网络入侵检测的快速规则匹配算法

在分析入侵检测系统的基础上,指出现有规则匹配算法的不足.提出一种新的规则匹配算法,该算法主要利用非精确匹配技术,缩小入侵分类的检测范围,达到快速匹配的目的.根据不同的安全性要求设置不同的门限值,该算法可用于预测适合不同门限值的可疑入侵行为.     

FDTD方法对有孔屏蔽腔的电磁特性分析

介绍了研究有孔腔体电磁特性的一种算法：时域有限差分法（FDTD）,并用FDTD方法对带孔缝机箱的电磁屏蔽特性进行了仿真分析,从而验证该方法对有孔矩形腔屏蔽效能计算的有效性和可靠性。     

基于非参估计的局部滑窗双参CFAR目标检测

针对特定杂波概率模型不能有效的描述SAR图像背景杂波这一问题,提出了一种基于非参估计的局部滑窗双参CFAR目标检测算法。该方法首先用非参估计方法逼近SAR图像局部背景,完成对局部背景的精确建模;在此基础上,理论推导了局部双参CFAR检测算法的阈值,设计了阈值求解的数值算法。对典型目标图像进行实验,结果表明,该方法检测速度较快、精度较高。     

坦克外壳对内部电磁波的影响及其振荡干扰波型的数值计算

在坦克的金属立方体盒子模型下,分析了坦克外壳对内部电磁波的影响,并对Lc型电路激发的电磁波在坦克内部形成的振荡波型进行了计算,分析了该类电磁振荡波对坦克内部其他电子元件产生电磁干扰的作用机理,对一种最低频率的谐振波型进行了仿真模拟,提供了对坦克内部电磁波电场分量及磁场分量的一种分析计算方法,为研究坦克内部电子设备的电磁干扰规律提供了思路,对提高坦克的电磁兼容性能具有一定参考意义。     

污水中重金属离子浓度软测量的BP算法

在重金属离子检测仪器及其工作原理研究的基础上,对污水中重金属离子浓度测量方法进行了分析研究,分析了采用软测量技术的可行性和必要性,针对水质测量具有非线性、大时变和多滞后的特点,提出了基于BP神经网络的污水中重金属离子浓度软测量建模方法,建立了软测量模型,并通过仿真分析和实验验证了该方法的实用性.     

基于遗传算法的跳频通信侦察技术

为了识别跳频序列,提出了采用遗传算法并结合跳频信号特点来完成跳频序列的分析与重组,从而实现跳频信号的侦察。在采用传统遗传算法的基础上,针对序列特点提出了5种改进手段,从而大大提高了算法的效率和可用性。最后利用采样的语音信号,对分析与重组过程进行模拟来验证算法的可行性。     

复杂电磁环境下电子系统仿生防护模式研究

阐述电磁防护工作中仿生研究所需要涉及的学科类型和基本内容,对所做工作在整个仿生领域中进行大致的定位;提出生物一电子研究的对等模型、总体构成和实现基础,细化具体研究的方向、设想和防护模式,并从原型实现的工程角度介绍实施策略,使得复杂电磁干扰环境下控制系统板卡及芯片级防护的仿生构想在技术上成为可能。     

融合神经网络与超像素的候选区域优化算法

为解决目标检测中候选区域召回率低的问题,提出融合神经网络与超像素的目标候选区域算法。该算法利用神经网络提取更能清楚表达目标边界的特征,并使用聚类、相似性等策略,计算每个滑动窗口所含有的边缘信息量;将待测图像使用简单线性迭代聚类算法分割成若干个超像素,并利用超像素的空间位置、完整性、相邻超像素间的对比度信息,计算各个超像素的显著性得分及每个滑动窗口的显著性得分;根据每个滑动窗口的边缘信息及显著性得分筛选滑动窗口。在PASCAL VOC 2007测试集上进行对比实验,其实验结果表明:所述算法能够快速产生定位质量高的候选区域。     

Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

In order to improve the infrared detection and discrimination ability of the smart munition to the dy-namic armor target under the complex background, the multi-line array infrared detection system is established based on the combination of the single unit infrared detector. The surface dimension features of ground armored targets are identified by size calculating solution algorithm. The signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored target. According to the characteristics of the target signal, a custom threshold de-noising function is proposed to solve the problem of signal preprocessing. The multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning stage. The method solves the disadvan-tages of wide scanning interval and low detection probability of single unit infrared detection. By reducing the scanning interval, the number of random rendezvous in the infrared feature area of the upper surface is increased, the accuracy of the size calculating is guaranteed. The experiments results show that in the fuzzy recognition method, the size calculating is introduced as the feature operator, which can improve the recognition ability of the ground armor target with different shape size.     

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.