Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

Improvement Detecting Method of Optical Axes Parallelism of Shipboard Photoelectrical Theodolite Based on Image Processing

An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.

Detecting XSS with Random Forest and Multi-Channel Feature Extraction

In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.

Building Real-Time Network Intrusion Detection System Based on Parallel Time-Series Mining Techniques

A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.

Anti-D Chain:A Lightweight DDoS Attack Detection Scheme Based on Heterogeneous Ensemble Learning in Blockchain

With rapid development of blockchain technology,blockchain and its security theory research and practical application have become crucial.At present,a new DDoS attack has arisen,and it is the DDoS attack in blockchain network.The attack is harmful for blockchain technology and many application scenarios.However,the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution.Aiming at the above problem,the paper proposes the virtual reality parallel anti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning.Here,Ada Boost and Random Forest are used as our ensemble learning strategy,and some different lightweight classifiers are integrated into the same ensemble learning algorithm,such as CART and ID3.Our detection framework in blockchain scene has much stronger generalization performance,universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network.Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators(such as Precision,Recall,F-Score,True Positive Rate,False Positive Rate,and ROC curve).

High photon detection efficiency InGaAs/InP single photon avalanche diode at 250 K

Planar semiconductor InGaAs/InP single photon avalanche diodes with high responsivity and low dark count rate are preferred single photon detectors in near-infrared communication.However,even with well-designed structures and well-con-trolled operational conditions,the performance of InGaAs/InP SPADs is limited by the inherent characteristics of avalanche pro-cess and the growth quality of InGaAs/InP materials.It is difficult to ensure high detection efficiency while the dark count rate is controlled within a certain range at present.In this paper,we fabricated a device with a thick InGaAs absorption region and an anti-reflection layer.The quantum efficiency of this device reaches 83.2%.We characterized the single-photon performance of the device by a quenching circuit consisting of parallel-balanced InGaAs/InP single photon detectors and single-period sinus-oidal pulse gating.The spike pulse caused by the capacitance effect of the device is eliminated by using the characteristics of parallel balanced common mode signal elimination,and the detection of small avalanche pulse amplitude signal is realized.The maximum detection efficiency is 55.4%with a dark count rate of 43.8 kHz and a noise equivalent power of 6.96×10^(−17 )W/Hz^(1/2) at 247 K.Compared with other reported detectors,this SPAD exhibits higher SPDE and lower noise-equivalent power at a higher cooling temperature.

Ping-pong effects study in PIC turbo joint detection for TDD CDMA

A kind of turbo joint detection scheme based on parallel interference cancellation （PIC） is studied; then, the eigenvalues of iteration matrix is deeply analyzed for studying the ping-pong effects in PIC JD and the corresponding compensation approach is introduced. Finally, the proposed algorithm is validated through computer simulation in TDD CDMA uplink transmission. The result shows that the ping-pong effects are almost avoided completely in the presence of the compensation scheme, and system performance is greatly improved.

A System of Signal Detection and Estimation for Ground Wave Over-The-Horizon Radar

Ground wave over-the-horizon radar(GW-OTHR) can detect the OTH moving targets on sea or at low altitude. This paper discusses the background for detecting a target with GW-OTHR, introduces the theory and implementation of the signal detection and estimation system which has the parallel processing function, and gives some experimental results. The results of GW-OTHR experiments show that this system can successfully detect and estimate the above-mentioned targets.

THE PARALLEL CONFOCAL DETECTING SYSTEM USING OPTICAL FIBER PLATE

Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whole-field confocal detecting, is proposed. Methods The optical fiber plate generates an 2D point light source array, which splits one light beam into N2 subbeams and act the role of pinholes as point source and point detecting to filter the stray light and reflect light. By introducing the construction and working principle of the multi-beam 3D detecting system, the feasibility is investigated. Results Experiment result indicates that the optical fiber technology is applicable in parallel confocal detecting. Conclusion The equipment needn't mechanical rotation. The measuring parameters that influence the detecting can easily be adapted to satisfy different requirments of measurement. Compared with the conventional confocal method, the parallel confocal detecting system using optical fiber plate is simple in the mechanism, the measuring field is larger and the speed is faster.

Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion

Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and speed.Therefore,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network framework.First,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the model.Moreover,we introduce the parallel attention mechanism into the encoding and decoding stages.The attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various noises.Finally,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion module.Our research results are validated on the self-made dataset.The experiments show that our method is more accurate than other methods.Its intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,respectively.In addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/s.The results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.

ERROR ANALYSIS OF 3D DETECTING SYSTEM BASED ON WHOLE-FIELD PARALLEL CONFOCAL MICROSCOPE

Compared with the traditional scanning confocal microscopy, the effect of various factors on characteristic in multi-beam parallel confocal system is discussed, the error factors in multi-beam parallel confocal system are analyzed. The factors influencing the characteristics of the multi-beam parallel confocal system are discussed. The construction and working principle of the non-scanning 3D detecting system is introduced, and some experiment results prove the effect of various factors on the detecting system.

Soft-PIC multiuser detection in MC-CDMA uplink system

It is necessary for an MC-CDMA uplink receiver to employ MUD (multiuser detection) in a frequency selective fading channel. After analyzing the algorithm of PIC(parallel interference cancellation) MUD, a novel MUD scheme, Soft-PIC (soft parallel interference cancellation) is proposed. Based on the reliability of each detected user signal in the former stage, this Soft-PIC detection scheme substitutes a soft decision of the variable for the hard decision in PIC scheme. Compared with the PIC scheme, it can reconstruct the interference signals more accurately and eliminate MAI(multiple access interference) in a more efficient way.PIC is one of the most practical schemes in numerous multiuser detection technologies. However, Soft-PIC as an improved PIC scheme deserves further study.

基于平行感知卸载区边缘工程结构规范性检测

当前露天矿无人驾驶技术在卸载阶段面临的主要挑战是安全隐患,尤其是在卸载区边缘工程结构的稳定性与规范性检测方面。为应对这一挑战,提出了一种平行感知理论驱动的点云模型解析AC-VIT算法,旨在实时稳定地检测露天煤矿卸载区边缘工程结构的稳定性与规范性。首先使用无人矿卡后向激光雷达扫描得到三维点云数据,随后应用网格平均方法、统计滤波法以及映射离散网格模型对点云数据进行处理,进而通过高度场梯度特征提取进行初步地形标记,并结合改进的AC-VIT神经网络进行规范性的识别与分类,AC-VIT模型通过全基于自注意力机制的平行计算和多层级的注意力机制,能有效捕捉长距离依赖关系。此外,在人工场景仿真环境中,基于内蒙古哈尔乌素露天煤矿实际生产作业环境搭建卸载区平行仿真环境,以获得大量多样性人工场景数据,在此基础上结合实际场景数据,利用提出的算法平行执行,进行平行感知计算实验的设计和实施,完成有效的检测算法训练和科学评估。实验结果表明,平行感知理论驱动的点云模型解析AC-VIT算法在准确率方面达到了98%,较传统神经网络模型的准确率与效率有所提高。此外,AC-VIT算法的成功应用不仅增强了露天矿卸载作业的智能化水平,还为其他类似的工程结构安全检测提供了有力的技术支撑。

M-APSK鉴相算法与并行载波同步方法

为实现M进制幅相调制(M-APSK)体制下高阶调制信号的相位精细校正,将DVB-S2标准推荐的16APSK和32APSK的Q次方无数据辅助鉴相算法进行了扩展,以应用于64APSK、128APSK和256APSK等高阶调制。针对高阶调制的有效鉴相星座点占比较低时环路工作不稳定的问题提出了改进算法,通过对功率归一化后接收符号的幅值进行阈值判决,仅在高于阈值时进行鉴相,低于阈值时则不改变滤波器状态和相位补偿值,以提高星座点的鉴相有效性和可靠性,从而降低入锁门限。针对高速数传的符号速率非常高,而处理器的工作时钟频率相对较低的问题,提出了一种适用于M-APSK的并行载波同步方法,可以满足接收机工作时钟处理需要。相对于传统固定编码调制(CCM)的载波同步环路,该并行方法还可应用于可变编码调制(VCM)体制的频率跟踪。

基于云计算的舰船通信网络入侵检测方法研究

为在面临大规模网络攻击或突发攻击时,提高入侵检测的实时性,提出基于云计算的舰船通信网络入侵检测方法。通过云计算的MapReduce编程模型,设计MapReduce并行化的遗传量子粒子群优化算法,在舰船通信网络数据内,提取网络入侵特征;利用MapReduce并行化熵聚类算法,确定径向基函数神经网络的基函数中心;确定基函数中心后,在MapReduce编程模型的Map函数内,输入网络入侵特征样本,训练神经网络,优化神经网络权值,通过Reduce函数输出训练结束指示,完成神经网络训练;在完成训练的MapReduce并行化径向基函数神经网络内,输入特征样本,输出舰船通信网络入侵检测结果。实验证明,该方法可有效提取舰船通信网络入侵特征;在不同网络攻击类型下,该方法均可精准完成舰船通信网络入侵检测。

并行特征提取和渐进特征融合的计算机主板装配缺陷检测

针对计算机主板装配缺陷检测中的元器件位置分布复杂、缺陷目标不显著及多尺度等问题,本文提出了一种并行特征提取和互交叉渐进特征融合的端到端的缺陷检测算法。首先,结合部分卷积和视觉Transformer提出了一种并行残差特征提取网络,利用部分卷积的低计算复杂度的优势提取局部特征,同时利用视觉Transformer的长距离建模能力扩大模型的感受野,增强网络的特征提取能力。其次,引入注意力机制和特征渐进融合机制,提出了一种多尺度注意力互交叉的渐进特征融合网络,增强检测模型的特征融合能力。在公开数据集上的实验结果表明,该算法的平均精度均值(mAP)达到了94.63%,相较于基线模型YOLOv5提升了4.62%,并优于其他几种先进模型,检测速度达到了25 FPS。实现了较好的检测精度与速度的平衡,为实际工业环境下计算机主板表面装配缺陷检测自动化和智能化的实现提供了一种快速、有效的方法。

细长竹木圆棒双激光对射自动检测系统设计

针对细长竹木圆棒胚体存在的表面凹坑、开裂等外观缺陷,设计了一种双激光对射式自动检测系统。机械结构采用载物链条使细长竹木圆棒整体平移并在弹性压板下滚动,由对称布置的双激光斜入射至压板和圆棒贴合部位,实现对竹木圆棒表面的双螺旋扫描和空隙检测;控制部分以西门子S7-200 Smart PLC为主体,设计了与实体链条同步的数字链条进行并行控制。测试结果表明,系统相对于人工检测漏检率为0,具有成本低、效率高、可靠性高、易于使用等特点,可有效解决细长竹木圆棒胚体表面质量初筛问题。

基于PHAM-YOLO网络的卷烟纸燃烧线检测方法

为实现卷烟纸燃烧时燃烧线的准确识别,构建了常见应用场景下的卷烟纸燃烧线数据集。针对检测背景复杂、多目标、燃烧线尺度不一且形状各异的难题,将并行混合注意机制嵌入了YOLO v5主干网络,构建了PHAM-YOLO网络模型用于卷烟纸燃烧线的检测。采用特征金字塔快速池化、边界盒回归等方法提升了卷烟纸燃烧线的定位准确性。结果表明,对于卷烟纸燃烧线数据集,PHAM-YOLO网络检测平均精度均值、精度和召回率分别为99.0%、99.8%和99.0%,其中平均精度均值比原始模型提高了5.0%,高于其他类型的目标检测方法。

平行缝焊微电子器件的光学检漏

光学检漏为非接触式封装漏率测量方法,且单次测试可同时完成粗检及细检,是一种快速的封装检漏技术。对光学检漏与氦质谱检漏的漏率计算公式进行了详细分析和对比。设计了多组实验探究如工装翘曲度、封装内空腔体积等因素对光学检漏检测结果的影响,分析了光学检漏的“充压”问题并给出解决方案,对比了光学检漏与氦质谱检漏的检测漏率。分析了光学检漏技术的优缺点,并通过实验验证了光学检漏的可行性及可靠性,为微电子器件的光学检漏应用提供参考。