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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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International conference on Intelligence in Next Generation Networks (ICIN)

作者： Sarantis Kalafatidis Dimitrios Tsiktsiris Dimitrios Trakos Antonios Lalas Konstantinos Votis Dimitrios Tzovaras Centre for Research and Technology Hellas / Information Technologies Institute Greece

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. Swarm-Catcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher's potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher's architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety.

关键词： Cloud computing Technological innovation 5G mobile communication Surveillance Prevention and mitigation Autonomous aerial vehicles Public security Real-time systems Critical infrastructure systems support

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14th International conference on applications and Techniques in Information Security, ATIS 2024

14th International Conference on Applications and Techniques...

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14th International conference on applications and Techniques in Information Security, ATIS 2024

ISBN: (纸本)9789819797424

The proceedings contain 24 papers. The special focus in this conference is on applications and Techniques in Information Security. The topics include: Optical Neural Networks – A Strategy for Secure Quantum Computing;guarding Against Quantum Threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers Through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge Devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning Algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent Threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT Threat Intelligence: Revealing advanced Persistent Threat Attack Patterns;Adaptive Data-Driven LSTM Model for sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic Algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge Devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and Information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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SmartPosture: Real-Time Posture Detection and Feedback System Integrating IoT and Machine Learning

SmartPosture: Real-Time Posture Detection and Feedback Syste...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Milind Rane Shrihari Mohole Yash Pahade Gokul Kurandale Aditya Munjale E&TC VIT Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Traditional posture monitoring methods often rely on physical correction, lacking advanced technologies for real-time detection and feedback, which can result in delayed interventions. This research presents a machine learning framework designed to address this deficiency by giving instantaneous posture detection and feedback. The framework categorizes user posture into “Good” or “Bad” classifications, achieving an accuracy rate of 75.52%, by the use of Convolutional Neural Networks (CNNs) along with TensorFlow. Trained on a 2,000-image Yoga Poses Dataset and fine-tuned over 50 epochs, the model achieved optimal performance. The system integrates IoT and machine learning, providing recommendations for posture -related advice through a medical chatbot powered by techniques of NLP like Langchain and Hugging Face embeddings. Key hardware components include the flexible sensor, LCD display, vibration motor, and ESP8266 Wi-Fi module, which offer real-time alerts and monitoring. The web application, built using Flask and Streamlit, allows users to track posture data and receive corrective prompts. This innovative approach not only enhances posture monitoring but also advances smart healthcare, offering a continuously learning and adaptable solution relevant to real-world scenarios.

关键词： Vibrations Machine learning Medical services Chatbots Motors Real-time systems Liquid crystal displays Convolutional neural networks Monitoring Wireless fidelity

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Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Multimodal-to-Text Prompt Engineering in Large Language Mode...

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IEEE conference on Wireless Communications and Networking

作者： Harshith Manjunath Lucas Heublein Tobias Feigl Felix Ott Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability and efficiency, LLMs have not been extensively explored for signal processing tasks, particularly in the domain of global navigation satellite system (GNSS) interference monitoring. GNSS interference monitoring is essential to ensure the reliability of vehicle localization on roads, a critical requirement for numerous applications. However, GNSS-based positioning is vulnerable to interference from jamming devices, which can compromise its accuracy. The primary objective is to identify, classify, and mitigate these interferences. Interpreting GNSS snapshots and the associated interferences presents significant challenges due to the inherent complexity, including multipath effects, diverse interference types, varying sensor characteristics, and satellite constellations. In this paper, we extract features from a large GNSS dataset and employ LLaVA to retrieve relevant information from an extensive knowledge base. We employ prompt engineering to interpret the interferences and environmental factors, and utilize t-SNE to analyze the feature embeddings. Our findings demonstrate that the proposed method is capable of visual and logical reasoning within the GNSS context. Furthermore, our pipeline outperforms state-of-the-art machine learning models in interference classification tasks. Github: https://***/darcy_gnss

关键词： Global navigation satellite system Accuracy Large language models Knowledge based systems Pipelines Interference Feature extraction Prompt engineering Monitoring Context modeling

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Optimized IRS Positioning for Phase-Tuned Wireless Physiological Motion Detection

Optimized IRS Positioning for Phase-Tuned Wireless Physiolog...

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IEEE MTT-S International Microwave Bio conference ((IMBIOC)

作者： Denny Vishnu Puri Landika Mohammad Shadman Ishrak Haofan Cai Alvin Yang Yao Zheng Khaldoon Ishmael Olga Borić-Lubecke Victor Lubecke University of Hawai'i Mānoa USA

ISBN: (数字)9798331510473

ISBN: (纸本)9798331510480

This paper explores the impact of physical positioning of Intelligent Reflective Surfaces (IRS) on enhancing physiological motion detection in non-line-of-sight (NLOS) millimeter-wave (mmWave) integrated sensing and communication (ISAC) systems. Operating at 28 GHz with an Orthogonal Frequency Division Multiplexing (OFDM) scheme, the proposed system incorporates a static IRS whose physical placement is systematically adjusted to optimize signal propagation and phase alignment. Experimental results reveal that precise IRS positioning significantly improves detection accuracy, with peak-to-peak (P2P) phase shifts increasing by 57 % in the perpendicular configuration and 56 % in the parallel configuration, corresponding to optimal signal reflections. These findings demonstrate the potential of physically repositioning IRS to achieve superior sensitivity and robustness in wireless physiological sensing, paving the way for advanced positioning algorithms and applications in complex multipath environments.

关键词： Wireless communication Wireless sensor networks Sensitivity OFDM Integrated sensing and communication Physiology Reflection Motion detection Millimeter wave communication Tuning

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Real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： V. Usha Sandeep Karagatla P. Sheela Gowr P.M.G Jegathambal A. Lizy V. Sathya Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE (AI&DS) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Vels Institute of Science Technology & Advanced Studies Chennai Dept. of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala (R&D) Institute of Science and Technology Chennai India Dept of Information Technology Velammal Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Internet of Things (IoT) grounded solution of Real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments.

关键词： Animals Tracking Heart beat LoRa Real-time systems Mobile applications Safety Internet of Things Monitoring Global Positioning System

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Touch Panel-Based Industrial Device Management System for Persons with Disabilities

Touch Panel-Based Industrial Device Management System for Pe...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： S. Jaganathan Muralidharan V Harish K Ajay Kumar M Gokul R Department of Electrical and Electronics Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The incorporation of touch screens in the control and monitoring of Industrial systems has substantially improved operational efficiency and user adaptability. This paper describes, developing and implementing an affordable scalable industrial device management system based on the ATmega328P microcontroller and a 7 inch DWIN touchscreen. The system allows real-time interactions with industrial devices such as sensors, actuators, and controllers, enabling powerful real-time monitoring and intuitive control of complicated processes. The ATmega328P is used as the CPU of the system and is the interfacing unit with industrial sensors for temperature, humidity, and pressure data, thereby allowing high precision real time data collection. Monitoring and controlling operations are performed from an advanced graphical user interface designed for the DWIN touch display. The GUI is designed to include customizable widgets such as active buttons, data panels, and gauges, enhancing user interaction. The communication between the microcontroller and the display is realized by robust serial communication and ensures an easy and reliable transfer of control and monitoring information. The system is intended to be modular, scalable and flexible for various industrial applications. By focusing on low-cost system implementation and including user-friendly design elements and real-time access to data, the solution makes a promising impact on the operational inclusiveness of the system particularly for people with physical disabilities. This work emphasizes the game-changing prospects of modern industrial automation and control systems through the integration of simplified hardware and complex software systems.

关键词： Temperature sensors Industries Automation Microcontrollers Assistive technologies Touch sensitive screens Real-time systems sensor systems sensors Graphical user interfaces

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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AI-Driven Predictive Maintenance for Industrial IoT with Real-Time Fault Detection and Prediction

AI-Driven Predictive Maintenance for Industrial IoT with Rea...

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International conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： A.Hency Juliet Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Predictive maintenance for industrial machinery uses data analysis and advanced techniques to forecast equipment failures. This proactive approach schedules maintenance to minimize downtime and optimize operational efficiency. Achieving accurate, consistent, and well-integrated IoT sensor data is difficult, as poor data quality can result in inaccurate predictions and false alerts. Scaling real-time fault detection and prediction requires substantial computational power and optimized algorithms to process the continuous influx of data from multiple machines. To address data quality and integration issues while enhancing real-time processing and scalability, a Custom Neural Network technique can be used to improve IoT sensor data accuracy and consistency, and efficiently manage the computational demands of continuous data influx for fault detection and prediction. The proposed system, leveraging an extended Neural Network technique, enhances prediction accuracy by efficiently managing diverse industrial datasets and improves real-time fault detection through optimized data processing and reduced latency. The model demonstrated a remarkable accuracy of 92.86%, showcasing its ability to accurately identify both faulty and non-faulty equipment. With a precision of 90.91%, a perfect recall of 100%, and an F1 score of 95.24%, the model exhibited a well-balanced fault detection performance, minimizing false positives while ensuring thorough fault identification, all while processing in real-time with an efficient time complexity of 2.49 seconds.

关键词： Fault diagnosis Accuracy Fault detection Scalability Data integrity Neural networks Real-time systems Time complexity Predictive maintenance Industrial Internet of Things

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Smart Low-Cost Sensing Solutions for Enhanced Behind-the-Meter Visibility of Grid Edge Renewable Energy Sources

Smart Low-Cost Sensing Solutions for Enhanced Behind-the-Met...

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Grid Edge Technologies conference & Exposition (Grid Edge), IEEE PES

作者： Mehrnaz Madadi Subhashish Bhattacharya Yang-Duan Su Paul Ohodnicki G. Subramania J. Pillars T. Monson N. Gurule North Carolina State University Raleigh NC USA University of Pittsburgh Pittsburgh PA USA National Laboratory SNL Albuquerque NM USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Behind-the-meter (BTM) visibility poses a signifi-cant challenge for utilities, especially when conducting area load forecasts, as the operations of distributed energy resource (DER) generation and storage significantly influence the net load. The expense of advanced sensors and budget constraints are key barriers to improving visibility at the grid edge. This paper addresses these challenges by introducing three new low-cost non-intrusive sensing solutions. These solutions offer real-time, high-resolution visibility of grid-edge power flow and health metrics, facilitating applications such as volt/var control and state-of-health estimation for BTM components. Each sensor is equipped for wireless data communication via radio frequencies. Experimental results confirm the feasibility and performance of the proposed sensors in measuring target parameters and transmitting data wirelessly to the aggregator. Additionally, the use of existing commercial micro Phasor Measurement Units $(\boldsymbol{\mu} \mathbf{PMUs})$ as data aggregators in distribution feeders is explored, showing comparable real-time monitoring capabilities to traditional sensors at a significantly lower cost.

关键词： Wireless communication Measurement Wireless sensor networks Renewable energy sources Load forecasting Real-time systems Phasor measurement units Distributed power generation Planning Intelligent sensors

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