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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Liu, Longhui Gong, Wenhui Wang, Wenjie Zeng, Zhiqiang Li, Wei Xiamen Global Navigation Satellite System Development & Application Co. Ltd. Xiamen China School Of Computer And Information Engineering Xiamen University Of Technology Xiamen China

ISBN: (纸本)9798350361445

Traffic sign recognition is crucial in enabling intelligent driving. This paper proposes a robust hierarchical traffic sign image recognition algorithm that maintains local topology. The algorithm is divided into two matching levels: rough matching and fine matching. The multi-scale image's Oriented Fast and Rotated BRIEF (ORB) [1] are extracted in coarse matching. Hamming distance is adopted to measure the similarity of feature points to execute rough and fast matching. In precise matching, the matched candidate feature points are further filtered using the number of features of surrounding points, that is, the Shape Context [2] feature, to achieve precise matching. This hierarchical matching method maintains the local topological characteristics of the image while performing robust and fast matching. In the experiment, the data set provided by Changsha University of Science and Technology was used to assess the performance of the algorithm presented in this article. © 2024 IEEE.

关键词： Traffic signs

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A Novel Single Shot Multibox Detector based Face Mask Detection 2

A Novel Single Shot Multibox Detector based Face Mask Detect...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Vaishnav, Parul Goswami, Ankur Mds University School of Engineering & System Sciences Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Mds University Ajmer India Govt. Engineering College Department of Computer Applications Bikaner India

ISBN: (纸本)9798350325799

Face mask detection has gained significant importance in recent times due to the COVID-19 pandemic. This research work proposes a face mask detection method using the Single Shot MultiBox Detector (SSD) framework. The proposed method aims to accurately identify whether individuals are wearing masks or not in real-time scenarios. To begin with, a dataset comprising images of masked and unmasked faces is collected from kaggle and annotated with corresponding labels. The SSD model is then trained on this dataset, utilizing its ability to simultaneously perform object localization and classification. By leveraging the pre-trained weights and the architecture of SSD, the model is able to detect faces and classify them as either wearing masks or not. During the inference phase, the trained SSD model is applied to input images or video frames. The model analyzes the detected faces and assigns a label based on the presence or absence of masks. The output includes bounding box coordinates around the detected faces, along with the corresponding mask or non-mask label. Experimental evaluations are conducted on various real-world datasets to assess the performance of the proposed method. The results demonstrate the effectiveness and efficiency of the approach, achieving high accuracy in face mask detection. © 2023 IEEE.

关键词： Face recognition

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Design of Digital Twin Circuit Principle Experiment system Based on NCSLab 42

Design of Digital Twin Circuit Principle Experiment System B...

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42nd Chinese Control Conference, CCC 2023

作者： Li, Yu-Zhong Liu, Guo-Ping Yu, Yi School of Computer Science and Engineering Huizhou University Huizhou516007 China School of System Design and Intelligence Manufacture Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789887581543

This paper is concerned with the requirements of current distance learning of circuit principle experiment of automation specialty, automatic fault detection of experimental equipment, automatic fault isolation, uninterrupted experiment in case of fault, and maintaining the number of available equipment. Specifically, based on the networked control system laboratory (NCSLab) architecture, digital twin, remote control technology and circuit design, aiming at the circuit experiment principle course, the digital twin are realized by Simulink, and a digital twin circuit principle experiment system(DT-CPES) is designed and implemented. The designed system shows that the system can realize the sharing of experimental equipment, the digital twin can predict, and can also verify with the actual measurement data of the circuit experiment. Through the digital twin fault isolation controller(DT-FIC), the fault diagnosis, fault isolation and replacement output of the experimental equipment can be realized. Therefore, the developed system reduces the overall risk and cost of installation and maintenance of large-scale circuit experiments, and provides fault-tolerant redundancy for the experimental system with low computational burden and high reliability. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Timing circuits

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation system Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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A lightweight point cloud analysis network based on graph walking 8

A lightweight point cloud analysis network based on graph wa...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Wenju Ji, Qianqian Sha, Liye Cui, Liu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

Based on the irregularity and permutation invariance of point clouds, graph neural networks have been widely used in 3D point cloud analysis tasks in recent years. However, these works suffer from insufficient feature extraction at long distances and high computational resource overhead. To this end, this paper proposes a graph walk based lightweight network for 3D point cloud analysis tasks. Specifically, after constructing the nearest neighbor graph in the downsampled point cloud, the MLP module in the original network was replaced by multiple nonparametric components to reduce the number of parameters of the model. Then, the graph walking mechanism was used to extract the long-distance features and geometric structure features of the point cloud. Finally, before point classification and segmentation, the residual pooling module is used to fuse the features to obtain the global features. Compared with the benchmark network DGCNN, the classification accuracy on the ScanObjectNN dataset is improved by 4.2%, the segmentation accuracy on the ShapeNetPart dataset is improved by 1.1%, and the number of model parameters is reduced by 33.3%. Experiments show that the design method has obvious effect on 3D point cloud analysis tasks. © 2023 IEEE.

关键词： Graph neural networks

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Design and implementation of a standardized review system for gas station safety production based on SpringBoot 2

Design and implementation of a standardized review system fo...

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2nd International Symposium on computer Applications and Information systems, ISCAIS 2023

作者： Li, Heng Liu, Yuxing School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Shanghai Tansi Computer System Co. Ltd Shanghai China

ISBN: (纸本)9781510666665

Gas stations are flammable and explosive hazardous places, and safety is the first priority. At present, when gas stations are reviewed for production safety standardization, they mainly rely on reviewers to score on site on the production safety standardization review form, and then conduct manual statistical analysis. The development of a standardized production safety review system for gas stations can overcome the problems of high labor intensity, low work efficiency, and general review accuracy caused by the manual mode. The system is developed by using software engineering methods, following the object-oriented development idea, adopting the development mode of separating the front and rear ends, developing the back end based on the SpringBoot framework, developing the front end using the VUE framework, and selecting MySQL 8.0 as the background database. The system mainly includes review index management module, release review task module, implementation review module, and management review result module. The practical application of the system can improve the efficiency and quality of the standardization review of gas station safety production, so as to further improve the safety management level and informatization level of the gas station. © 2023 SPIE.

关键词： Efficiency

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Coverage Hole Aware Data Routing for Mobile Sink Wireless Sensor Networks 14

Coverage Hole Aware Data Routing for Mobile Sink Wireless Se...

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14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Wedaj, Fisseha Teju Hawbani, Ammar Wang, Xingfu Al-Gunid, Haithm M. Yilma, Getinet Farooq Qaisar, Muhammad Umar University of Science and Technology of China School of Computer Science and Technology Hefei China Shenyang Aerospace University School of Computer Science Shenyang China Adama Science and Technology University School of Electrical Engineering and Computing Adama Ethiopia Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350361889

In wireless sensor networks (WSNs), the coverage hole problem is one of the challenging problems that needs an effective solution. Data routing protocols in WSNs aim to disseminate the sensors' data to the central device with the minimum possible network resource usage. However, suppose there is a coverage hole in the middle of the source and destination nodes, and the source node doesn't have information about the hole. In that case, the data may choose the longer and more repetitive path to reach the destination, increasing resource usage, mainly energy and time. The condition is even more challenging when the network employs a mobile data collector. This paper proposes a Coverage Hole Aware Data Routing (CHADR) protocol for Mobile Sink WSNs that determines the shorter hole bypassing path in advance at the source node using the hole and data collector location information. CHADR evaluates the path efficiency using directional angle and expected hops count. After determining the path, data will be transmitted to the mobile sink using an Opportunistic routing technique. We proved CHADR performance with experimental simulations. © 2024 IEEE.

关键词： Routing protocols

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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