Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.     

基于点对群网络反馈机制的恶意代码传播模型

在点对群信息共享网络中,群体成员之间交流频繁并且同一群体内的成员可以同时从信息源接收到相同信息,依据点对群网络的这2个特点,考虑从恶意代码感染中恢复后的节点作用,在点对群网络中建立一种具有动态反馈防治信息功能的易感-感染-反馈-免疫(SIFR)模型。在经典易感-感染-免疫(SIR)传播模型的基础上引入反馈节点,通过动态共享防治信息遏制恶意代码在点对群网络中的传播。根据计算得到SIFR模型的平衡点和传播阈值,构建相应的Lyapunov函数,证明了平衡点的局部和全局稳定性。数值模拟实验结果显示：当反馈率取0.000 1时,SIFR模型相较于经典SIR模型在传播阈值小于1的情况下,感染节点在峰值处的数量降低了36.16%,能更早更快地趋近于0;当传播阈值大于1时,同一时间的感染节点数量有所减少,趋于稳定的感染节点数量降低了80%。上述实验结果表明SIFR模型应用在点对群网络中能够更好地遏制恶意代码的传播,且反馈率越高,遏制效果越好。     

基于字符分割和LeNet-5网络的字符验证码识别

为了解决传统验证码识别方法效率低,精度差的问题,设计了一种先分割后识别的验证码处理方案。该方案在预处理阶段用中值滤波去噪,再利用霍夫变换对图像字符进行矫正;在字符分割阶段,利用垂直投影算法确定验证码字符块个数,以及字符坐标点,再用颜色填充算法对验证码进行初步分割,根据分割后的字符块数量对粘连字符进行二次分割;在识别阶段,我们对LeNet-5网络进行了改进,修改了输入层,并用全连接层替换了LeNet-5网络中的C5层,以此来对验证码字符进行识别;实验表明,对于非粘连验证码和粘连验证码,单张图片分割时间为0.14和0.15ms,分割准确率为98.75%和97.25%,识别准确率为99.99%和97.7%;结果表明,该算法对验证码分割和识别都有着很好的效果。     

基于改进Yolov5s的无人机火灾图像检测算法

为了解决现有火灾检测算法模型复杂,实时性差,难以部署在无人机平台的问题,通过改进Yolov5s算法对无人机火灾图像目标检测进行分析研究。利用搭载高清摄像头的无人机设备获取的火灾图像、公开数据集、互联网航拍视频自主建立无人机火灾图像数据集;采用轻量化模型Yolov5s为基础模型,MobileNetV3作为特征提取主干网络,降低模型参数和计算量,解决实时性差和模型部署的问题;模型颈部引入注意力模块CBAM,综合了通道和空间信息,加强网络对高层次语义信息的传递;修改模型检测头部结构,增强小目标检测能力。通过消融试验对比分析各个模块对模型的影响,与常见火灾模型进行对比分析,分析本文算法的优劣。算法在自建数据上的平均精度达到78.2%,模型大小为6.7M,单帧（640×640）图像处理时间为15.2ms。实验结果表明,本文算法模型简单、实时性好,为火灾检测算法部署在无人机平台奠定技术基础。     

基于YOLOv5和生成对抗网络的塑料标签缺陷检测

塑料标签物的缺陷检测与识别是工业过程控制和质量控制的关键;为了克服现有塑料标签缺陷检测方法的局限性,使用了单阶段目标检测模型YOLOv5对其瑕疵进行实时检测与分类;此外,为解决由于样本缺陷数量不足造成的模型识别准确率低等问题,采用了一种基于Defect-GAN的生成对抗网络对小样本进行数据增强和扩增;该方法通过模拟缺陷生成和缺陷图像重建的过程,可以高效合成大量具有高保真度和多样性的缺陷样本,尤其适用于形状不规则、分布随机且尺寸不同的瑕疵生成;实验结果表明,通过使用扩增数据集训练目标检测器,并对网络的超参数进行优化,可以显著提高目标检测器的准确率和精度,其平均精度mAP可达99.5%;此外,为了模拟该方法在实际生产中的应用场景,设计并定制了一台半自动的图像采集机械平台用于采集圆柱样品表面的印刷标签,以及一个自主开发的图像处理和统计分析软件用于样本采集、图像处理及统计分析;该方法和平台可以很容易地推广并应用到其他工业质量控制和缺陷检测系统中。     

基于PP-PicoDet技术的智能垃圾分类

目前在垃圾分类目标检测上大多采用YOLOv5系列算法,该算法在相同参数量的情况下,检测精度和检测速度都相对不太高,难以满足实际应用需求。论文对基于PP-PicoDet技术的垃圾分类目标检测应用进行了研究,并将其与几种常见的垃圾分类目标检测算法进行实验对比分析;结果表明,PP-PicoDet算法能够在使用更少的参数量的情况下,实现较高的检测精度和速度,能够满足移动端部署需求。     

改进YOLOv5的瓷砖表面缺陷检测

现有瓷砖表面缺陷检测存在识别微小目标缺陷能力不足、检测速度有待提升的问题, 为此本文提出了基于改进YOLOv5的瓷砖表面缺陷检测方法. 首先, 由于瓷砖表面缺陷尺寸偏小的特性, 对比分析YOLOv5s的3个目标检测头分支的检测能力, 发现删除大目标检测头, 只保留中目标检测头和小目标检测头的模型检测效果最佳. 其次, 为了进一步实现模型轻量化, 使用ghost convolution和C3Ghost模块替换YOLOv5s在Backbone网络中的普通卷积和C3模块, 减少模型参数量和计算量. 最后, 在YOLOv5s的Backbone和Neck网络末端添加coordinate attention注意力机制模块, 解决原模型无注意力偏好的问题. 该方法在天池瓷砖瑕疵检测数据集上进行实验, 实验结果表明: 改进后的检测模型的平均精度均值达66%, 相比于原YOLOv5s模型提升了1.8%; 且模型大小只有10.14 MB, 参数量相比于原模型减少了48.7%, 计算量减少了38.7%.     

基于YOLOv5的交通标志识别

交通标志识别是自动驾驶技术中的关键一部分.针对交通标志在道路场景中目标较小且识别精度较低的问题,提出一种改进的YOLOv5算法.首先在YOLOv5模型中引入全局注意力机制(GAM),提高网络捕获不同尺度交通标志特征的能力;其次将YOLOv5算法中使用的GIoU损失函数更换为更具回归特性的CIoU损失函数来优化模型,提高对交通标志的识别精度.最后在Tsinghua-Tencent 100K数据集上进行训练,实验结果表明,改进后的YOLOv5算法对交通标志识别的平均精度均值为93.00%,相比于原算法提升了5.72%,具有更好的识别性能.     

YOLOv5-SFE: An algorithm fusing spatio-temporal features for detecting and recognizing workers' operating behaviors

The occurrence of production accidents can be effectively reduced by monitoring workers' operating behaviors in real time. However, most of the monitoring tasks are currently performed by the monitoring personnel, which takes up a lot of manpower and material resources. To solve this problem, a YOLOv5-SFE algorithm is proposed in this paper for real-time detection and recognition of workers' operating behaviors. The YOLOv5-SFE algorithm makes the following contributions: (1) During data preprocessing, a hash sampling algorithm is used to extract frames with low similarity. (2) A feature enhancement module is designed and integrated into YOLOv5 to distinguish between valid and invalid information. (3) A convolution-based spatio-temporal feature fusion module is designed and is inserted after the extraction of spatial features to extract the temporal features between multiple frames. The videos of workers' operating behaviors are from factories’ industrial scene. The improved algorithm in this paper was trained and tested on the dataset. Compared with the original algorithm, the accuracy of the algorithm improves from 89.3% to 94.7%, the recall improves from 81.5% to 90.8%, and the mean average precision(mAP) improves from 88.2% to 92%. The results show that the improved algorithm is able to accurately detect and recognize workers' operating behaviors in real time, thereby improving the safety of the production process.     

THz antennas design,developments, challenges,and applications: A review

Terahertz (0.1–10 THz) wireless communication will be the future technology to reach a top-notch data rate. THz is one of the most promising candidates for 6G systems because it provides enormous bandwidth, up to 100 GHz, and a massive data rate of up to 1 Tbps. THz antennas, antenna arrays, and MIMO antenna arrays in 6G are hot research topics for implementing 6G wireless communication systems. The 6G aims to continue to enhance the features of the 5G as it is capable of achieving the maximum high-speed data rate, excellent reliable communication, massive connectivity, and very low latency connectivity. The 6G requirements need high-gain antenna arrays and MIMO antenna arrays to combat the effect of atmospheric losses in high frequencies. An in-depth discussion of the planar THz antennas that have been extensively used in THz applications like imaging, sensing, and Internet-of-Things (IoT) has been conducted. The study of the THz antennas, antenna arrays, and MIMO antennas on different conducting materials such as copper and graphene, which are designed on different dielectric substrates such as polyimide, quartz, liquid crystalline polymer, and polytetrafluoroethylene, has been carried out in detail. Metamaterial, photoconductive, plasmonic antennas, and THz beamforming are significant parts of THz communications. This paper also provides antennas and antenna arrays based on them.