面向鲁棒频谱感知的模糊K-means++算法

恶意用户通过向数据融合中心发送伪造的频谱感知数据,解决自身频谱资源短缺问题,但会极大地降低频谱感知系统的检测概率。为了解决此问题,提出了基于模糊K means++的数据融合算法。该算法首先引入模糊处理机制处理样本的数据特征值,以此来增加样本间的差异性;然后将模糊处理后的数据发送到融合中心,融合中心采用离群点挖掘的方法排除恶意用户,并对保留下来的用户进行融合,使样本向量具有鲁棒性;最后运用K means++算法对样本向量进行聚类。该算法利用轮盘法选择聚类中心,可有效抵御恶意用户的攻击,提高系统感知性能;无需知晓信号与噪声的分布等一些先验信息,也避免了繁杂的门限推导。从仿真结果可以看出,该算法对抵御恶意用户攻击具有突出的效果,有效提升了协同频谱感知系统的稳定性和鲁棒性。     

低温环境下FBG温度传感特性研究

在低温环境中,光纤光栅(Fiber Bragg Grating,FBG)材料的热膨胀系数和热光系数会发生改变,从而影响其温度传感特性。文章通过实验研究了裸光纤光栅传感器和黄铜管封装的光纤光栅传感器在低温下的温度传感特性。结果表明,在80~300 K温度范围,裸FBG温度传感器的灵敏度为6.43 pm/K,线性度为0.974,在80~230 K温度范围,温度与光纤光栅的中心波长呈现非线性关系;黄铜管封装的FBG温度传感器,在整个温度范围内灵敏度可达26 pm/K,线性度为0.996,较裸FBG温度传感器均有较大提升。对比实验表明,对光纤光栅进行封装,可以提高其温度灵敏度和线性度,改善温度传感特性。     

基于压缩感知的BOTDA性能增强方法

为了提高布里渊光时域分析系统(BOTDA)在长距离监测应用中的实时性,提出了一种基于压缩感知的布里渊光时域系统实时性增强方法。该方法包含稀疏表示、随机采样和信号重构三个部分。首先采用K-均值奇异值分解算法获得布里渊增益谱的稀疏表示,然后通过高斯随机采样和正交匹配追踪算法进行布里渊增益谱重构。为了验证所提方法的性能,仿真生成了不同信噪比水平的布里渊增益谱,搭建了45 km的布里渊光时域系统进行温度传感实验。仿真和实验结果表明:在累加平均次数为100时,所提算法将信噪比提升了6.37 dB,优于累加平均次数3000时的10.13 dB,对应测量时间减少了1/30;采用8 MHz步长数据重构布里渊增益谱,该方法的重构结果与4 MHz步长数据的相关系数为0.9992,对应扫频时间减少了一半。所提算法在保证测量精度的同时提升了测量实时性。     

提升小波与能量检测联合优化下的频谱感知方法北大核心CSCD

传统的频谱感知方法易受噪声波动的干扰,而且在低信噪比的无线通信条件下检测精度较差。通过结合提升小波去噪与动态门限能量检测算法,能有效提高传统频谱感知方法的抗噪声性能和检测精度。首先对含噪信号进行奇偶抽样,分解信号,去除噪声部分,再重构为去噪新信号,然后通过能量检测方法来统计信号的能量积累,设置动态门限,最后以动态门限判断用户信号是否存在。提升小波去噪能够有效地去除采样信号中的噪声,减少噪声对能量检测法检测精度的影响,动态门限能根据噪声波动进行调整来适应复杂的噪声环境。仿真结果表明,提升小波去噪结合动态门限能量检测算法相比于传统的频谱感知要有更优的检测精度。此方法不但提高了其对不确定噪声的抵抗性,使之能适应复杂的通信环境,而且提高了频谱感知过程的可靠性。     

基于背景先验与中心先验的显著性目标检测北大核心CSCD

针对传统的图流行排序显著性目标检测算法存在先验信息单一,显著目标检测不完整的问题,提出一种新的基于背景先验与中心先验的显著性目标检测算法。首先将图像边界节点作为背景种子进行流行排序获得粗略的前景区域,将其再次流行排序得到初步显著图;然后利用Harris角点检测、聚类实现中心先验显著性检测,捕获中心显著信息;最后在初步显著图上融合图像中心显著性,得到最终显著图。本文对综合指标、精确率-召回率曲线、F-measure值以及平均绝对误差(mean absolute error,MAE)值进行实验评估,在公开数据集MSRA-10K和ECSSD上进行的实验结果表明:对比10种主流算法,本文算法在不同的评估指标上都具有较好的表现,且能准确地突出显著目标,提升背景抑制效果。     

基于多尺度圆周频率滤波与卷积神经网络的遥感图像飞机目标检测方法研究

针对遥感图像飞机目标检测因目标尺度不一存在漏警、虚警等问题,该文基于遥感图像中飞机目标形状特征和灰度变化特点提出了一种多尺度圆周频率滤波(MSCFF)与卷积神经网络(CNN)相结合的MSCFF+CNN飞机目标自动检测算法.该算法首先采用多尺度圆周频率滤波器滤除遥感图像复杂背景,实现不同尺度飞机目标候选区域提取;然后,通过构建卷积神经网络(CNN)模型实现候选区域有效分类,最终精确确定飞机目标位置.最后,基于获取的真实遥感图像进行目标检测算法实验验证,经统计该算法的飞机目标检测率为94.38%,虚警率为3.76%,实验结果充分验证了该文算法的有效性,该算法可为机场监管、军事侦察等应用提供重要的技术支持.     

A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.     

Effect of solvent on the cation-sensitive fluorescence of polyanions bearing 4′-acryloylbenzo-18-crown-6 Units

Changes in the fluorescence intensity of polyanions bearing 4-acryloylbenzo-18-crown-6 units on the addition of cations were studied in a mixed solvent of methanol and water at 30°C. The sensitivity of the change in fluorescence intensities of the polymers toward cations was strongly enhanced compared to that of the corresponding model compound. When alkali metal cations were added, the fluorescence intensity of the polymers decreased in the orders Li⁺>Na⁺>Cs⁺>Rb⁺>K⁺ in a methanol-water (19) mixture and Li⁺>Na⁺>Rb⁺>K⁺Cs⁺ in a methanol-water (91) mixture. Alkaline earth metal cations and alkylamine hydrochlorides decreased the fluorescence intensity of the polymers in a methanol-water (19) mixture. The cation-dependent decrease in the fluorescence intensity of the polymers was affected by the water fraction in a mixed solvent of methanol and water.     

In situ sensing of the middle atmosphere with balloonborne near-infrared laser diodes

Since 1997, two near-infrared laser diode sensors have been developed with the support of the CNES, the French space agency, to provide in situ data of H(2)O, CH(4) and CO(2) in the middle atmosphere. The realized instruments were flown from stratospheric balloons within the framework of European campaigns for the study of stratospheric ozone and water vapor and were involved in the validation of the ODIN and ENVISAT satellites. In this paper, we describe the developed laser probing technique, we report atmospheric measurements and finally we discuss future perspectives, particularly the in situ laser sensing of the lower atmosphere of Mars and the implication of the laser hygrometers in balloon campaigns at mid-latitudes and tropical regions to investigate the sources and sinks of stratospheric H(2)O.     

基于荧光熄灭的二氧化硫选择性光化学敏感膜的研究

本文基于二氧硫对固定于增塑的聚氯乙烯膜上的芳香稠环化合物蒽的荧光熄灭，研制了二氧化硫选择性光化学敏感膜，经过组成优化 的敏感膜测定二氧化硫的浓度范围为6．59×10^-3-5.23×10^-1mol/L，校正曲线与理论推导的模拟曲线相吻合，且荧光信号具有较高的重现性，信号响应时间为5－30s.