双模压缩真空态与玻色-爱因斯坦凝聚相互作用系统中的量子纠缠

研究的系统为双模压缩真空态和玻色-爱因斯坦凝聚体相互作用体系,在双光子跃迁过程中,分别用量子约化熵和量子相对熵研究了该系统中的双模压缩真空态与凝聚体间的纠缠以及双模压缩真空态的模间纠缠,分析了光场初始压缩因子、原子间相互作用对场-凝聚体间纠缠和模间纠缠的影响.     

基于NSCT及熵的旋转不变彩色图像检索算法

为了解决图像在转载过程中所产生的旋转变化和尺 度变化对检索的影响,根据熵的对称性,提出了基于NSCT及熵的旋转不变图像检索算法。首 先,利用非下采样轮廓波变换(NSCT)对图像进行多尺度、多方向分解,对不同尺度、同方 向的高频方向子带求多尺度积,以减小尺度变 化和噪声对检索效率的影响;然后,考虑到图像旋转后各方向子带在整幅图像中的能量比例 不会发生变化, 将各方向子带的能量比例作为概率矢量,各方向子带的粗糙度作为权值求取图像的加权信息 熵,作为具 有旋转不变性的图像纹理特征,利用矩提取图像的颜色和形状特征;最后,归一化3种特征 来比较两幅图 像的相似性。性能测试表明,本文所提出的方法对旋转变换鲁棒性强,且具有很高的查准率 和查全率。     

基于量子最大熵多阈值算法的图像分割研究

针对图像分割中的问题,采取量子多阈值最大熵算法.首先确定图像信息最大熵的概率密度函数,求解出熵最大化验前密度;接着把图像用灰度值分成背景和物体两个区域,统计不同的灰度等级内像素的量子比特值,由所占的比例得出像素点的最佳分割阈值;最后给出了算法步骤.实验仿真给出了不同算法的图像分割结果,本文算法对噪声的抑制能力较强,得到较高精度的图像,并且保留了边缘的重要信息.     

重点雷达电子目标的快速靶向识别

针对现有雷达对抗侦察系统对特定雷达电子目标识别针对性不强的问题,提出一种重点雷达电子目标快速靶向识别方法,并采用并行处理流程,对多目标同时进行处理。首先,利用雷达电子目标的先验信息完成小样本脉冲序列建模;其次,实现了基于小样本脉冲序列快速匹配出雷达电子目标的算法,该方法绕过传统雷达信号的分选环节,不仅对重点雷达信号的识别效果较好,而且处理速度得到有效的提高;最后,进行了计算机仿真验证。理论分析与仿真结果均表明:以快速靶向为目的识别算法,能够有效克服现有雷达辐射源识别方法针对性不强和处理流程僵化的缺陷。     

Practical evaluation of encrypted traffic classification based on a combined method of entropy estimation and neural networks

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet’s sizes, packet's inter‐arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.     

移动式电子系统总体设计与电气布局

介绍了电子设备方舱基本总体设计流程及其相关的基本原则．论述了电气总体设计与电源选择、电磁兼容以及电缆布线设计、布线施工一致性的关系．提出了电子设备方舱总体设计的基本方法,并举例说明通过电缆布线设计解决电磁兼容问题的方法．     

Metric and classification model for privacy data based on Shannon information entropy and BP neural network

Aiming at the requirements of privacy metric and classification for the difficulty of private data identification in current network environment, a privacy data metric and classification model based on Shannon information entropy and BP neural network was proposed. The model establishes two layers of privacy metrics from three dimensions. Based on the dataset itself, Shannon information entropy was used to weight the secondary privacy elements, and the privacy of each record in the dataset under the first-level privacy metrics was calculated. The trained BP neural network was used to output the classification result of privacy data without pre-determining the metric weight. Experiments show that the model can measure and classify private data with low false rate and small misjudged deviation.     

Application of entropy formulas in detection of denial‐of‐service attacks

The paper compares five entropy formulas (Shannon, Tsallis, Rényi, Bhatia‐Singh, and Ubriaco) and their application in the detection of distributed denial‐of‐service (DDoS) attacks. The Shannon formula has been used extensively for this purpose for more than a decade. The use of the Tsallis and Rényi formulas in this context has also been proposed. Bhatia‐Singh entropy is a novel information metric with promising results in initial applications in this area. Ubriaco proposed an entropy function based on the fractional calculus. In this paper, flow size distribution was used as the input for detection. The type of DDoS attack is SYN flood, and simulation was used to obtain the input dataset. The results show that the Rényi and Bhatia‐Singh detectors perform better than the rest. Rényi and Tsallis performed similarly with respect to the true positive rate, but Rényi had a much lower false positive rate. The Bhatia‐Singh detector had the best true positive rate but a higher false positive rate than Rényi. The Ubriaco detector performed similar to the Shannon detector. With respect to detection delay, Tsallis, Ubriaco, and Shannon produced similar results, with a slight advantage associated with the Ubriaco detector, while Rényi and Bhatia‐Singh had a larger detection delay than the former three.     

一种基于麦克风阵列的声源定位算法研究

麦克风阵列声源定位广泛应用于视音频会议系统及枪声定位系统等领域。提出了一种基于最小熵值（ME）的麦克风阵列声源定位新方法,其特点在于利用最小熵值方法对麦克风阵列进行时延估计,并与离散网格方法相结合,对声源进行空间搜索。实验结果表明,在同等混响或噪声条件下,该方法定位优于广义互相关-相位变换方法（GCC-PHAT）。     

粗糙集理论中一种连续属性离散化算法

连续属性离散化一直是机器学习领域中亟待解决的关键问题之一。提出一种基于断点重要性的离散化算法。首先给出粗糙集理论的几个基本概念:决策表、不可分辨关系、信息熵和条件熵,然后对离散化问题进行介绍,给出断点分类的条件熵定义,在此基础上给出了断点选择的粗糙集连续属性离散化算法。仿真结果表明,算法的综合性能优越于文献报道的同类算法。