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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

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A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving

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Autonomous Intelligent systems 2025年第1期5卷 1-15页

作者： Fan, Rui Li, Jiahang Li, Jiaqi Wang, Jiale Long, Ziwei Jia, Ning Liu, Yanan Wang, Wenshuo Bocus, Mohammud J. Vityazev, Sergey Chen, Xieyuanli Xiao, Junhao Andreev, Stepan Lu, Huimin Dvorkovich, Alexander College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and the Frontiers Science Center for Intelligent Autonomous Systems Tongji University 4800 Caoan Rd Shanghai201804 China College of Electronics & Information Engineering Tongji University 4800 Caoan Rd Shanghai201804 China School of Microelectronics Shanghai University 20 Chengzhong Rd Shanghai201800 China School of Mechanical Engineering Beijing Institute of Technology 5 Zhongguancun South Street Beijing100081 China Department of Electrical and Electronic Engineering University of Bristol Woodland Road England BristolBS8 1UB United Kingdom Faculty of Radio Engineering and Telecommunications Ryazan State Radio Engineering University Ulitsa Gagarina 59/1 Ryazan Oblast Ryazan390005 Russia College of Intelligence Science and Technology National University of Defense Technology 109 Deya Road Changsha410073 China Microwave Photonics Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia Multimedia Technology and Telecom Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia

Road scene parsing is a crucial capability for self-driving vehicles and intelligent road inspection systems. Recent research has increasingly focused on enhancing driving safety and comfort by improving the detection of both drivable areas and road defects. This article reviews state-of-the-art networks developed over the past decade for both general-purpose semantic segmentation and specialized road scene parsing tasks. It also includes extensive experimental comparisons of these networks across five public datasets. Additionally, we explore the key challenges and emerging trends in the field, aiming to guide researchers toward developing next-generation models for more effective and reliable road scene parsing. © The Author(s) 2025.

关键词： Semantics

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Highly efficient flexible perovskite solar cells with vacuum-assisted low-temperature annealed SnO2 electron transport layer

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Journal of Energy Chemistry 2022年第4期31卷 1-7页

作者： Xiaoguo Li Zejiao Shi Fatemeh Behrouznejad Mohammad Hatamvand Xin Zhang Yaxin Wang Fengcai Liu Haoliang Wang Kai Liu Hongliang Dong Farhan Mudasar Jiao Wang Anran Yu Yiqiang Zhan Center for Micro Nano Systems School of Information Science and Technology(SIST)Fudan UniversityShanghai 200433China Academy for Engineering&Technology Fudan UniversityShanghai 200433China Center for High Pressure Science and Technology Advanced Research PudongShanghai 201203China

The demand for lightweight, flexible, and high-performance portable power sources urgently requires high-efficiency and stable flexible solar cells. In the case of perovskite solar cells(PSCs), most of the common electron transport layer(ETL) needs to be annealed for improving the optoelectronic properties,while conventional flexible substrates could barely stand the high temperature. Herein, a vacuumassisted annealing SnO_(2) ETL at low temperature(100℃) is utilized in flexible PSCs and achieved high efficiency of 20.14%. Meanwhile, the open-circuit voltage(V_(oc)) increases from 1.07 V to 1.14 V. The flexible PSCs also show robust bending stability with 86.8% of the initial efficiency is retained after 1000 bending cycles at a bending radius of 5 mm. X-ray photoelectron spectroscopy(XPS), atomic force microscopy(AFM), and contact angle measurements show that the density of oxygen vacancies, the surface roughness of the SnO_(2) layer, and film hydrophobicity are significantly increased, respectively. These improvements could be due to the oxygen-deficient environment in a vacuum chamber, and the rapid evaporation of solvents. The proposed vacuum-assisted low-temperature annealing method not only improves the efficiency of flexible PSCs but is also compatible and promising in the large-scale commercialization of flexible PSCs.

关键词： Flexible perovskite solar cells Vacuum-assisted Electron transport layer Trap-assisted recombination

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Multimedia Fingerprinting Codes Resistant to Linear Attacks and Adversarial Noise

Multimedia Fingerprinting Codes Resistant to Linear Attacks ...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Fernandez, Marcel Kabatiansky, Grigory Kamel, Ibrahim Miao, Ying Rabie, Tamer Farouk The Technical University of Catalonia Department of Network Engineering Spain Center for Next Generation Wireless and IoT Skolkovo Institute of Science and Technology Russia University of Sharjah Electrical and Computer Engineering United Arab Emirates Institute of Engineering Information and Systems University of Tsukuba Japan

ISBN: (纸本)9798350335590

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes. © 2023 IEEE.

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LaneDraw: Cascaded lane and its bifurcation detection with nested fusion

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Science China(Technological Sciences) 2021年第6期64卷 1238-1249页

作者： REN KeYan HOU HaoChen LI SiYang YUE TianYi Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Engineering Research Center for IoT Software and Systems Beijing 100124China

Lane and its bifurcation detection is a vital and active research topic in low cost camera-based autonomous driving and advanced driver assistance system(ADAS). The common lane detection pipeline usually predicts lane segmentation mask firstly, and then makes line fitting by parabola or spline post-processing. However, if the speed of the lane and its bifurcation detection is fast and robust enough, we think curve fitting is not a necessary step. The goal of this work is to get accurate lane segmentation,identification of every lane, adaptability of lane numbers and the right combination of lane bifurcation. In this work, we relabeled lane and its bifurcation with solid line if the image of Tu Simple dataset has both of them. In the data training process, we apply a data balance strategy for the heavily biased lane and non-lane data. In such a way, we develop a competitive cascaded instance lane detection model and propose a novel bifurcation pixel embedding nested fusion method based on full binary segmentation pixel embedding with self-grouping cluster, called Lane Draw. Our method discards curve fitting process, therefore it reduces the complexity of post-processing and increases detection speed at 35 fps. Moreover, the proposed method yields better performance and high accuracy on the relabeled Tu Simple dataset. To the best of our knowledge, this is the first attempt in 2 D lane and bifurcation detection, which more often happens in actual situations.

关键词： lane detection bifurcation fusion instance segmentation autonomous driving

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ABLATION-CAM++: GROUPED RECURSIVE VISUAL EXPLANATIONS FOR DEEP CONVOLUTIONAL NETWORKS 29

ABLATION-CAM++: GROUPED RECURSIVE VISUAL EXPLANATIONS FOR DE...

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29th IEEE International Conference on Image Processing, ICIP 2022

作者： Salama, Ahmed Adly, Noha Torki, Marwan Applied Innovation Center Ministry of Communications and Information Technology Egypt Computer and Systems Engineering Department Alexandria University Egypt

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recently, providing explainable deep learning models has sparked a lot of attention. In this paper, we take a further step in this direction. We introduce a time-efficient method, called Ablation-CAM++, which can generate smooth visual explanations of CNN model predictions. Our approach uses the concept of studying the ablation analysis to determine the importance of activation maps w.r.t. the target class, similar to Ablation-CAM. However, instead of focusing on the individual importance of each activation map, we group activation maps using a clustering technique. Then, we construct a binary tree for each group by recursively splitting these groups, studying the ablation of each subgroup, and applying tree pruning. We perform qualitative and quantitative evaluations of our visual explanations against Ablation-CAM and Grad-CAM. Our approach can provide visual explanations in less than half of the time of Ablation-CAM. Using average drop and average increase evaluation metrics on 2000 images of the ImageNet validation set, we provide a comparison of the effect of applying different clustering techniques in our method. © 2022 IEEE.

关键词： Computer vision

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A Deep Encoder-Decoder Based Primal-dual Proximal Network for Image Restoration 15

A Deep Encoder-Decoder Based Primal-dual Proximal Network fo...

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Wang, Siqi Jiu, Mingyuan Chen, Li Li, Shupan Xu, Mingliang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450002 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou450002 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450002 China National Supercomputing Center in Zhengzhou Zhengzhou450002 China

ISBN: (纸本)9781510675018

Image restoration is a popular and challenge task, which is regarded as a classical inverse problem. Condat-Vu primal-dual algorithm based on proximal operator is one of successful optimization methods. It is further reformulated as a primal-dual proximal network, where one iteration in the original algorithm corresponds to one layer in the network. The drawback of primal-dual network is that blur kernels should be given as prior information, however, it is usually very hard to be known in the real situation. In this work, we propose a deep encoder-decoder primal-dual proximal network, named ED-PDPNet. In each layer, the blur kernels and the projections between the primal and dual variables are designed as encoder-decoder modules, in this way, the network can be learned in an end-to-end way and all the parameters in the primal-dual algorithm are learned. The proposed method is applied on the MNIST and BSD68 datasets for image restoration. The preliminary results show that the proposed method by combining simple encoder-decoder modules obtained very promising and competitive performance compared to the state-of-the-art methods. In addition, the proposed network is shown to be a lightweight network with fewer learning parameters in comparison to the recent popular transformer-based method. © 2024 SPIE. All rights reserved.

关键词： Image reconstruction

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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Computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Miniaturized W-band Bandpass Filter Using IPD Technology

Miniaturized W-band Bandpass Filter Using IPD Technology

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Pan, Dongzhe You, Bin Wen, Xuan College of Electronics and Information Hangzhou Dianzi University The Key Laboratory of Rf Circuits and Systems of Ministry of Education Microelectronics Cad Center Hangzhou China The School of Information Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350336535

This work presents the design of a four-order bandpass filter (BPF) for W-band applications using integrated passive device (IPD) technology. A coupling architecture with three configurable transmission zeros is employed to improve the passband selectivity. Additionally, combining folded stub loaded resonators (SLRs) and stacking resonators vertically reduces the overall foot-print of the circuit. The simulation results show that the suggested BPF, which operates at 95 GHz, has a number of benefits including low insertion loss, excellent selectivity, small size, and convenient to be integrated. The compact size and easy integration of the proposed BPF render it a compelling option for upcoming wireless communication systems. © 2023 IEEE.

关键词： Bandpass filters

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