Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

基于深度学习和灰度纹理特征的铁路接触网绝缘子状态检测

铁路接触网绝缘子状态检测对铁路行车安全有着 重大的意义,为解决目前人工对绝缘 子图像检测结果的不确定性,提出一种深度学习结合灰度纹理特征的检测方法。首先使用 Faster R-CNN (faster region-based convolutional neural network)目标检测算法对图像中绝缘子精确识别,再通过灰度共生矩阵对绝缘子纹理 特征进行分析提取,之后结合支持向量机将绝缘子分为正常绝缘子和异常绝缘子,实验数 据结果证明使用能量、熵、相关度3种纹理特征进行绝缘子状态分类时对实验数据中的正 常状态绝缘子的分类精度可达100%,异常状态绝缘子的分类精度达97.5%,最后依据绝缘 子图像灰度分布的周期性特点,利用灰度积分投影将异常绝缘子分为破损绝缘子和夹杂异 物绝缘子。实验结果表明所提方法可以有效对绝缘子状态进行检测分类。     

编码协作系统准循环重复累积码的联合设计与性能分析

重复累积(RA)码是一种特殊结构的低密度奇偶校验(LDPC)码,不仅具有LDPC码的优点,还能实现差分编码。针对LDPC编码协作系统编码复杂度高、时延长的问题,该文引入准循环RA(QC-RA)码,推导出信源节点和中继节点采用的QC-RA码对应的联合校验矩阵,基于公差构造方法设计该联合校验矩阵,并证明该方法设计的联合校验矩阵不存在围长为girth-4, girth-6的短环。理论分析和仿真结果表明,同等条件下该系统比相应点对点系统具有更优异的误码率性能。仿真结果同时表明,与采用一般构造QC-RA码或基于Z型构造QC-RA码相比,采用基于公差构造的联合设计QC-RA码的多信源多中继协作均可获得更高的编码增益。     

Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.     

Spectral-Null Codes and Null Spaces of Hadamard Submatrices

Codes of length 2 ^m over {1, -1} are defined as null spaces of certain submatrices of Hadamard matrices. It is shown that the codewords of all have an rth order spectral null at zero frequency. Establishing the connection between and the parity-check matrix of Reed-Muller codes, the minimum distance of is obtained along with upper bounds on the redundancy of . An efficient algorithm is presented for encoding unconstrained binary sequences into .     

二维同步OCDMA系统的性能分析

构造了一种用于二维同步光码分多址(OCDMA)系统的修正素数跳频码(MPC/PC),分析了码字的自相关和互相关性能,研究了二维同步OCDMA系统的误码率和吞吐量性能.结果表明,与一维同步OCDMA系统相比,二维同步OCDMA系统的可接入用户数大大增加,误码率大大降低,吞吐量明显提高.     

GF(2p)LDPC编码的相位调制OFDM系统在瑞利衰落信道的性能

OFDM是一项能有效对抗高速无线通信中多径衰落的关键技术，为了进一步提高OFDM系统的误码性能，许多信道编码技术已被应用于OFDM系统中，二元域LDPC码以其近香农限的误码性能和较低的译码复杂度成为研究的热点。在AWGN信道下，多元域LDPC码比等效码长的二元域LDPC码有更好的纠错性能。本文提出了一种将多元域LDPC码经过MPSK调制后用于OFDM系统的新方法。仿真结果表明，在多径衰落信道下，通过合理选择多元LDPC码域的阶数和调制的方法，多元域LDPC编码的高阶调制OFDM系统比等效码长的二元域LDPC编码OFDM系统具有更好的性能，并且由于采用了多元域LDPC的快速BP译码，译码复杂度只是稍有增加。     

分层空时码检测算法的研究

分层空时码技术是提高无线信道传输速率的一种十分有效的方法.迫零检测算法和最小均方误差检测算法是分层空时码体制中经常使用的两种检测算法,它们都使用了通常的线性合并置零技术,因此要求接收天线数不小于发射天线数,即要求在接收机上安装较多的天线,从而限制了分层空时码在移动环境下的应用.本文引入分层空时码的最大似然检测算法,突破了前两种算法对接收天线数的限制,并分别针对单路径和多径衰落信道环境,对分层空时码的三种检测算法的性能进行了仿真比较和分析,从而提出了它们各自适合的应用环境.     

Efficient algorithm for calculating short cycles in Tanner graph based on matrix computation

Loop distribution of Tanner graph affects the BER performance of low-density parity-check codes(LDPC) decoding.To count short cycles in the Tanner graph efficiently,a side by side recursion algorithm based on matrix computation was proposed.Firstly,5 basic graph structures were defined to realize recursive calculate in the implementation process.Compared with previous works,the algorithm provided many methods for counting the same length of cycles.The same result confirmed the correctness of the algorithm.The new algorithm could not only calculate the total number of cycles,but also gave the number each edge participating in fixed-length cycles.Its complexity was proportional to the product of D and square of N,where D was the average degree of variable nodes,and N denoted the code length.For LDPC codes,D was far less than N.For most of the LDPC codes,the calculation for numbers of cycle-length g、g+2、g+4 was only several seconds.     

Boolean operations on rasterized shapes represented by chain codes using space filling curves

This paper introduces a new algorithm for Boolean operations on rasterized geometric shapes that are represented with chain codes. The algorithm works in three steps. Firstly, the chain code symbols are transformed in the Hilbert space, where the overlaid chain code symbols are recognised. After that, a suitable starting cell is determined. Finally, the walk-about through the sequence of the initial chain code symbols is performed to obtain the sequence of chain code symbols representing the shape of the required Boolean operation. The algorithm is demonstrated on Freeman chain code in four directions. The time and space complexity of the proposed algorithm is linear, which was proven theoretically and confirmed by experiments.