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

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Modelling of advanced sensor based Distributed Control System for Real- Time Prediction and Minimization of Power Loss in Industrial Automation systems

Modelling of Advanced Sensor based Distributed Control Syste...

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Expert Clouds and applications (ICOECA), International conference on

作者： D. Sengeni M. R. Mano Jemila G. Soorya Priya T. Santhana Krishnan G. Rathinasabapathi Sathish Kannan Department of Electronics and Communication Engineering CK College of Engineering and Technology Chellangkuppam Cuddalore Tamil Nadu India Department of Biomedical Engineering Dr. N. G.P. Institute of Technology Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering Loyola Institute of Technology Mevalurkuppam Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Rajalakshmi Engineering College (Autonomous) Thandalam Chennai Tamil Nadu India Department of Mechanical Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

This research study analyses the integration of a Distributed Control System (DCS) to optimise the efficiency and reliability of industrial automation, with a special emphasis on reducing power loss. Leveraging real-time sensor data and machine learning models, including Decision Trees, Artificial Neural Networks, and Random Forests, the research provides a proactive strategy to anticipating and avoiding anomalous current and voltage patterns. The DCS, armed with predictive maintenance procedures, emerges as a solid framework for reducing energy usage, assuring system stability, and limiting possible quality losses. Evaluation using performance ratings and confusion matrices reveals the efficiency of the proposed system in properly recognising and correcting deviations from normal operating circumstances. The results contribute not only to the progress of distributed control systems in industrial automation but also provide a realistic approach to real-time monitoring and predictive maintenance, supporting sustainability and operational excellence in manufacturing settings. This study sets a platform for future initiatives seeking to construct intelligent, adaptable systems for increased industrial automation performance.

关键词： Automation Decentralized control Predictive models Solids Real-time systems Stability analysis Reliability

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Wireless Micro/Nanobots For Cancer Detection and Remote Sensing

Wireless Micro/Nanobots For Cancer Detection and Remote Sens...

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International conference on Computing and Networking Technology (ICCNT)

作者： G Mathangi M Shunmugathammal S Maheswaran P Jeyanthi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Microbots have proven their usefulness in a range of biomedical applications, including targeted medication administration, tissue repair, regeneration, and assisted fertilization. This is because of their remarkable capacity to maneuver through intricate biological medium or small blood vessels. Cancer is still a major worldwide health concern because it is frequently discovered at an advanced stage, when there are few effective treatment choices. This paper explores the potential of nanobots, microscopic robots on the nanometer scale, for revolutionizing early cancer detection. This paper provides an overview of the latest advancements in micro- and nanobot technology and their applications in remote sensing, including how they generate signals, behave in the outside world, and are used in a variety of settings. Its potential paths are also discussed in this research, including how it will be utilized in real-time and sensing in complex contexts.

关键词： Wireless communication Wireless sensor networks Navigation Robot sensing systems Cancer detection Real-time systems sensors Nanobioscience Remote sensing Cancer

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Smart Fish Feeding in IoT-based Aquarium: A Comprehensive Approach Integrating Computer Vision, Machine Learning and sensor Technology

Smart Fish Feeding in IoT-based Aquarium: A Comprehensive Ap...

引用

International conference on Next Generation Computing applications (NextComp)

作者： Ryan Cassy Girish Bekaroo Middlesex University Mauritius Mauritius

ISBN: (数字)9798331517403

ISBN: (纸本)9798331517410

With the increasingly busy lifestyles of people, advanced maintenance systems such as automated feeding has become of utmost importance to aquarium owners. Despite existing technologies, there remains a gap in smart feeding solutions tailored to the specific dietary requirements of different fish species and population sizes. This paper investigates smart fish feeding through automated detection, counting, classification and feeding of fishes within aquariums. As part of this paper, a smart population-driven aquarium maintenance system, named AquaTrack, is proposed that integrates Internet of Things (IoT), computer vision, machine learning, and sensor technologies. The system detects, classifies, and counts fish, adjusting feeding amounts based on species and population to mitigate the risks of overfeeding and underfeeding, which can significantly affect water quality. The system was evaluated through four research questions, where a general accuracy of 76.9% in fish species identification and 77.1% in fish counting were found. While promising outcomes were observed under specific conditions, variability and limitations in performance across different scenarios were also noted. Environmental factors such as lighting, reflections, shadows, and particulate matter affected the accuracies, particularly in scenarios involving smaller fish quantities. Based on these findings, the study recommends further refinement of the system to enhance its capabilities.

关键词： Computer vision Accuracy Machine learning Water quality Fish Reflection Maintenance Internet of Things Next generation networking Monitoring

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Potentials of Learning Entropy for Sub-Nyquist and Sub-Noise Anomaly Detection

Potentials of Learning Entropy for Sub-Nyquist and Sub-Noise...

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SICE Annual conference

作者： Ivo Bukovsky Witold Kinsner Katerina Marzova Noriyasu Homma University of South Bohemia in CB Ceske Budejovice Czech Republic CESNET Prague Czech Republic University of Manitoba Winnipeg Canada Czech Technical University in Prague Prague Czech Republic Tohoku University Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

Recently, we have noted the intriguing ability of Learning Entropy (LE) with Higher-Order Neural Units (HONUs) to detect abrupt frequency changes in both sub-Nyquist signals and pure noise signals. The concept of the LE and its fundamental element, the Learning Information (LI), is revised and extended. The LI is presented as the logical counterpart of Shannon's probabilistic information and the LE as its aggregation, e.g. as an average. The approach emphasizes the computational efficiency and analyzability of HONUs with gradient learning, which implies minimal computational and energy requirements for real-time computations. For pure noise, we show that bandpass filtering and principal components improve LE performance to immediately detect frequency changes below the noise level. By demonstrating the ability of LE with linear units to detect anomalies in undersampled signals and also in pure noise, we show the potential of LE for industrial applications and perhaps also for research related to future digital communication systems or advanced analysis of data from distributed sensors in optical transmission infrastructures.

关键词： Optical filters Noise sensor systems and applications Probabilistic logic Entropy Real-time systems Computational efficiency Optical sensors Optical noise Nonlinear optics

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An Embedded AI-Based Smart Intrusion Detection System for Edge-to-Cloud systems 1st

An Embedded AI-Based Smart Intrusion Detection System for Ed...

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1st International conference on Cryptology, Coding Theory and Cyber Security (I4CS)

作者： Shrivastwa, Ritu-Ranjan Bouakka, Zakaria Perianin, Thomas Dislaire, Fabrice Gaudron, Tristan Souissi, Youssef Karray, Khaled Guilley, Sylvain Secure IC SAS 15 Rue Claude ChappeBat B F-35510 Cesson Sevigne France

ISBN: (纸本)9783031232008;9783031232015

This article proposes a general purpose IoT framework usually applicable to all Edge-to-Cloud applications and provides an evaluation study on a use-case involving automotive V2X architecture, tested and verified on a toy smart-car in an emulated smart-car environment. The architecture in study is finely tuned to mimic actual scenarios and therefore the sensors available on the toy car encompasses almost all the sensors that assist a regular ADAS in smart cars of today. The cloud connectivity is maintained through the CoAP protocol which is a standard IoT connectivity protocol. Finally, the security solution proposed is that of a smart Intrusion Detection System (IDS) that is built using Machine Learning (ML) technique and is deployed on the edge. The edge IDS is capable of performing anomaly detection and reporting both detection results as well as sensor collected big data to the cloud. On the cloud side the server stores and maintains the collected data for further retraining of ML models for edge anomaly detection which is differentiated into two categories viz. sensor anomaly detection model and network anomaly detection model. To demonstrate Software update Over The Air (SW-OTA) the cloud in the evaluation setup implements a ML model upgrade capability from the cloud to the connected edge. This implementation and evaluation provides a Proof-of-Concept of the choice of ML as IDS candidate and the framework in general to be applicable to various other IoT scenarios such as Healthcare, Smart-home, Smart-city, Harbour and Industrial environments, and so on, and paves way for future optimization studies.

关键词： Edge computing Artificial intelligence Cybersecurity services Embedded security Anomaly detection Intrusion detection systems V2X Internet of Things advanced Driver Assistance systems) (ADAS)

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Application of Artificial Intelligence Techniques to Detect Water Stress

Application of Artificial Intelligence Techniques to Detect ...

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ACS/IEEE International conference on Computer systems and applications

作者： Marwa Dorai Mehrez Abdellaoui Bouthaina Douh Ali Douik NOCCS Laboratory National Engineering School of Sousse University of Sousse Higher Institute of Computer Science and Communication Technologies University of Sousse Higher Institute of Agronomic Sciences of Chott Mariem University of Sousse

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Water stress is a critical issue that significantly affects agricultural productivity worldwide, particularly in the context of climate change and diminishing water resources. Conventional methods for detecting water stress, such as visual inspections and ground sensors, often lack the precision, costeffectiveness, and scalability needed for modern agricultural demands. Recent advances in artificial intelligence (AI) offer innovative solutions by leveraging complex data from multiple sources. This research aims to develop a multimodal AI model that integrates satellite imagery, drone data, and ground IoT sensors to improve the accuracy and timeliness of water stress detection. By combining Convolutional Neural Networks (CNNs) for image analysis, machine learning algorithms for sensor data interpretation, and advanced data fusion techniques, the proposed model captures water stress indicators from various perspectives, enhancing sensitivity and specificity. The expected outcomes include significant improvements in precision and responsiveness compared to traditional single-source methods. This approach aims to optimize irrigation, minimize crop losses, and support sustainable water management, providing a valuable tool for precision agriculture.

关键词： Artificial intelligence Multimodal sensors Machine learning Water monitoring Water resources Agriculture Detection algorithms Climate change Stress measurement Intelligent sensors Data models sensor fusion Sensitivity and specificity Scalability Convergence of numerical methods Irrigation Machine learning Precision agriculture

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Electronic Health Monitoring System with Live Data Tracking using IoT

Electronic Health Monitoring System with Live Data Tracking ...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International conference on

作者： Vipin Bondre Abhishek Khandare Anurag Bhoyar Swamini Patil Nikita Bundhele Shweta Bondre Department of Electronics and Telecommunication YeshwantraoChavan College of Engineering Nagpur India Department of Information Technology Shri Ramdeobaba College of Engg.&Mangt. Nagpur India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

E-healthcare monitoring systems play a pivotal role in revolutionizing healthcare by leveraging advanced technologies to improve overall healthcare results, expedite medical procedures, and increase patient care. This research paper provides a thorough analysis of the current state of the art in e-healthcare monitoring systems, examining the underlying technologies, challenges, and future perspectives. In cutting-edge applications the Internet of Things (IoT) is critical to many areas, including smart cities, healthcare, education, transportation, and defense operations. The healthcare sector benefits greatly from IoT applications because they provide safe, real-time remote patient monitoring, which raises people’s quality of life. The Internet of Things is used in this review study to look at recent advancements in healthcare monitoring systems. The article examines the advantages of IoT-based healthcare systems in relation to their relevance. This research study provides a thorough overview of current research on IoT-based healthcare monitoring systems through a review of the literature. The literature review compares the effectiveness, efficiency, privacy, security, monitoring, and data protection of different *** addition to examining Internet of Things monitoring options based on wearable and wireless sensors, the study provides taxonomy of healthcare monitoring sensors. This study discusses about the challenges and outstanding issues related to Quality of Service (QoS), Security and Privacy in the healthcare industry. Recommendations and concepts for Internet of Things (IoT) healthcare applications are provided at the study’s conclusion, along with future directions for a number of ongoing technical advancements.

关键词： Reviews Medical services Quality of service sensor systems Real-time systems Internet of Things Time factors Security Monitoring Wearable sensors

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Toward All-Digital Time-Domain Neural Network Accelerators for In-sensor Processing applications

Toward All-Digital Time-Domain Neural Network Accelerators f...

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Norchip

作者： Ahmed M. Mohey Marko Kosunen Jussi Ryynänen Martin Andraud Department of Electronics and Nanoengineering Aalto University Espoo Finland

Deep Neural Network (DNN) accelerators are increasingly integrated into sensing applications, such as wearables and sensor networks, to provide advanced in-sensor processing capabilities. Given wearables’ strict size and power requirements, minimizing the area and energy consumption of DNN accelerators is a critical concern. In that regard, computing DNN models in the time domain is a promising architecture, taking advantage of both technology scaling friendliness and efficiency. Yet, time-domain accelerators are typically not fully digital, limiting the full benefits of time-domain computation. In this work, we propose a time-domain multiply and accumulate (MAC) circuitry enabling an all-digital with a small size and low energy consumption to target in-sensor processing. The proposed MAC circuitry features a simple and efficient architecture without dependencies on analog non-idealities such as leakage and charge errors. It is implemented in 22nm FD-SOI technology, occupying 35 μm×35 μm while supporting multi-bit inputs (8-bit) and weights (4-bit). The power dissipation is 46.61 μW at 500MHz, and 20.58 μW at 200MHz. Combining 32 MAC units achieves an average power efficiency, area efficiency and normalized efficiency of 0.45 TOPS/W and 75 GOPS/mm 2 , and 14.4 1b-TOPS/W.

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Design and Development of Pulse Rate Monitoring System Powered by STM32 Microcontroller

Design and Development of Pulse Rate Monitoring System Power...

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IoT Based Control Networks and Intelligent systems (ICICNIS), International conference on

作者： Kalpana Murugan M. Lorate Shiny Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Dayananda Dagar University Bangalore

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

With the advent of sophisticated sensor networks and protocols, the biomedical field has undergone significant revolution which diagnosis the risk factors of human at early stage. The conventional devices employed for health monitoring is progressively being replaced by advanced sensors and controllers. Accounting for such replacements and evolving technologically disruptive industrial applications, an attempt is being made in this research to design and develop a pulse rate monitoring system of patients using STM32. The developed sensors is built on Audino Uno platforms with provisions for encryptions and reliable cyber security features. This module developed on a laboratory scale illustrate higher degree of sensitivity and reproducibility. Provisions are created to ensure data security which also involves elimination of noises and redundancy. Additionally. The device is equipped with Bluetooth to transmit the data to view it in the host system for regular monitoring and subsequent actions to be taken if any.

关键词： Heart rate Wireless communication Bluetooth Sensitivity Microcontrollers Termination of employment Real-time systems Skin Reproducibility of results Monitoring

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Implementation Of Humanoid Robot Using Arduino Mega

Implementation Of Humanoid Robot Using Arduino Mega

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Smart Technologies, Communication and Robotics (STCR)

作者： Likitha G Shivapanchakshari T G Moulya Reddy NR Manasa A Meghana A Sudha M S Electronics and Communication Engineering Cambridge Institute Of Technology K R Puram Banglore India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Humanoid robots are advanced robotic systems designed to mimic human structure and movement. The robot is designed to move and perform tasks based on commands received wirelessly through a Bluetooth module (HC-05). It combines servo motors for limb movement and DC motors for wheeled navigation, controlled via an L298N motor driver. The system is powered by rechargeable batteries, ensuring continuous operation. The robot responds to user inputs via a mobile application or controller, enabling precise and flexible movement. The Arduino Mega processes real-time commands, ensuring smooth operation and quick response. The absence of an ultrasonic sensor simplifies the design while maintaining functionality through motor control. Bluetooth connectivity allows for remote control, making the system suitable for educational, automation, and assistive applications. This project showcases the integration of robotics and embedded systems for smart, wireless control.

关键词： Wireless communication Bluetooth Embedded systems Navigation Humanoid robots Robot sensing systems DC motors Mobile robots Servomotors Remote control

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