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Water Quality Monitoring and Cleaning System

Water Quality Monitoring and Cleaning System

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International conference on Communication, Computing and Internet of Things (IC3IoT)

作者： B. Puviyarasi P. Gubhenthiran P. Keerthana S. Megala Electronics and Instrumentation Engineering Sri Sairam Engineering college Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper shows a strategy for the growing need for water resources, making it necessary to maintain the sustainability and quality of these essential ecosystems. This paper introduces a sophisticated Water Quality Monitoring and Cleaning System designed to revolutionize the way we perceive, assess, and safeguard our water bodies. The primary goal is to develop an integrated platform that seamlessly combines cutting-edge sensor technologies, mobile applications, and intelligent alert systems to address the challenges associated with water quality monitoring and purification. The primary aim of this paper is to establish an effective and user-centric system that provides real-time insights into water quality parameters. This system includes advanced sensors and data is collected and transmitted to a secure Firebase database. A feature-rich mobile application empowers users to visualize, analyze, and interpret current and historical water quality data. Additionally, an intelligent alert system ensures immediate response to potential water quality deviations, promoting timely intervention.

关键词： Ecosystems Water quality Water conservation Real-time systems Water pollution Mobile applications Telecommunication computing

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Giant Magnetoimpedance Microwires for sensor applications

Giant Magnetoimpedance Microwires for Sensor Applications

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8th International conference on Antennas and Electromagnetic systems, AES 2022

作者： Zhukov, Arcady Corte-León, Paula González-Legarreta, Lorena Ipatov, Mihail Blanco, Juan M. Zhukova, Valentina Dpto. Fisica de Materiales Fac. Quimicas UPV/EHU Paseo Manuel Lardizabal 3 San Sebastian20018 Spain Dpto. de Física Aplicada EIG UPV/EHU Europa Plaza 1 San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48011 Spain

The influence of post-processing (annealing and stress-annealing) on the Giant magnetoimpedance (GMI) effect in thin magnetic microwires is reviewed. High GMI effect has been observed in Co-rich magnetic microwires with vanishing magnetostriction coefficient. Post-processing including annealing or stress-annealing at adequate conditions allows further improvement of GMI effect in Co-rich magnetic microwires. © 2022 advanced Electromagnetics Symposium. All rights reserved.

关键词： Annealing

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Intelligent Fruit Quality Monitoring System using sensor Networks and Deep Learning

Intelligent Fruit Quality Monitoring System using Sensor Net...

引用

Smart Electronics and Communication systems (ISENSE), International conference on

作者： Arvind Kumar N. Jaya Krishna Mahesh Chandra Rampravesh Kumar Jaya Prakash Sahoo N. Poojitha Biswajit Karan Kamlesh Kishore Department of EECE Gitam University Bengaluru India School of ECE Reva University Bengaluru India Department of EEE Sandip University Nashik India Department of EEE University of Stellenbosch Stellenbosch South-Africa Department of ECE RTC Institute of Technology Bengaluru India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This paper presents a novel approach for fruit quality detection using a multi-modal system that combines sensor data and image processing techniques with deep learning. In the first stage of our experiment, we employed various sensors to measure temperature, gas levels, and moisture content using DHT 11 and MQ-135 sensors. These sensors provide valuable insights into the environmental conditions surrounding the fruits, which are crucial indicators of their freshness. In the second stage, we utilized image processing algorithms to pre-process fruit images and train a SqueezeNet convolutional neural network (CNN) to classify fruits into two classes: fresh and rotten. By integrating sensor data with image-based classification, our proposed method offers a comprehensive solution for real-time fruit quality assessment. Experimental results demonstrate the effectiveness of our approach in accurately detecting fruit quality, paving the way for advanced applications in food safety and quality control.

关键词： Deep learning Temperature measurement Temperature sensors Accuracy Quality control Robustness Food safety Quality assessment Convolutional neural networks Monitoring

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Optimized Bidirectional Long Short-Term Memory Model for Distributed Denial of Service Attack Detection in Wireless sensor Networks

Optimized Bidirectional Long Short-Term Memory Model for Dis...

引用

Smart Technologies and systems for Next Generation Computing (ICSTSN), International conference on

作者： K Poongodi S Mithuna S Rajkumar Computer Science and Engineering K. S. Rangasamy College of Technology India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

The rapid expansion of Wireless sensor Networks (WSNs) has made them a critical component in various applications, from environmental monitoring to military surveillance. However, their inherent vulnerabilities make them prime targets for Distributed Denial of Service (DDoS) attacks, that have the potential to significantly impair data integrity and network performance. This paper presents an advanced approach for DDoS detection in WSNs using an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) network. The proposed method leverages the strengths of Bi-LSTM in capturing temporal dependencies and patterns within network traffic data, providing a robust mechanism for identifying anomalous activities indicative of DDoS attacks. To enhance the detection accuracy and efficiency, introduce a novel optimization technique that fine-tunes the BiLSTM parameters, balancing the trade-offs between detection performance and computational overhead. Extensive simulations demonstrate that our optimized Bi-LSTM model significantly outperforms traditional machine learning approaches and standard LSTM configurations. The results underline the potential of this approach in fortifying WSNs against sophisticated DDoS threats, ensuring reliable and secure network operations.

关键词： Wireless communication Wireless sensor networks Computational modeling Surveillance Telecommunication traffic Denial-of-service attack Security

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Highly sensitive temperature sensor with optomechanofluidic resonators 11

Highly sensitive temperature sensor with optomechanofluidic ...

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conference on advanced sensor systems and applications XI

作者： Liu, Yize Jiang, Junfeng Liu, Kun Wang, Shuang Niu, Panpan Wang, Tong Liu, Tiegen Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China Tianjin Univ Minist Educ Key Lab Optoelect Informat Technol Tianjin 300072 Peoples R China Tianjin Univ Inst Opt Fiber Sensing Tianjin Opt Fiber Sensing Engn Ctr Tianjin 300072 Peoples R China

ISBN: (数字)9781510646520

ISBN: (纸本)9781510646520;9781510646513

Microfluidic optomechanical device are a unique optofluidics platform that can exhibit optomechanical oscillation in the 10-20 MHz, driven by radiation pressure (RP). The resonant enhancement of both mechanical and optical response in microcavity optomechanical devices allows exquisitely sensitive measurements of environment stimuli (pressure, force, sound speed change) and non-solid states of matter (freely flowing particles, viscous fluids). In this work, we experimentally investigate temperature tuning of these hollow-shell oscillators. We also demonstrate the effect of temperature on the frequency domain of optical machine oscillation resonance shift and applied it to the field of temperature sensing. Our result is a step towards optomechanical sensor in the field of temperature.

关键词： Optomechanical oscillation Temperature sensor Whispering gallery mode

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Wearable Web Enabled IoT-Based Health Monitoring Framework

Wearable Web Enabled IoT-Based Health Monitoring Framework

引用

advanced Computing and Emerging Technologies (ACET), International conference on

作者： Neha Gupta Shruti Jha Vritti Sachdeva Ritika Sharma School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad Haryana

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The healthcare industry is one of the domains where the Internet of Things (IoT) has made the global impact. By intelligently integrating applications into medical devices, IoT enables the provision of smart and efficient healthcare facilities. Through such sensors, which can be either surgically placed or worn on the body, patients can be monitored continually using the advanced technologies. Such data collection allows for the analysis, mining, and aggregation of information to facilitate early disease prediction. Sophisticated remote health monitoring systems have been developed due to the availability of cheap smart phones and wearable sensors. Even if doctors/pharmacists are unavailable, such health monitoring IoT solutions can be helpful in disease prevention/management and accurate patient diagnosis. In this paper, a novel IoT-based wireless remote health monitoring system for a patient in an indoor environment is presented for monitoring continuous patient vital sign data like temperature and pulse. The collected data is stored on an IoT platform so that only the authorised person can obtain the data. This makes it possible for the doctors to diagnose conditions from the information that has been collected, and this improves the chances of offering effective care at the right time.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Internet of Things Communication system security Biomedical monitoring Monitoring Wearable sensors Diseases

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Purushotham Endla R. Mohandas Achinta Saikia C. Lakshmi G Karthikeyan Tejashree Tejpal Moharekar Department of Physics School of Sciences and Humanities SR University Warangal Telangana India Department of ECE Chennai Institute of Technology Chennai Department of Statistics Mariani College Jorhat Assam Saveetha Engineering College Chennai Department of Artificial Intelligence & Data Science Karpagam College of Engineering Coimbatore Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model’s capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model’s success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability.

关键词： Wireless sensor networks Renewable energy sources Accuracy Reinforcement learning Throughput Energy efficiency Real-time systems Delays Resource management Reliability

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Application and optimization of FPGA-based real-time motion control systems in robotics

Application and optimization of FPGA-based real-time motion ...

引用

International conference on Electrical Engineering and Intelligent Control (EEIC 2024)

作者： Zimo Chen School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol BS8 1QU UK

ISBN: (数字)9781837242160

With the increasing popularity of robotic applications in various fields and the important role robots play in technological innovation and high-end manufacturing, it is crucial to optimise their performance. Field Programmable Gate Arrays (FPGAs) have greatly advanced control technology, leading to their integration into traditional robotic motion control systems, thereby enhancing performance, efficiency, and flexibility. The paper reviews traditional motion control techniques such as open-loop, closed-loop, model predictive, and adaptive control, then explores the unique capabilities of FPGAs, including parallel processing, data acceleration, system-level integration, and low-power design. These capabilities are analyzed in the context of improving sensor data processing, motion control algorithms, path planning, motor control, communication, image processing, safety, and power management in robots. The article demonstrates the advantages of using FPGAs for precise control, efficient data processing, parallel processing, and low power consumption in robot motion control. It reveals the great potential of FPGA technology in improving robot performance and efficiency. This not only provides a new path to enhance the adaptability and robustness of robots in complex environments, but also provides guidance for future technology development and industrial applications.

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Soft sensor for monitoring dynamic changes in cell composition 9

Soft sensor for monitoring dynamic changes in cell compositi...

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9th IFAC conference on Foundations of systems Biology in Engineering, FOSBE 2022

作者： Espinel-Ríos, Sebastián Morabito, Bruno Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

ISBN: (纸本)9781713867890

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia coli's metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens. Copyright © 2022 The Authors.

关键词： Metabolism

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Learning Topological Representation of sensor Network with Persistent Homology in HCI systems

Learning Topological Representation of Sensor Network with P...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Yan Yan Chengdong Li Jing Xiong Lei Wang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Wenzhou Institute of Technology Wenzhou China

Hand gesture and movement analysis is a crucial learning task in Human-computer interaction (HCI) applications. sensor-based HCI systems simultaneously capture the information with multiple locations to track the coordination of different regions of muscles. Based on the fact that there exists a temporal correlation between the regions, the connectivity analysis of sensor signals builds a network. The graph-based approach for analyzing the sensor network has provided novel insight into the learning in HCI, which has not been broadly investigated in hand gesture recognition tasks. This work proposes a topological representation learning scheme as a graph-based approach for sensor network analysis. Through investigation of the topological properties with persistent homology, the spatial-temporal characteristics are well described to build recognition models. Experiments on the NinaPro DB-2, DB-4, DB-5, and DB-7 datasets with sensor networks built with sEMG signal and IMU signal demonstrate exceptional performance of the proposed topological approach. The topological features are effective in graph representation learning with sensor networks used in hand gesture recognition. The proposed work provides a novel learning scheme in HCI systems and human-in-the-loop studies.

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