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共 63738 条记录，以下是271-280 订阅

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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On Identifiability and Model Validation of a Population Balance Model for Liquid-Liquid-Mixing 11

On Identifiability and Model Validation of a Population Bala...

引用

11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Wilms, Terrance Topalovic, Daniel Bliatsiou, Chrysoula Villwock, Jörn Kraume, Matthias Bück, Andreas Knorn, Steffi Control Fak. III Technische Universität Berlin Germany Chemical and Process Engineering Fak. III Technische Universität Berlin Germany Institute of Particle Technology Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

In this paper, liquid-liquid-mixer systems modelled by population balance equations are considered. As the control input / manipulated variable, the stirrer speed (and hence the energy dissipation rate) is used to control the distribution of the particle size. Three different measurement signals are considered. We investigate the observability and identifiability of the system for each measurement option and show that only in one scenario the model is observable and structurally identifiable. Further, it is shown that in this scenario, practical identifiability is generally challenging and particular care must be taken to ensure the model with identified parameter values is able to describe the dynamic behaviour of the system. Copyright © 2025 The Authors.

关键词： Mixers (machinery)

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Home Safety System with Earthquake and Fire Detection

Home Safety System with Earthquake and Fire Detection

引用

Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： Adline Jancy Y Rakshan S Raseem Farhan F Rithik P Department of Electronic and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents innovative approach to home safety combines advanced sensor technology with smart automation to create a robust defense against potential disasters. The system uses a mix of software and hardware components to improve inhabitants' safety and security by delivering timely detection and pertinent alerts. These sensors continuously monitor environmental conditions and seismic activity, providing real-time data to the system. Additionally, a buzzer is incorporated to provide audible alerts, while a servo motor may be employed for physical actions such as closing windows or doors in response to detected threats The system is programmed using the Arduino IDE and embedded C language, allowing for flexibility and customization based on specific user requirements. This paper outlines the installation of a complete safety system intended to identify and address fire and earthquake emergencies in a home environment

关键词： Electric potential automation Microcontrollers Disasters Earthquakes Software Real-time systems Safety Security Usability

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Automated Seizure control Using RFID and Wearable Patch Injector System

Automated Seizure Control Using RFID and Wearable Patch Inje...

引用

Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： Kanmani R Kiren Karthick A Thilagavathi K Agilan VS Amoha J Bharath M Department of ECE SNS College of Technology Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The primary aim of this paper is to use Radio Frequency Identification Technology (RFID) in wristbands to solve medical issues and to improve patient care without causing any inconvenience. Since the seizures are unpredictable, they could happen at any time. Therefore, addressing the cause at the right time with aid assistance may lessen the causes of concern. In order to enable remote data entry and monitoring, each patient will be given an RFID wristband that is linked to entire medical record. In addition to a patch injector that acts as a delay mechanism before seizures begin, the wristband incorporates sensors such as EEG (Electroencephalogram), GSR (Galvanic Skin Response), and accelerometer sensors to detect seizures. Healthcare providers can instantly access the most recent patient information by scanning the RFID band, ensuring prompt and accurate treatment decisions, particularly in emergencies. In the event of a critical change, the system will automatically alert medical staff, enabling a rapid response and lowering the risk of complications.

关键词： Accelerometers Temperature sensors Accuracy Brain modeling Electroencephalography Skin Sensors Telecommunication computing Biomedical monitoring Radiofrequency identification

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A High-Performance 5G/6G Framework for Smart Cities Supporting Multi-Level Artificial Intelligence 2

A High-Performance 5G/6G Framework for Smart Cities Supporti...

引用

2nd international conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Wallace, Jeffrey Vukovic, Miroslav Karimovic, Toni Edwards, William Rocket Technology Systems Llc WashingtonDC United States

ISBN: (纸本)9798331506995

The evolution of Smart Cities hinges on robust 5G/6G networks that provide the foundational infrastructure for real-time, data-driven services and automation across urban landscapes. This paper explores the essential role of advanced communication systems in facilitating smart city applications such as emergency response, mobility services, environmental monitoring, and autonomous vehicles. Leveraging multi-access edge computing (MEC) and multi-level artificial intelligence (AI), the proposed 5G/6G platform supports mission-critical applications requiring low latency, high bandwidth, and reliable connectivity. Through network slicing, dedicated virtual networks are tailored to specific urban services, enabling efficient data management and optimized resource allocation. Additionally, this framework promotes layered AI processing across MEC and cloud nodes, allowing localized decision-making while aggregating data for long-term city planning. The paper further details key use cases and technical architecture diagrams, illustrating the integration of AI, MEC, and network slicing as transformative elements in building resilient, adaptive urban ecosystems. This approach not only enhances public safety, transportation, and resource management but also provides scalable, future-proof solutions for the next generation of urban infrastructure. © 2025 IEEE.

关键词： Mobile edge computing

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Climate Change Prediction Using Cloud computing

Climate Change Prediction Using Cloud Computing

引用

Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： H.R. Narasimha Prabhu Sah DR Supraja P Department of Networking and Communications SRM Science of Institute And Technology Chennai India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Among the most critical and rapidly growing challenges related to climate change, one of its effects makes a significant impact on both ecosystems and human societies. The traditional approach to mitigation of climate change is traditionally related to computational scalability along with processing data in real-time; it usually constrains the system to respond efficiently and timely. In contrast, this research idea proposes using cloud computing technologies from the perspective of AWS Cloud services to handle these challenges by providing scalable and flexible computing resources. This project will focus on accuracy in climate prediction and effective mitigation strategies using advanced techniques of distributed computing and machine learning algorithms. The project will aggregate large-scale environmental data, deploy distributed computing systems using AWS cloud, and integrate machine learning algorithms to provide actionable insights and effective strategies to combat climate change.

关键词： Climate change Cloud computing Scalability Machine learning Data analysis Predictive models

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A Comprehensive Framework Integrating ML, automation Pyramid, and KPIs for Industry 5.0 5th

A Comprehensive Framework Integrating ML, Automation Pyramid...

引用

5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Ponce, Pedro Anthony, Brian Bradley, Russel Xu, Wenhao Isabel Mendez, Juana Molina, Arturo Tecnol Monterrey Inst Adv Mat Sustainable Mfg Monterrey 64849 Nuevo Leon Mexico MIT Dept Mech Engn Cambridge MA 02139 USA Inst Tecnol Tlahuac III Tecnol Nacl Mexico Mexico City DF Mexico

ISBN: (纸本)9783031807596;9783031807602

The manufacturing industry continually seeks advanced technologies to enhance performance per evolving customer requirements. Machine learning (ML) emerges as a pivotal assistive technology essential for strategic integration with Key Performance Indicators (KPIs). Traditionally, KPIs monitor and measure industrial system performance. This paper proposes a framework leveraging KPIs to integrate ML across the automation pyramid in Industry 5.0. The framework enables early detection of malfunctions and areas for improvement, preventing productivity loss. Validated across various industries, the framework demonstrates enhanced operational efficiency, sustainability, and human-centric benefits. Information and communication Technologies advancements facilitate real-time data collection and analysis, aligning with ISO 22400 standards for manufacturing operations management. ML techniques generate actionable insights crucial for sustainable development in industries such as automotive, which require holistic goal assessments. Industry 4.0 marked a significant shift towards automation and data exchange, leveraging IoT, cloud computing, and big data analytics. Industry 5.0 emphasizes human-machine collaboration, customization, and sustainability, evolving KPIs to include worker satisfaction, customization capabilities, and social and environmental impact metrics. This evolution spans various sectors: manufacturing, pharmaceuticals, retail, e-commerce, high-energy-use industries, and consumer goods. ML minimizes downtime, enhances product quality, optimizes supply chains, and improves worker safety by analyzing data from wearables and sensors. Integrating ML with KPIs in Industry 4.0 and 5.0 enables industries to be more efficient, adaptive, and responsive to market and environmental changes, improving decision-making, operational efficiency, and alignment with business and sustainability goals.

关键词： Industry 4.0 Industry 5.0 Machine learning automation pyramid KPIs

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Decentralised Cloud Architecture: Blockchain Driven Integrity and Raptor Codes for Enhanced Data Transmission 14

Decentralised Cloud Architecture: Blockchain Driven Integrit...

引用

14th IEEE international conference on communication Systems and Network Technologies, CSNT 2025

作者： Preethi, M. Kale, Ayush Satheesh, Pooja Harshitha, R. Rohith Vaidya, K. Department of Computer Science PES University Bangalore India

ISBN: (纸本)9798331531935

Ensuring data integrity and security has become integral in the digital era. Blockchain, an immutable ledger can be leveraged to provide data integrity to the cloud system. Centralised cloud systems present challenges in data integrity, reliability and security due to single point of failure and control. The alternative decentralised approach distributes data across multiple nodes thus reducing reliance on one server. However, this can lead to issues like network congestion, difficulty verifying file integrity and origin of tampering. The proposed system uses a combination of decentralised cloud nodes, blockchain, and fountain codes to provide a robust, scalable and secure cloud solution. Blockchain is used to store hashes of the files and validate every operation ensuring transparency and integrity. To provide a robust data recovery and enhance reliability of communication amongst the nodes, fountain codes are utilised for data transmission. © 2025 IEEE.

关键词： Cloud computing architecture

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Accident Liability Assessment Using Traditional Machine Learning Classification Algorithms 4

Accident Liability Assessment Using Traditional Machine Lear...

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4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： D'Souza, Aaron Mishra, Ashutosh Barmon, Bidhan Chandrow Kumar, Jatoth Ravi Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (纸本)9798331530402

This paper focuses on analyzing factors that lead to road accidents, with the main objective of determining if the driver was at fault or not. Road accidents are a major concern that often results in injuries or life-threatening. A considerable amount of these accidents are linked to errors caused by drivers. These errors are generally caused when they engage in non-driving activities which often lead to distractions and increase the chances of accidents. Different parameters are considered to determine the cause of an accident. At the time of the accident, these parameters are collected and they play an important role in determining if it was the driver or other factors responsible for the accident. In this study, we classify accidents by whether they are caused by drivers or by other factors involving them. We assess four machine learning classification models-Logistic Regression, Decision Tree, Random Forests, and XGBoost. We identify the most effective method for classifying driver faults by comparing the performance metrics. This paper provides the evaluation of these machine learning classification techniques and aims to give a foremost model for accident liability that could be helpful in contributing to better prevention strategies. © 2025 IEEE.

关键词： Random forests

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Transforming Ophthalmology: A CNN-Based Mechanism for Diabetic Retinopathy Screening 4

Transforming Ophthalmology: A CNN-Based Mechanism for Diabet...

引用

4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Vinod, Dasari Naga Sai Varshim, N. Shukriya, M. Sai Manusha, G. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331530402

Diabetic Retinopathy (DR) is a solemn eye ailment that can root vision loss if not diagnosed. It is a complication of diabetes and is caused when high blood sugar destroys the retina. While DR cannot be reversed, early detection and treatment can prevent or slow down vision loss. Traditional physical prognosis of DR by ophthalmologists can be inefficient, prone to errors, and expensive. Computer-aided finding systems, especially those using deep learning mechanisms, have produced great promise by enhancing the accuracy of DR recognition. Convolutional Neural Networks (CNNs) are a particular kind of deep learning mechanism that has been highly successful in analyzing medical images. In this study, we achieved a 95% accuracy in detecting DR using a CNN-based approach. © 2025 IEEE.

关键词： Convolutional neural networks

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