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Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia

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Journal of Mobile Multimedia 2023年第6期19卷 1415-1438页

作者： Rajesh, M. Vengatesan, K. Sitharthan, R. Dhanabalan, Shanmuga Sundar Gawali, Mahendra Bhatu Department of Computer Engineering Sanjivani College of Engineering India Centre for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Chennai India Functional Materials and Microsystems Research Group RMIT University Melbourne 3001 VIC Australia Department of Information Technology Sanjivani College of Engineering India

The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches. © 2023 River Publishers.

关键词： content authentication decentralized trust management Federated AI mobile multimedia trustworthiness

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Constrained Deep Reinforcement Learning for Energy Management of Community Multi-Energy Systems

Constrained Deep Reinforcement Learning for Energy Managemen...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Ahmed Shaban Omar Ramadan El-Shatshat Department of Electrical and Computer Engineering Smart Grid Lab University of Waterloo Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This study proposes an intelligent energy management framework based on Constrained Deep Reinforcement Learning (CDRL) tailored for Community Multi-Energy Systems (CMES), aiming to enhance energy efficiency and mitigate carbon emissions by coordinating the optimization of various energy vectors in a synchronized manner. Initially, a multidimensional, continuous, stochastic action and state space constrained Markov Decision Process (CMDP) is employed to model the sequential multi-energy scheduling problem. Subsequently, an intelligent agent is iteratively trained through interactions with the environment, utilizing constrained policy Policy Optimization (CPO) algorithm, to learn the near-optimal system schedule while ensuring near-zero constraint violations. The effectiveness of the proposed approach is then substantiated through numerical validation conducted on authentic datasets, which incorporate electricity consumption records from fifteen households in London, Ontario, Canada, covering the years from 2014 to 2016. The results underline the potential of CDRL in enhancing sustainable energy management practices, offering insights into scalability and application in broader contexts.

关键词： Schedules Scalability Markov decision processes Simulation Stochastic processes Deep reinforcement learning Vectors

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Evaluating Solar Power Forecasting Robustness: A Comparative Analysis of XGBoost, RNN, KNN, RF, and LSTM with emphasis on Lagged Steps, Sensitivity, and Cross-Validation Techniques

Evaluating Solar Power Forecasting Robustness: A Comparative...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Mahmoud Kiasari Hamed H. Aly Electrical and Computer Engineering Department Smart Grid and Green Power Research Lab Dalhousie University

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The transition towards renewable energies is inevitable in the face of global warming conditions and the increasing scarcity of fossil fuels, alongside their harmful impacts on the environment. Solar power, considered as a clean and its availability energy source, plays a vital role in this matter. However, due to the unpredictability nature of solar, influenced by environmental and temporal factors, energy production has faced significant challenges for its integration into microgrids. The present study works on addressing these obstacles by implementing advanced machine learning techniques to empower the predictability of solar energy. Utilizing one-month datasets of two power plants in India, Gandikota, Andhra, and Nasik, Maharashtra. This work focuses on various predictive models, including Support Vector Regression (SVR), Recurrent Neural Networks (RNN), eXtreme Gradient Boosting (XGBoost)m K-Nearest Neighbors (KNN), and Random Forest. These models are evaluated under different conditions, like incorporating lagged steps, and dynamics capturing. For the evaluation, a range of metrics is used. Mean Squared Log Error (MSLE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), and R 2 , for the identification of the most effective algorithm are used. The final goal of this work is not only to improve the forecasting accuracy of solar power generation, by advantageous model modification but also to contribute to the efficient management and optimization of renewable energy integrations. Overall, XGBoost emerges as the preferred method for forecasting, offering a balance of high accuracy and robustness across different forecasting scenarios.

关键词： Support vector machines Renewable energy sources Recurrent neural networks Accuracy Solar energy Nearest neighbor methods Predictive models

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Design oligoporous-core based multimode fiber for mode division multiplexing applications

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Optical and Quantum Electronics 2025年第5期57卷 1-24页

作者： Mitu, Sumaiya Akhtar Abdulrazak, Lway Faisal Ibrahim, Sobhy M. Tahhan, Shaymaa R. Hossain, Md Bellal Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering University of Asia Pacific 74/A Green Rd Dhaka1205 Bangladesh Department of Information and Communication Technology Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Laser and Optoelectronics Engineering College of Engineering Al-Nahrain University Jadriya Baghdad64074 Iraq School of Electrical and Information Engineering The University of Sydney SydneyNSW Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

A polarization-maintaining oligoporous-core-based multi-mode fiber is proposed. By tuning the air hole, as well as the core number, shape, size, and position up to 28 distinct linearly polarized (LP) modes are obtained. The Finite Element Method (FEM) is used to perform the numerical investigations. In addition, various materials combinations are used as a doping with silica which is highly helpful to increase or decrease the refractive index of the core material. The multimode fiber is identified by the normalized frequency or V parameter. Besides, the high birefringence value, low loss value, minimum crosstalk with high sensitivity response of 1.46×10-2, 2×10-11dB/m, 41.80 dB and 88,280.46 nm/RIU, respectively, are achieved from the numerical investigations over the wavelength range from 1.55 µm to1.65 µm for the different LP modes. Moreover, good responses also obtain for the numerical aperture, V number, coupling length and other parameters. In the end, every value reveals both an easy-to-fabricate structure design as well as adequate performance analysis. The suggested fiber structure can support many modes and might be applicable in the field of optical communications and spatial multiplexing based on the user demand. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fiber to the x

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Water droplets play a role in Internet of Things applications

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Droplet 2024年第3期3卷 27-48页

作者： Feng Wen Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore i-Lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)SuzhouChina NUS Graduate School—Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingapore

Water droplets help life in nature survive,thrive,and *** water droplet serv-ing as one of the indispensable elements in the Internet of Things(IoT),many droplet-oriented technologies,such as microfluidics,droplet manipulation,electrowetting,and energy harvesting,make rapid progress driven by material science,computer science,and ***-based wearable devices are endowed with advantages such as flexibility,sensing ability,and automation for various parameter ***,the continuous exploration of droplet manipulation has led to the emergence of a wide variety of manipulation ***,electrowetting that utilizes exter-nal fields modifying liquid–solid surfaces has found its applications in various areas,including droplet transportation,microfabrication,and *** energy gener-ation from water droplets also presents exciting opportunities for the development of novel electricity *** approaches for droplet utilization underscore the immense potentials and versatilities of droplet-based technologies in the IoT ***,this mini review presents the fundamental droplet-based technologies by summarizing their working mechanisms and methods,device structures,and *** the challenges in materials,fabrication,and system integration,this review shows the overall development roadmap in terms of improved functionality and performance and highlights the opportunities toward multifunctional,self-sustainable,and intelligent systems,which is called for IoT construction.

关键词： IoT utilize fabrication

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Improvement of Microgrids Operation Considering Demand Response Using Imperialist Competitive Algorithm

Improvement of Microgrids Operation Considering Demand Respo...

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IEEE International Conference on smart Energy Grid engineering (SEGE)

作者： Mahdi Ghaffari Hamed H. Aly Electrical and Computer Engineering Department Smart Grid and Green Power Research Lab Dalhousie University Halifax NS Canada

ISBN: (数字)9798350377378

ISBN: (纸本)9798350377385

In recent years, with the restructuring and privatization of power networks, special attention has been paid to the role of customers in network operation. Therefore, demand response programs have played an important role in power network studies and many researchers have been working in this area. Microgrids are a collection of loads and generating units that can supply their own power independently. In this article, the problem of demand response of loads in a microgrid has been studied. For this purpose, the customers in a microgrid announce their proposed prices for participating in the demand response program to the network operator for different hours of the day. This demand response program will be executed when the network operation constraints are violated. Therefore, there is no need to run it in the hours when the network is operating properly. The network operator will run the demand response program with the aim of removing the constraints imposed on the network. For this purpose, the imperialist competitive algorithm (lCA) has been used to find the best possible solution. After running the proposed method, it will be determined how much power each load should reduce in each hour. The proposed method has been implemented on a sample network and its results have been evaluated and analyzed for different scenarios. The problem modeling has been done using MATlab software. The results proved the effectiveness of the proposed model.

关键词： Costs Software algorithms Stochastic processes Microgrids Voltage Demand response Stability analysis Software smart grids Load modeling

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Dual-UNet: An End-to-End Deep Learning Framework for the Automatic Chronic Diabetic Wound Segmentation

Dual-UNet: An End-to-End Deep Learning Framework for the Aut...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mustafa Alhababi Gregory Auner Hafiz Malik Lubna Alazzawi Muteb Aljasem Electrical and Computer Engineering Wayne State University Detroit MI USA Department of Smart Sensors and Integrated Microsystems Program Wayne State University Detroit MI USA Electrical and Computer Engineering University of Michigan-Dearborn Dearborn MI USA Electronics and Computer Engineering & Robotics Engineering Bowling Green State University Bowling Green OH USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Foot ulcers are one of the chronic wounds commonly occurring in diabetic patients and are associated with a high risk of major or minor amputations. Evaluation and measurement of such wounds are done manually by experts to assess the wound healing and suggest further treatment. Manual assessment methods can cause infections at times and are less feasible in the existence of pandemic situations. The development of artificial intelligence has brought a revolution in many domains of medical imaging. Deep learning-based computer-aided approaches can be applied to assist clinicians in assessing and monitoring chronic wounds. Wound segmentation helps to measure wound area which is useful for assessing its healing progress. In this research work, a deep learning-based segmentation method, namely Dual-UNet is presented for precisely segmenting the diabetic foot ulcers images. The foot ulcer images are passed to Dual-UNet to generate the mask image having the segmented wound area. We proposed two modified UNets each having a customized encoder and decoder with skip connections, atrous spatial pyramid pooling (ASPP) block, and output block to improve the segmentation results. We assessed our framework by performing experimentation on two benchmark datasets, named the foot ulcer segmentation (FUSeg) challenge and the AZH segmentation dataset. Additionally, we have also assessed our Dual-UNet for cross-corpora validation to demonstrate the generalization capability of the proposed approach. The qualitative and quantitative results highlight the accuracy and effectiveness of the Dual-UN et framework for segmenting chronic diabetic wounds. Future research should focus on the development of large and diverse wound datasets and improving the results for cross-corpora evaluation.

关键词： Deep learning Image segmentation Pandemics Computational modeling Manuals Wounds Diabetes Decoding Information technology Monitoring

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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3D-Printed Stainless Steel Electrodes for Advancing Mems Microbial Fuel Cells Toward Sustainable on-chip Energy 38

3D-Printed Stainless Steel Electrodes for Advancing Mems Mic...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Elhadad, Anwar Li, Guangfa Yang, Jiaqi Liu, Dehao Choi, Seokheun State University of New York at Binghamton Integrated Computational Materials Design Lab Department of Mechanical Engineering BinghamtonNY United States State University of New York at Binghamton Bioelectronics & Microsystems Laboratory Department of Electrical & Computer Engineering BinghamtonNY United States State University of New York at Binghamton Center for Research in Advanced Sensing Technologies and Environmental Sustainability BinghamtonNY United States

ISBN: (纸本)9798331508890

This study introduces a stainless steel microbial anode fabricated via advanced laser powder bed fusion, marking a transformative step in microbial fuel cell (MFC) technology. By leveraging this cutting-edge metal 3D printing approach, we achieve a 3D microporous structure with superior electrical conductivity, outstanding corrosion resistance in aquatic environments, robust mechanical strength, and adaptability for surface modification - essential attributes for sustainable, high-performance MFCs. The assembled MFC demonstrates unparalleled power output, along with exceptional durability and stability over multiple cycles, outperforming existing MFCs based on alternative 3D-printed substrates. This advancement not only solidifies the role of MFCs as a sustainable energy solution for powering on-chip electronics but also opens new avenues for scalable, cost-effective bioelectronic systems that align with the demands of next-generation technology. © 2025 IEEE.

关键词： Corrosion resistance

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