基于功率谱分段对消频谱感知算法研究及性能分析

在实用的认知无线电系统中,频谱感知技术必须具备在噪声电平高动态变化和无线信道严重衰落电磁背景下,进行实时盲频谱感知的能力,这为经典的频谱感知算法带来巨大的挑战。该文提出的功率谱分段对消频谱感知算法,依据傅里叶变换的渐进正态性和相互独立性,计算出功率谱的统计特性,利用监测频带内部分谱线强度和与全部谱线强度和的比值作为检验统计量进行信号存在性的判断。该文推导了算法的虚警概率和不同信道模型下正确检测概率的数学表达式,并依据Neyman-Pearson准则得到判决门限的闭式表达式。理论分析和仿真结果均表明：功率谱分段对消频谱感知算法对噪声不确定度具有鲁棒性;固定信噪比,算法的频谱感知性能不受噪声电平改变的影响;应用于高斯白噪声和平坦慢衰落信道中,可在较宽的信噪比范围内获得较优越的频谱感知性能;算法计算复杂度低,可在微秒级时长内完成频谱感知。     

三棱镜折射率测量结果的不确定度评定

根据不确定度的有关概念及具体实验教学模型 ,提出了测量不确定度的简化模式. 结合分光计 测三棱镜折射率的例子 ,进行了具体分析 ,给出了其测量不确定度的最终评定.     

第Ⅱ种强度不等的两态叠加多模叠加态光场的等阶N次方H压缩——2腔模总数与压缩阶数两者之积取奇数的情形

构造了由多模复共轭相干态的相反态|{-Z_j^(a)*}〉q和多模虚共轭相干态的相反态|{-Z_j^(b)*}〉q的线性叠加所组成的第Ⅱ种强度不等的非对称两态叠加多模叠加态光场|Ψ(ab)Ⅱ〉q.利用多模压缩态理论,研究了态|Ψ(ab)Ⅱ〉q的等阶N次方H压缩特性.结果发现,在腔模总数与压缩阶数这两者之积取奇数,则当各对应模的初始相位、各对应模的初始相位差,态间的初始相位差、以及光子干涉项的幅度之和等分别满足一定的量子化条件时,态|Ψ(ab)Ⅱ〉q的第一及第二正交分量可分别呈现出周期性变化的等阶N次方H压缩效应.这与现有报道的结果截然不同.     

Generalized Uncertainty Principle and Black Hole Entropy of Higher-Dimensional de Sitter Spacetime

Recently, there has been much attention devoted to resolving the quantum corrections to the Bekenstein-- Hawking black hole entropy. In particular, many researchers have expressed a vested interest in the coetticient of the logarithmic term of the black hole entropy correction term. In this paper, we calculate the correction value of the black hole entropy by utilizing the generalized uncertainty prlnciple and obtain the correction term caused by the generalized uncertainty principle. Because in our calculation we think that the Bekenstein-Hawking area theorem is still valid after considering the generalized uncertainty principle, we derive that the coefficient of the logarithmic term of the black hole entropy correction term is positive. This result is different from the known result at present. Our method is valid not only for four-dimensional spacetimes but also for higher-dimensional spacetimes. In the whole process, the physics idea is clear and calculation is simple. It offers a new way for studying the entropy correction of the complicated spacetime.     

气相色谱法测定人体血液中乙醇含量的测量不确定度评定

对气相色谱法测定人体血液中乙醇含量的测量不确定度进行评定。人体血液中乙醇含量测量结果的测量不确定度主要来源于相对定量校正因子、检材量、检材中添加内标物叔丁醇的体积、检材中乙醇峰面积的平均值与添加内标物叔丁醇峰面积的平均值之比、无水乙醇的纯度及密度等参数引起的不确定度。当检材中乙醇的含量为0．915mg／mL时,扩展不确定度为O．030mg/mL(k=2)。     

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Because of its ease of implementation and minimum requirements about the primary signals' information, energy detection is broadly considered for signal detection in spectrum sensing algorithms. However, the noise uncertainty phenomenon, caused by the random variations in the noise power, degrades the performance of an energy detector, particularly when the signal‐to‐noise ratio (SNR) is low. In this work, we propose to reduce the negative effects of the noise uncertainty in the performance of an energy detector by dynamically adapting its detection threshold to the noise conditions experienced at each sensing epoch. The noise power is estimated from the received signal samples using an algorithm based on a high‐pass filters bank and median filtering. With our proposal, it is possible to maintain a constant and low false alarm rate in the presence of noise uncertainty, without increasing the probability of misdetection, even in the low SNR regime, and without increasing the number of samples considered for spectrum sensing. Copyright © 2014 John Wiley & Sons, Ltd.     

大学物理实验数据处理系统

本文用C#编写了“大学物理实验数据处理系统”软件,该软件包含：坏值的剔除、A类和B类不确定度的计算、实验结果的综合评定、曲线的拟合及显示等。该软件有助于学生处理物理实验数据,加深对实验的理解。     

电磁骚扰测量不确定度的评定

简要介绍了测量不确定度,阐述了不确定度A类评定和B类评定的方法和要素,以《测量不确定度的评定与表示（JJF1059-1999）》、《电磁干扰测量中不确定度的评定指南（CNAS-GL07：2006）》等文献为指导,识别出影响电磁骚扰测量结果不确定的输入量,建立评定模型,提出了辐射骚扰电场强度测量、电源端口传导骚扰测量、骚扰功率测量等电磁骚扰测量的不确定度评定方法和步骤,为电磁兼容检测实验室的测量不确定度分析提供了参考。     

光波长计量标准的建立

通过对光波长测量原理及企业实际需求进行分析，建立光波长标准，确定测试项目及方法，并对光波长标准的不确定度进行评定和验证。     

基于图卷积的视网膜血管轮廓及高不确定度区域细化框架

针对传统卷积神经网络(convolutional neural network, CNN)受感受野大小的限制,无法直接有效地获取空间结构及全局语义等关键信息,导致宽血管边界及毛细血管区域特征提取困难,造成视网膜血管分割表现不佳的问题,提出一种基于图卷积的视网膜血管分割细化框架。该框架通过轮廓提取及不确定分析方法,选取CNN粗分割结果中潜在的误分割区域,并结合其提取的特征信息构造出合适的图数据,送入残差图卷积网络(residual graph convolutional network, Res-GCN)二次分类,得到视网膜血管细化分割结果。该框架可以作为一个即插即用模块接入任意视网膜血管分割网络的末端,具有高移植性和易用性的特点。实验分别选用U型网络(U-neural network, U-Net)及其代表性改进网络DenseU-Net和AttU-Net作为基准网络,在DRIVE、STARE和CHASEDB1数据集上进行测试,本文框架的Sp分别为98.28%、99.10%和99.04%,Pr分别为87.97%、88.87%和90.25%,证明其具有提升基准网络分割效果的细化能力。