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Effective Garbage monitoring System Using GSM Module

Effective Garbage Monitoring System Using GSM Module

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IEEE international conference on intelligent Techniques in control, Optimization and Signal Processing (INCOS)

作者： P Sathish Kumar V Bindhu Sree N Ramanjaneyulu Shaik Nagurvali V Rohith R Sai Teja Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhnagar Tamilnadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Inefficient garbage collection not only leads to overflowing bins and unpleasant but also poses significant environmental and health risks. This paper proposes an effective garbage monitoring system utilizing a GSM module for real-time fill-level detection and automated alerts. The system employs ultrasonic sensors to measure garbage level within the bin, triggering an alert (SMS or data transmission) to designated authorities via the GSM module when a predefined threshold is reached. This enables optimized collection schedules, reducing unnecessary trips, fuel consumption, and associated emissions. Additionally, the system can be integrated with GPS for location tracking, facilitating targeted waste management in geographically dispersed areas. The proposed system offers a cost-effective and scalable solution for enhancing waste management efficiency, promoting environmental sustainability, and improving public health.

关键词： GSM Waste management Schedules Target tracking Ultrasonic variables measurement Signal processing Sensor systems

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A Breif Review on Data-driven Battery Health Estimation Methods for Energy Storage Systems 19

A Breif Review on Data-driven Battery Health Estimation Meth...

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19th IEEE international conference on Networking, Sensing and control, ICNSC 2022

作者： Chen, Minzhi Wu, Hao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781665472432

Battery degradation has an impact on the safety and sustain ability of energy storage systems, which is a consequence of multiple coupled ageing mechanisms. The caused factors include battery chemistry and manufacturing, as well as environmental and operating conditions. Hence, the ageing mechanisms are highly complicated to characterize. The state of health (SOH) of a battery is a commonly used metric to evaluate its aging level. monitoring battery SOH can realize safety and reliable operation of battery management systems. Data-driven methods for battery health estimation and prediction are gaining increasing attention in both academia and industry due to the advantage of avoiding complex physical models. Hence, this paper reviews current state-of-the-art data-driven SOH estimation methods published in 2018-2022, where the variants and extensions of each method existing in current papers are introduced. Finally, the current faced challenges and the solutions are analyzed. © 2022 IEEE.

关键词： Battery management systems

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Enhancing Water Resource Management through IoT-Enabled Smart Water monitoring Systems: A Multi-Agent Algorithm Approach

Enhancing Water Resource Management through IoT-Enabled Smar...

引用

IEEE international Symposium on Innovations in intelligent Systems and Applications (INISTA)

作者： Elton Domnori Donald Elmazi Gledi Tace Computer Engineering Department Universiteti Metropolitan Tirana Tirana Albania Research and Development Center Universiteti Metropolitan Tirana Tirana Albania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

The Internet of Things (IoT), has significantly impacted several industry sectors in recent years, particularly those that deal with air, soil, and water quality. With the growing diversity of sensors and their processing and communication capabilities, a number of methods have been put up for creating systems that continuously gather environmental data. These data can be further processed for long-term analysis or quick response. Every solution that has been put forth presents a static system, meaning that the sensors are positioned in fixed, designer-determined locations. Our work presents a dynamic and autonomous system in which sensors are free to travel within a given area and, by utilizing intelligent algorithms and distributed systems techniques, they determine the optimal way to employ in order to monitor the target environment by combining intelligent algorithms and distributed systems techniques.

关键词： Industries Technological innovation Heuristic algorithms Diversity reception Water quality Soil Sensor systems

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Smart Diagnosis of Active Systems 28th

Smart Diagnosis of Active Systems

引用

28th international conference on Knowledge Based and intelligent information and Engineering Systems, KES 2024

作者： Lamperti, Gianfranco Zhao, Xiangfu Department of Information Engineering University of Brescia Brescia25123 Italy School of Computer and Control Engineering Yantai University Yantai264005 China

Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ′, then δ is ignored (minimality);(2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ′, then δ is generated before δ′ (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions;also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting in an

关键词： Finite automata

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A Novel Electronic Wheel Chair Design using Artificial Intelligence Assisted Smart Sensors and controller

A Novel Electronic Wheel Chair Design using Artificial Intel...

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intelligent Communication Technologies and Virtual Mobile Networks (ICICV), international conference on

作者： Abhay Kumar Manmohan Singh Megha Singh Ramprabu J Alok Dubey Sheetal Mujoo Department of Computer Science and Engineering IES College of Technology Bhopal Department of Computer Science and Engineering Oriental university Indore Deparment Of EEE Kumaraguru College of Technology Coimbatore Department of Preventive Dental Sciences College of Dentistry Jazan University Jazan Saudi Arabia Denartment of Maxillofacial Surgery and Diagnostic Sciences College of Dentistry Jazan University Jazan Saudi Arabia

This research work introduces a design paradigm for an electronic wheelchair, incorporating Artificial Intelligence (AI) assisted smart sensors and controllers to enhance overall functionality and user experience. Core components include advanced sensors for environmental and health data, an intelligent controller driven by AI algorithms, and motorized wheels for achieving seamless mobility. The smart sensors, spanning temperature, ECG, oxygen, and heart rate sensors, contribute to real-time health monitoring and environmental awareness. The AI-assisted controller optimizes wheelchair navigation, providing a responsive and adaptive system. Comparative performance metrics reveal the proposed design's superiority over traditional control methods, demonstrating heightened precision, sensitivity, specificity, and an impressive overall accuracy. User feedback corroborates the success of this innovative design, attaining high ratings for ease of use, health monitoring capabilities, overall comfort, and higher accuracy. This work outlines a transformative approach to electronic wheelchair design, emphasizing cutting-edge technologies to address user needs, enhancing performance standards, and redefine the landscape of mobility assistance and proposed DNN model achieved an accuracy of 0.97.

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Enterprise Pollution Emission monitoring System Based on Deep Learning of Power Data

Enterprise Pollution Emission Monitoring System Based on Dee...

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2022 international conference on Automation, Robotics and computer Engineering, ICARCE 2022

作者： Zhao, Lin Wang, Hui Zhang, Zhen-Yu Feng, Shu-Ming Gu, Wei Shi, Xuan-Xuan Miao, Jia-Wei Jiangsu Electric Power Information Technology Co. Ltd Nanjing China State Grid Lianyungang Power Supply Company Lianyungang China State Grid Nanjing Power Supply Company Nanjing China Nanjing University of Finance&Economics College of Information Engineering Nanjing China

ISBN: (纸本)9781665475488

The current situation of polluting enterprises is that they are numerous and widely distributed. The traditional supervision means only have regular inspection and public reporting, which is difficult to achieve effective monitoring. Based on the existing hardware of intelligent electricity meters, combined with Internet of things technology, Convolution Recurrent Neural Network, Long Short-Term Memory and other technologies, we build an automatic monitoring system for pollution enterprises based on power data. The system can automatically identify different types of equipment according to the load characteristics. By comparing and analyzing the operation of the enterprise's pollutant production equipment and pollutant treatment equipment, it can detect the illegal sewage discharge behavior of enterprises in time. The experimental verification shows that the overall recognition mean square error of the model is only 0.5, and the accuracy of the model is higher than that of RNN model and LSTM model. The system can accurately and timely detect violations, filling the regulatory loopholes of the environmental supervision department for enterprises. © 2022 IEEE.

关键词： Long short-term memory

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The application of intelligent optical sensor networks in industrial automation 5

The application of intelligent optical sensor networks in in...

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5th international conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Yang, Qiwei Chen, Tao Li, Rui Tian, Zhen Fuzhou University Fuzhou China Chongqing University of Posts and Telecommunications Chongqing China University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781510691667

With the advancement of industrial automation, Wireless Sensor Networks (WSNs), particularly those incorporating optical sensing technologies, have shown great potential in areas such as industrial process monitoring and equipment condition management due to their flexible deployment, real-time environmental sensing capabilities, and reliable automation control advantages. This paper comprehensively evaluates the role of wireless sensor networks, with emphasis on optical sensing nodes, in the field of industrial automation through literature review, demand analysis, and analysis of typical application cases. The research shows that compared to traditional wired systems, wireless sensor networks integrated with optical sensing elements enable more flexible layouts and provide real-time, accurate, and reliable environmental monitoring and process control, including precise optical measurements for quality inspection and process optimization, thereby improving production efficiency, safety, and resource utilization efficiency. However, this technology also faces technical challenges such as network reliability, optical signal interference, and system security that need to be overcome. Overall, wireless sensor networks with optical sensing capabilities are driving industrial automation systems towards flexibility, intelligence, and sustainability, despite some technical challenges that remain to be addressed. This study provides practical theoretical support and solution strategy references for the application of wireless sensor network technology with optical sensing in industrial automation. © 2025 SPIE.

关键词： Sensor nodes

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A Smart Home Design with intelligent Patient monitoring Features by Using Internet of Things (IoT) Enabled Communication Technology

A Smart Home Design with Intelligent Patient Monitoring Feat...

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Innovative Computing, intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： P. Prince C. Rajabhushanam Department of Computer Science and Engineering Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The integration of Internet of Things (IoT) technology into smart home systems has revolutionized patient monitoring by providing real-time health insights and automated environmental adjustments. This study focuses on designing and evaluating a smart home system with intelligent patient monitoring features, aimed at enhancing patient safety, comfort, and healthcare delivery. The system employs a network of IoT-enabled devices, including wearable health sensors, environmental monitors, and smart cameras, to track vital signs such as ECG, SPO2, and body temperature, along with environmental parameters like air quality, humidity, and temperature. The system achieved a high accuracy rate, with vital sign sensors demonstrating over 98% reliability across various metrics. A communication framework using Wi-Fi and MQTT protocols ensures seamless data transfer and real-time responsiveness. Edge computing enables low-latency data processing, while cloud analytics provide predictive insights for early health issue detection. Key findings demonstrate the system's efficiency in emergency response, with an average detection-to-alert time of 1.4 seconds, and its ability to adapt the environment based on health conditions with over 95% precision. Automated features such as temperature control, fall detection, and personalized lighting enhance patient care and well-being. This work underscores the transformative potential of smart home technologies in healthcare, paving the way for future advancements in personalized health monitoring systems.

关键词： Temperature sensors Patient monitoring Smart homes Medical services Real-time systems Sensor systems Internet of Things Wearable sensors intelligent sensors Edge computing

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AI based Analysis of Hyperspectral Images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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intelligent & Innovative Practices in Engineering & Management (IIPEM), international conference on

作者： Samarth Yogesh Jadhav Rutuja Rajatam More Bhagyashree Ashok Tingare G. Prasanna Lakshmi Shivam Kolse N.K. Darwante Department of Information Technology Sanjivani College of Engineering Kopargaon MH India Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Electronics and Computer Engineering Sanjivani College of Engineering Kopargaon MH India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control.

关键词： Accuracy Image analysis Visual analytics Surveillance Quality control Real-time systems environmental monitoring Object recognition Artificial intelligence Hyperspectral imaging

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Implementing AI to Enhance Irrigation and Precision Application of Pesticides and Herbicides in Agriculture

Implementing AI to Enhance Irrigation and Precision Applicat...

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IoT Based control Networks and intelligent Systems (ICICNIS), international conference on

作者： P. Malathi K. Senbagam Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The utilization of water, pesticides, and herbicides by producers is being transformed by AI. Precision cultivation enhances sustainability and efficiency. An AI-based irrigation and pesticide/herbicide targeting system is suggested in this paper. Machine learning, computer vision, and IoT sensors comprise the system. The system can distribute water and fertilizers in accordance with the requirements of the crop by monitoring real-time soil moisture, temperature, and insect activity. Machine learning algorithms can forecast optimal irrigation regimens and address insect infestations exclusively in drone-impacted areas, thereby reducing waste and environmental impact. Without affecting crop health or productivity, a pilot study decreased pesticide use by 25% and water usage by 30%. The AI-driven system enhances precision agriculture by promoting sustainable cultivation, reducing costs, and increasing efficiency.

关键词： Irrigation Insects Crops Pesticides Real-time systems Internet of Things Artificial intelligence Sustainable development monitoring Chemicals

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