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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ouyang, Man Zhang, Ran Wang, Bingqing Liu, Jiang Zhuang, Weihua Bupt State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Future Network Research Center Nanjing211111 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350351255

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes. © 2024 IEEE.

关键词： Dynamic programming

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Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations 23rd

Learning Shape-Preserving Autoencoder for the Reconstructi...

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23rd International Conference on Computational Science, ICCS 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science and Software Engineering Concordia University 1455 De Maisonneuve Blvd. West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031360206

We propose a new autoencoder preserving input functions’ general shape (monotonicity, convexity) after their reconstruction without imposing a priori constraints. These properties are inherent to the coefficients of the Bernstein-Durrmeyer polynomials that serve here as theoretical descriptors. Their estimates, computed from noisy observations by the coder, play the role of latent variables. The approach is purely nonparametric, i.e., no prior finite-dimensional model is assumed. The answer to the question of how many latent variables should be used for an acceptable reconstruction accuracy of a family of functional data is inferred from learning based on the proposed approximation of the Akaike Information Criterion. A distinguishing feature of this autoencoder is that the coder and encoder are designed as precomputed and stored matrices with the Bernstein polynomial entries. Thus, after selecting the number of latent variables, the autoencoder usage has a low computational complexity since it is linear with respect to observations. The proposed computational algorithms are tested on real data arising in mechanical engineering when control of damping vibrations is necessary. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomials

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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A Novel Network Security Data Resource Description Standard

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 295-305页

作者： Han, Wei Sun, Xiu-Yan He, Chen Tang, Lin-Lin Kumari, Saru Department of Electronic and Information Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Department of Mechanical and Electrical Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Media and Communications College WeiFang University No. 5147 Dongfeng East Street Shandong Weifang China Department of Computer Science and Engineering Harbin Institute of Technology Shenzhen Taoyuan Street Shenzhen China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

Current network security situational awareness field has problems of poor communication and low efficiency of data resources and information. Therefore, in order to solve these two problems, this paper has established a better network security data resource description standard (metadata standard) for communication of data resource information, so that communication of data resource information has a unified standard basis. Then it provides a more efficient channel for circulation of data resource information, and improve efficiency of data resource information flow through publishing and subscription technology. Finally, a data resource sharing platform was developed, which applied the above metadata standards. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Network security

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Geometric Mean Rate Maximization in RIS-aided mmWave ISAC Systems Relying on a Non-Diagonal Phase Shift Matrix

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4756-4771页

作者： Singh, Jitendra Jagannatha, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

The joint optimization of the hybrid transmit precoders (HTPCs) and reflective elements of a millimeter wave (mmWave) integrated sensing and communication (ISAC) system is considered. The system also incorporates a reconfigurable intelligent surface (RIS) relying on a non-diagonal RIS (NDRIS) phase shift matrix. Specifically, we consider a hybrid architecture at the ISAC base station (BS) that serves multiple downlink communication users (CUs) via the reflected links from the RIS, while concurrently detecting multiple radar targets (RTs). We formulate an optimization problem that aims for maximizing the geometric mean (GM) rate of the CUs, subject to the sensing requirement for each RT. Additional specifications related to the limited transmit power and unit modulus (UM) constraints for both the HTPCs and the reflective elements of the NDRIS phase shift matrix make the problem challenging. To solve this problem, we first transform the intractable GM rate expression to a tractable weighted sum rate objective and next split the transformed problem into sub-problems. Consequently, we propose an iterative alternating optimization approach that leverages the majorization-minimization (MM) framework and block coordinate descent (BCD) method to solve each sub-problem. Furthermore, to tackle the UM constraints in the sub-problem of the HTPC design, we propose a penalty-based Riemannian manifold optimization (PRMO) algorithm, which optimizes the HTPCs on the Riemannian manifold. Similarly, the phases of the reflective elements of the NDRIS are optimized by employing the Riemannian manifold, and the locations of the non-zero entries of the NDRIS phase shift matrix are obtained by the maximal ratio combining (MRC) criterion. Finally, we present our simulation results, which show that deploying an NDRIS achieves additional gains for the CUs over a conventional RIS, further enhancing both the communication efficiency and sensing reliability. Furthermore, we compare the results t

关键词： Image analysis

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Utilizing Deep Convolutional Neural Network and Local Binary Pattern Fusion for Plant Leaf Disease Identification: An Integrated Approach

Utilizing Deep Convolutional Neural Network and Local Binary...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： S. Rahamat Basha Pratibha C. Kaladeep Yalagi Balaram Puli Pandian Sundaramoorthy Rajesh Daruvuri S Vinayagapriya Department of computer science and engineering Malla reddy college of engineering and technology Department of Computer Science and Engineering Walchand Institute of Technology Solapur Pratibhayalagi Engineering and Data Science Everest Computers Inc Application Developer EL CIC-1W-AMI IBM Technical Account Manager Cloud Data and AI Google Dept of ece St.josephs college of engineering

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural Networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity.

关键词： Productivity Plant diseases Accuracy Merging Crops Benchmark testing Feature extraction Spatial databases Bayes methods Convolutional neural networks

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3D Point Cloud Reconstruction Based on Deformed Network

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 818-827页

作者： Liu, Wei Sun, Xiu-Yan Tang, Lin-Lin Kumar, Sachin Department of Electronic and Information Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Department of Mechanical and Electrical Engineering Laiwu Vocational and Technical College No. 1 Shancai Street Shandong Jinan China Department of Computer Science and Engineering Harbin Institute of Technology Shenzhen Taoyuan Street Shenzhen China Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Delhi-Hapur Bypass Road Ghaziabad India

With development of deep learning, 3D reconstruction has become more and more popular based on it. For complexity and variability of 3D reconstruction object itself, overall reconstruction quality of 3D reconstruction method based on voxels is not high. 3D reconstruction method based on point cloud has better reconstruction effect than the method based on voxel. In this paper, a fully connected point cloud deformation network and a GraphX-based multi-resolution point cloud deformation network is proposed. Experiments show its efficiency and when IoU is used as evaluation metrics, problem of poor quality evaluation in most voxel-based methods can be solved. © 2021, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Deformation

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Deep learning based DeepFake video detection

Deep learning based DeepFake video detection

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Guefrachi, Sarra Ben Jabra, Marwa Alsharabi, Naif A. Ben Othman, Mohamed Tahar Alharabi, Yasser O. Alkholidi, Abdulsalam Hammam, Habib Université de Moncton Faculty of Engineering MonctonNBE1A3E9 Canada Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia Computer Engineering University of Ha'il Computer Science Amran University Yemen Ha'il55476 Saudi Arabia College of Computer Qassim University Department of Computer Science Buraydah51452 Saudi Arabia College of Computer Sc. & Engneering Ha'il University Computer Science and Information Ha'il Saudi Arabia Canadian Institute of Technology College Department of Software Engineering Tirana Albania International Institute of Technology and Management Commune d'Akanda BP Libreville1989 Gabon School of Electrical Engineering University of Johannes-burg Dept of Electrical and Electronic Eng. Science Tirana Albania

ISBN: (纸本)9798350347050

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%. © 2023 IEEE.

关键词： Crime

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Deep Learning-Based Automated Segmentation of Fiber Breaks in Advanced Composites 38

Deep Learning-Based Automated Segmentation of Fiber Breaks i...

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38th Technical Conference of the American Society for Composites, ASC 2023

作者： Jain, Sudhir Li, Carina Xiaochen Vuong, Daniel McGllick, Matthew Saifaee, Saeed Furtado, Carolina Kopp, Reed Joseph, Joshua Adjodah, Dhaval Rosini, Sebastian Sinclair, Ian Spearing, S. Mark Wardle, Brian L. Integrated Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Southampton SouthamptonSO17 1BJ United Kingdom Departments of Aeronautics and Astronautics and Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781605956916

The heterogeneity of the microstructure and the anisotropy of the mechanical properties of carbon fiber reinforced polymer composites (CFRP) are associated with complex damage mechanisms that make failure prediction highly challenging. Typical damage mechanisms for CFRP include: (i) interlaminar cracking (delamination), (ii) matrix cracking, and (iii) fiber breakage. X-ray computed tomography (CT) has been utilized recently to identify the complex damage in progressive failure of CFRP in 3D and 4D (3D spatial and 1D temporal). However, these scans contain a large amount of data (≈10 GB/mm3), which makes it challenging to extract mechanistic insights in a reasonable time frame. In previous work, we presented the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale matrix damage in CFRP. The trained CNN segments complex and sparse composite matrix damage mechanism to ≈ 99.99% agreement, with nearly 100% of human time eliminated, providing higher accuracy compared to human segmentation. In this work, we developed CNN-based method for the automated segmentation of fiber breaks using synchrotron radiation-based CT (SRCT) scans that have been human-labeled. The model employed a U-Net full CNN as the high-level encoder (feature extraction)-decoder (feature localization) structure, and VGG16 as the deep CNN backbone via the publicly available Segmentation Models GitHub repository. An unbiased evaluation was performed comparing the machine vs. the human-labeled ground truth. Finally, active learning was presented for the machine-assisted relabeling of datasets containing fiber breaks. The deep learning-based automated segmentation method demonstrated accurate identification (> 90%) of fiber breakage for CFRP. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Complex networks

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Comparison of Classification Algorithms for Alzheimer's Disease Prediction 7

Comparison of Classification Algorithms for Alzheimer's Dise...

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7th International Conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Saxena, Harshit Joshi, Divyansh Singh, Hari Anand, Rohit Jaypee University of Information Technology Computer Science and Engineering Department Himachal Pradesh Solan India G.B.Pant Dseu Okhla-1 Campus Ece Department New Delhi India

ISBN: (纸本)9781665454018

The application of machine learning to investigate metabolic disorders like diabetes and Alzheimer's Disease (AD), which impact significant populations of people worldwide, is currently receiving a lot of attention. Early on, AD was challenging to predict. The method of identifying the parameters that were most effective for predicting Alzheimer's disease involved the use of decision trees, support vector machines, XGBoost, random forest, extra trees, AdaBoost, gradient boosting, and voting classifiers. In this research, the Extra Trees classifier has been used which, from our research, has rarely been explored in the past. This classifier gives the best results in terms of performance among all the implemented techniques. The performance is evaluated using evaluation parameters such as precision, recall, F1-Score and accuracy. Using these machine learning methods, especially the Extra Trees algorithm, for early identification of Alzheimer's disease will greatly benefit in lowering the annual mortality rate caused by AD. © 2022 IEEE.

关键词： Adaptive boosting

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