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Joint Positioning and Computation Offloading in Multi-UAV MEC for Low Latency Applications: a Proximal Policy Optimization Approach

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Yuhui Farooq, Junaid Ghazzai, Hakim Setti, Gianluca University of Michigan-Dearborn Department of Electrical & Computer Engineering College of Engineering and Computer Science DearbornMI48128 United States Division Saudi Arabia

Multi-access edge computing (MEC) has emerged as a proven solution for reducing communication latency and enhancing user experience in delay-sensitive applications by offloading computation-intensive tasks to edge servers. In future networks, unmanned aerial vehicles (UAVs), with their flexible deployment and reliable communication capabilities, have the potential to be deployed as aerial MEC servers in areas lacking cellular infrastructure. However, the joint optimization of UAV placement and task offloading poses significant challenges due to the interdependence between communication latency, computational demands, and the resource limitations of UAVs. In this paper, we propose a novel joint optimization framework utilizing proximal policy optimization (PPO) to simultaneously address UAV placement and computation offloading in UAVenabled MEC networks. The framework dynamically adapts to changing network conditions, minimizing end-to-end latency while balancing computational loads and energy consumption. Extensive simulations demonstrate that the proposed PPO-based approach achieves superior performance compared to conventional optimization methods, with significant improvements in system latency, resource utilization, and network resilience. This work contributes scalable, adaptive solutions for UAV-assisted MEC networks in dynamic environments, enabling robust support for mission-critical and latency-sensitive applications. © 2002-2012 IEEE.

关键词： Computation offloading

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Design and Analysis of Piezoelectric Energy Harvester for Wireless Sensor Networks 2nd

Design and Analysis of Piezoelectric Energy Harvester for Wi...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Ravikumar, C.V. Sathish, K. Su, Chunhua School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Deemed University Chennai602105 India Division of Computer Science University of Aizu Fukushima Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9789819785452

The design and study of piezoelectric energy harvesters for wireless sensor networks (WSNs) includes the creation of effective energy solutions to increase the operating life of sensors without requiring frequent battery changes. These harvesters are intended to capture energy from piezoelectric sources. This study focuses on providing a sustainable power supply for WSNs through the use of piezoelectric materials. These materials can transfer mechanical vibrations from surrounding sources into electrical energy. The primary goal of this study is to maximise the quantity of energy that can be captured by maximising the piezoelectric harvester’s design parameters. These parameters include material selection, geometric arrangements, and the harvester’s positioning in various circumstances. Sophisticated modelling approaches and analytical models are used to forecast the harvester’s performance across a range of vibrational frequencies and amplitudes. In addition, experimental validation is performed to ensure that the theoretical models are accurate. The investigation reveals the primary factors influencing the efficiency and feasibility of piezoelectric energy harvesters. These considerations include resonance tuning, power conditioning circuits, and integration with energy storage systems. Both of these factors are well examined. Not only does this comprehensive approach highlight the potential of piezoelectric harvesters to significantly improve the energy autonomy of WSNs, but it also lays the groundwork for future advances in low-power, self-sustaining sensor networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Piezoelectricity

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GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles 27th

GUARDEV: Guided Unified Adaptive Response for Defending Elec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mondal, Koustav Kumar Das, Debasis Inter-Disciplinary Research Division - IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783821

In the rapidly evolving landscape of electric vehicles (EVs), integrating the Internet of Vehicles (IoV) has opened up new dimensions in automotive technology. While enhancing vehicle performance, safety, and security, this advancement also exposes EVs to heightened cyber threats. To address this critical concern, we introduce GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles, a novel Machine Learning (ML)-enhanced conflict resolution-based ensemble Intrusion Detection System (IDS) architecture. GUARDEV leverages the power of adaptive ML techniques to provide a unified and guided response in safeguarding EVs against sophisticated cyber-attacks. Our framework evaluates the effectiveness of three prominent ML models - XGBoost, Random Forest, and Extra Trees - in detecting specific categories of cyber threats. By harnessing the prediction confidence scores from these models, GUARDEV offers a nuanced and intelligent approach to recognizing and thwarting diverse cyber-attacks. Our research, grounded in rigorous experimentation using public IoV security datasets, demonstrates GUARDEV’s exceptional accuracy of 99.47% in intrusion detection. This remarkable achievement not only underscores the potential of ML in enhancing vehicular cybersecurity but also establishes GUARDEV as a powerful tool for creating safer, more secure EVs. GUARDEV paves the way for robust protection of internet-connected electric vehicles in today’s digital age by providing a guided, unified, and adaptive response to cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

Advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Advancing ECT Imaging: Residual UNet Deep Neural Network Approach 1st

Advancing ECT Imaging: Residual UNet Deep Neural Network App...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Deabes, Wael Khalifa, Fahmi Abdel-Hakim, Alaa E. Bouazza, Kheir Eddine Computational Engineering Mathematical Sciences Department Texas A&M University SA San AntonioTX78224 United States Electrical and Computer Engineering Department Morgan State University BaltimoreMD21251 United States Computer Science Department in Jamoum Umm Al-Qura University Makkah Saudi Arabia Computer and Information Science Division Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031823763

Recently, there has been significant progress in Electrical Capacitance Tomography (ECT) image reconstruction, yet there remains room for enhancing both speed and quality in these reconstructions. Deep Learning (DL) offers a promising avenue due to its capacity to handle intricate nonlinear functions. This study introduces DL-based ECT image reconstruction model based on Residual UNet and termed as ECT_ResUNet. A sizable dataset containing permittivity distribution and corresponding capacitance measurements was created to train and evaluate the proposed model. The ECT_ResUNet processes modulated capacitance vectors, representing them as 66×66 images. The model’s effectiveness and practicality were assessed using unseen samples, demonstrating precise image reconstruction with an average Image Error (IE) of 0.0918 and Correlation Coefficient (CC) of 0.9527. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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MindMate: AI-Powered Multilingual Mental Health Chatbot with Personalized Voice and Text Support with Rasa and Streamlit

MindMate: AI-Powered Multilingual Mental Health Chatbot with...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Dharshini S Samson Arun Raj A Venkatesan R Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

An AI motivated mental health chat bot which can enable personalized support through voice as well as text based interaction, it’s dubbed as MindMate. Using the Rasa framework for natural language understanding (NLU) and dialogue management, along with Streamlit to make the interface user friendly and breathable for people, it makes an empathetic environment to allow people to express themselves and get responses from it. It is a multilingual chatbot, so it can be used by a lot of people with different languages. MindMate approaches users with a personalized approach to learn from user interactions to provide personalized responses, suggestions and resources that respond to the individual needs and preferences. This unique system not only makes it easy to share how you feel, but also gives you useful resources for dealing with whatever problem or issue you may be facing, helping to solve those problems. MindMate hopes to change the game of mental health support, incorporating advanced AI technology into an intuitive interface meant to ensure that mental well-being is readily available and effective for people seeking assistance with how to manage that aspect of their life.

关键词： Measurement Emotion recognition Mental health Games Aerospace electronics Chatbots Control systems Multilingual Artificial intelligence Resilience

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Machine Learning-based Immune Cell Classification for Enhanced Immunological Analysis

Machine Learning-based Immune Cell Classification for Enhanc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Naveen Pranouv S S Deva Priya Isravel Julia Punitha Malar Dhas Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The automated classification of immune cells plays a vital role in advancing immunological research, diagnostics, and therapeutic monitoring. This paper leverages machine learning and image processing techniques to accurately classify various immune cell types from microscopic images by analyzing cellular features such as shape, size, and texture. The proposed Convolutional Neural Networks (CNN) based system integrates digital pathology workflows to provide a reliable and efficient tool for immune cell classification, reducing reliance on manual inspection. The model trained on a diverse dataset of labeled immune cell images to ensure high classification accuracy and adaptability across different cell types and imaging conditions. The proposed model provides a proactive solution to immunology research challenges, enhancing both the precision and efficiency of immune cell classification.

关键词： Deep learning Pathology Adaptation models Renewable energy sources Accuracy Shape Manuals Streaming media Convolutional neural networks Cancer

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MEMS Design Process Based Simulation and Analysis of a Hypothetical Sensor Design with Electromechanics Interface 2nd

MEMS Design Process Based Simulation and Analysis of a Hypot...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Sathish, K. Ravikumar, C.V. Su, Chunhua Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Deemed University Chennai602105 India School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Division of Computer Science University of Aizu Aizuwakamatsu City Fukushima965-8580 Japan

ISBN: (纸本)9789819785452

This study simulates and examinesthe MEMS design process for a hypothetical sensor with an electromechanical interface. Conceptualizing the sensor entails establishing its purpose, operational environment, and capacitive sensing mechanism for sensitivity and precision. Geometric and physical models are developed using advanced COMSOL Multiphysics software to create a moveable proof mass supported by micro-fabricated beams. Static and dynamic finite element analysis (FEA) simulations can be used to evaluate the sensor’s mechanical reaction to input signals and electrode electrostatic forces. This guarantees that mechanical signals are properly transformed into electrical signals. These data are evaluated to determine the best performance criteria such as sensitivity, linearity, and thermal stability, resulting in iterative design improvements. Robustness analysis, including tolerance and FMEA, ensures dependability. Additionally, typical MEMS manufacturing technologies are being examined for fabrication. This simulation-driven strategy prioritises electro-mechanical interface issues throughout the design process, resulting in high-performance, and dependable MEMS sensors for a wide range of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microchannels

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Advancing Hate Speech Detection: A Multilingual System Using LLAMA-2 for Real-Time Analysis of Audio, Video, and Text Content

Advancing Hate Speech Detection: A Multilingual System Using...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Talha, M.D. Rexie, J.A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331521394

This paper introduces an advanced, multilingual hate speech detection system designed to process various media formats, including audio, video, and text. Unlike traditional detection models that focus solely on text analysis, our system leverages OpenAI's Whisper model to convert audio and video content into text, enabling a broader scope of analysis. At its core, the system utilizes a fine-tuned Llama-2 model specifically trained for hate speech classification. The detection system is accessible through a user-friendly Streamlit web application, providing real-time processing and feedback. Results demonstrate that this comprehensive, multimedia approach significantly enhances detection accuracy and usability, marking a notable advancement in the field. © 2025 IEEE.

关键词： Speech enhancement

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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