逼近克拉美罗下限,定位性能优于基于定位参数的分步定位算法,尤其在低信噪比情况下优势更明显。     

面向终端个性化服务的模糊垂直切换算法

在异构无线网络中,针对终端不同类型的应用在服务质量(QoS)需求上的差异,该文提出一种面向终端个性化服务的模糊垂直切换算法。该算法从以下两个方面提高切换性能：首先在网络发现阶段,通过预测候选网络在切换时刻的负载状态,对候选网络进行筛选;然后在切换判决阶段,根据每类应用对QoS参数的不同需求范围设计不同的隶属度函数。仿真结果表明,该算法能有效降低切换阻塞率,提高系统吞吐量,并能根据终端的应用类型合理地选择切换网络,满足了终端的个性化服务需求。     

基于虚拟时差的运动阵列空间无源定位算法

针对脉冲骨架周期固定的雷达脉冲信号辐射源无源定位问题,该文提出一种基于虚拟时差的运动阵列空间无源定位算法,采用运动单阵列接收目标辐射源信号,通过多次测量侦察站不同位置的脉冲到达时间来估计目标辐射源位置。分别针对脉冲骨架周期已知和未知两种情况,提出了一种基于高阶统计量的无源定位算法,并推导了不同情况下定位精度的克拉美罗界。仿真实验表明,不同情况下定位算法定位精度逼近克拉美罗界,且脉冲骨架周期已知时虚拟时差无源定位算法定位精度优于同孔径实阵列TDOA无源定位算法定位精度,脉冲骨架周期未知时虚拟时差无源定位算法定位精度低于同孔径实阵列TDOA无源定位算法定位精度。     

一种开槽的双频段宽带科赫分形天线

基于分形理论设计了一种开槽的双频宽带特性的科赫（以下称Koch）雪花分形天线.天线的结构紧凑,两个工作频段宽覆盖了第二代移动通信数字蜂窝系统(Digital Cellular System,DCS)/个人通信业务(Personal Communication System,PCS)频段、第三代移动通信时分同步的码分多址技术（Time Dicision-Synchronization Code Divcision Multiple AccessTime,TD-SCDMA）频段和全球微波互联接入或无线城域网(Worldwide Interoperability for Microwave Access,WiMax)频段等无线通信频段.同时,天线在各个工作频段内有良好的方向辐射性.天线模型经过二维电磁仿真软件（High Frequency Structure Simulator,HFSS）仿真分析,并对天线的样品进行了实物测量,仿真结果与测量结果有较好的一致性.