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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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Practical Secure Heterogeneous Communication for Internet of Things

SSRN

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SSRN 2022年

作者： Jin, Chunhua Zhu, Huihui Qin, Wenyu Chen, Zhiwei Jin, Ying Shan, Jinsong Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an233003 China

Internet of things (IoT) is a promising network that combines various information sensing devices with the Internet to realize the interconnection of pervasive things. Security is a pivotal issue to ensure widespread adoption of the IoT. To ensure the security of data transmission, in this paper, we construct a heterogeneous online and offline signcryption from a wireless sensor network (WSN) to an Internet server. In our construction, the WSN is in certificateless cryptosystem (CLC) which does not involve public key certificate management problem in public key infrastructure (PKI) and key escrow problem in identity based cryptosystem (IBC), while the server is in IBC which avoids public key certificate management problem. For signcryption process, the proposed scheme is split into two steps: offline step and online step. In the former step, the time-consuming calculations are completed, but do not involve the transmitted message. In the latter step, only some lightweight calculations are involved, but message must be available. Confidentiality, authentication, non-repudiation, and integrity can be achieved in a logical single step. In addition, an elaborate performance analysis demonstrates that our proposed scheme has advantages in terms of computational cost and private key size, offline storage, as well as communication overhead. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Deep Learning-based Classification of Lung CT Scan for Accurate Cancer Diagnosis 7

Deep Learning-based Classification of Lung CT Scan for Accur...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sujatha, D.Christy Lakshmi, T.R. Vijaya Surya, G. Surendar, U. Rajendiran, M. Maranan, Ramya Periyar Maniammai Institute of Science And Technology Department of Software Engineering Tamail Nadu Thanjavur India Mahatma Gandhi Institute of Technology Department of Electronics And Communication Engineering Telangana Hyderabad India K. Ramakrishnan College of Engineering Department of Computer Science And Engineering Tamil Nadu Tiruchirappalli India Srm Institute of Science And Technology Department of Electronics And Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Poonamallee India Saveetha School of Engineering SIMATS Department of Research And Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

This paper investigates the suitability of advanced deep learning models for precise diagnosis of lung cancer from MRI images. Recurrent neural networks (RNN), K-Nearest Neighbors (KNN), ResNet50, and convolutional neural networks (CNN) were all carefully evaluated to determine their unique contributions. The CNN showed off its good performance and capacity to recognize intricate patterns in lung images, achieving an accuracy of 92.3%. KNN demonstrated competitive results, demonstrating the adaptability of non-parametric methods for medical image classification. Remarkably, ResNet50 fared extremely well, exhibiting a remarkable accuracy of 94.8% and verifying the value of deep residual networks in differentiating between intricate features. RNNs gave the analysis a temporal dimension and contributed to its 89.5% accuracy. Information from confusion matrices containing comprehensive classification results was useful in refining the model. Spatial representations of expected cancer cell locations showed the effectiveness of the models by giving doctors visual cues for targeted interventions. Comparisons with the literature show that the results are in line with recent developments in deep learning for medical image analysis. Because of its comprehensive assessment of different deep learning architectures, which provides fresh perspectives that advance the field of lung cancer detection technologies, this work is an invaluable resource for future research. © 2024 IEEE.

关键词： computerized tomography

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Drowsy Driving Detection System Using Face Detection 3

Drowsy Driving Detection System Using Face Detection

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Mirabdullayev, Ismoiljon Ahamed Ayoobkhan, Mohamed Uvaze Jasmine Hashana, A.M. Khan Subair Ali, Liayakath Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication Tmc Institute in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone. © 2023 IEEE.

关键词： allowed distraction time braking distance deep learning driver's fatigue level Drowsy driving face analysis reaction distance

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Pondering About Task Spatial Misalignment: Classification-Localization Equilibrated Object Detection 48

Pondering About Task Spatial Misalignment: Classification-Lo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Yudong Lu, Wei Wang, Xu Wang, Pengkun Wang, Yang Hefei China School of Software Engineering Ustc Hefei China School of Computer Science and Technology Ustc Hefei China School of Data Science Ustc Hefei China Suzhou Institute for Advanced Research Ustc Suzhou China

ISBN: (纸本)9781728163277

Object detection is a fundamental task in computer vision, consisting of both classification and localization tasks. Previous works mostly perform classification and localization with shared feature extractor like Convolution Neural Network. However, the tasks of classification and localization exhibit different sensitivities with regard to the same feature, hence the "task spatial misalignment"issue. This issue can result in a hedge issue between the performances of localizer and classifier. To address these issues, we first propose a novel Dynamic Coefficient Loss to simultaneously consider and balance the performances of classification and localization tasks. To well address anchor label misjudgment issue in irregular- shaped object detection, we define a new classification-aware IoU metric to assign anchors intelligently. Finally, we further introduce the localization factor into NMS by proposing a Classification-Localization balanced NMS. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our proposals can improve RetinaNet by around 1.5% AP with various backbones. © 2023 IEEE.

关键词： Non-Maximum Suppression Object detection task spatial misalignment

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Heterologous Image Matching Based on Salience Region Using Q-test and Kernel Density Estimation 4

Heterologous Image Matching Based on Salience Region Using Q...

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Zhai, Jinlong Yan, Xiaohu Xu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-MacaoGreater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781510682528

To address the matching problem caused by the salience differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region using Q-test and kernel density estimation is proposed in this paper. Firstly, the center pixel point of each sub-region in the image is detected for salience difference with other points in its neighborhood by using the Q-test method. The detected points are defined as Q-points. Secondly, the point with the largest kernel density in all Q-points is calculated by using kernel density estimation function and defined as the M-point. Then, the region size is defined according to the coordinates of M-point, and the salience region is detected. Finally, to comprehensively evaluate the performance of the proposed method, five common types of heterologous images are selected as data sources, including point cloud depth maps, infrared images, electronic navigational maps, synthetic aperture radar images, and night-time light images. Based on the salience regions, comparative experiments are carried out using histogram of absolute phase consistency gradients (HAPCG) and histogram of the orientation of the weighted phase descriptor (HOWP). The results show that the matching performance of the proposed method is better than that of the original algorithm without salience region, especially on the type of electronic navigational map. © 2024 SPIE.

关键词： Radar imaging

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models

Countering Deepfakes using an Improved Advanced CNN and its ...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Abeer Mathur Moulik Tejpal Kshitiz Bhargava Krishnaraj Natarajan Manvendra Singh Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models—MobileNet and Xception—with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online.

关键词： Training Deepfakes Solid modeling Accuracy Forensics Solids Convolutional neural networks Stakeholders Standards Testing

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