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A Novel IoT-Enabled Oxygen Delivery System with Neural Network-Based Predictive Control

A Novel IoT-Enabled Oxygen Delivery System with Neural Netwo...

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Ubiquitous Computing and intelligent information Systems (ICUIS), International conference on

作者： Vijai Sivalingam Jayakumar Jayaraj Subha Hency Jose Paul Geetika Yadav K. Rajasekaran P. Anantha Christu Raj Division of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India Indian Council for Medical Research New Delhi India Department of Biomedical Engineering KPR Institute of Engineering and Technology Coimbatore India Division of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In this research study, a hardware is designed to monitor and control oxygen delivery based on physiological parameters, blood Oxygen saturation, and heart rate for real time applications. The development and evaluation of an intelligent portable Oxygen Concentrator system that integrates a sensor, Motor driver Controller, and ESP8266 microcontroller. The controller measures SPO 2 and heart rate. Data from these sensors are processed by neural network model implemented on the ESP8266 which predicts the optimal Oxygen flow rate constrained internet of things (IoT) applications such as wireless sensor networks. Therefore, in this paper, the design steps to build a model in NodeMCU ESP8266 using an artificial neural network (ANN) are presented. Controller is used in our study and NodeMCU ESP8266 is chosen because it is suitable to be used as a sensor node or sink in WSN applications. ANN with a Levenberg Marquardt algorithm is used as it can model the non - linearity with high accuracy. This intelligent system effectively manages Oxygen delivery, optimizing patient care, and has demonstrated high accuracy MSE of 0.0588 in predicting Oxygen flow requirements using a dataset of 1178 data points. For physiological metrics like SPO 2 , heart rate, respiration rate, and temperature, combining neural networks with data sets can help lower mistakes and increase the precision of predictions generated using these methods.

关键词： Heart rate Temperature sensors Wireless sensor networks Oxygen Accuracy Artificial neural networks Predictive models Data models Internet of Things Biomedical monitoring

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Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping

Demo Abstract: Range-SLAM: UWB based Realtime Indoor Locatio...

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International Symposium on information processing in sensor networks (IPSN)

作者： Yi Liu Zhuozhu Jian Junbo Tan Lunfei Liang Houde Liu Xinlei Chen School of Information Science and Engineering Shandong University Shenzhen International Graduate School Tsinghua University School of Mechatronics Engineering Harbin Institute of Technology Pengcheng Laboratory RISC-V International Open Source Laboratory

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

Simultaneous localization and mapping (SLAM) systems frequently employ LiDAR and cameras as essential sensing components. However, these sensors are proved to be unreliable in environments with poor visibility or reflective surfaces. And UWB (Ultra Wide Band) sensor with a longer wavelength shows better potential to achieve perception tasks. However, since UWB sensors can only obtain distance information from the anchors, it is difficult to densely construct the geometric structure of the environment. In this paper, We propose Range-SLAM, a method based on received signal strength indicator (RSSI) recognition and binary filtering to complete the mapping task and enhance positioning based on the map, and only require UWB as external perception sensor. Real-world experiments are conducted and prove the effectiveness, real-time performance and robustness of the Range-SLAM algorithm.

关键词： Location awareness Simultaneous localization and mapping Laser radar Surface waves information processing Robustness Real-time systems

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Enhanced Smart Crop Health Monitoring Using Visual sensors and Image processing

Enhanced Smart Crop Health Monitoring Using Visual Sensors a...

引用

MetroCon, IEEE

作者： Kyle Boatright Luis Trejo Emma Louise Smith Haitham Abu Ghazaleh Sotirios Diamantas Hennen Cummings Department Computer Science & Electrical Engineering and Department of Wildlife & Natural Resources Tarleton State University Stephenville Texas USA

ISBN: (数字)9798331507640

ISBN: (纸本)9798331507657

The main goal of this research is to explore and apply newer solutions for modernizing the agricultural industry which would effectively contribute to the sustainability and well-being of the society. Our objective is to investigate and implement smart-connected and cost-effective technologies that can aid with efficient crop management. Such solutions can further enable smart decision-making and aid in the automation of certain operations in the agricultural process. In this work, we developed a monitoring system using wireless sensor networks and RGB cameras, with an emphasis on implementing the suitable image processing techniques for monitoring certain features of the crop. We present our preliminary results showing the success of our solution at estimating leaf density and tip burns. This information, and in conjunction with other measurements, can enable smart decision-making and aid in the automation of certain operations in the agricultural process.

关键词： Industries Wireless sensor networks Visualization Automation Image processing Decision making Crops Sustainable development Monitoring intelligent sensors

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Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices 2nd

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozo...

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2nd International conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Quota Matching-based Task Offloading for WSN in Smart Grid 7

Quota Matching-based Task Offloading for WSN in Smart Grid

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2022 7th International conference on Electronic Technology and information Science, ICETIS 2022

作者： Cui, Junbin Li, Yichao Yang, Huifeng Wei, Yong Liu, Wei Ji, Chunhua Huang, Biyao Zhang, Sunxuan State Grid Hebei Electric Power Co. Ltd. Information and Communication Branch Shijiazhuang050021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9783800757794

With the development of the smart grid, massive sensors are deployed to collect data and offload them to edge gateways for processing. However, the task offloading optimization still faces several challenges such as differentiated quality of service (QoS) requirements, decision coupling among multiple sensors, and the impact of electromagnetic interference. In this paper, a low complexity task offloading algorithm based on quota matching is proposed for wireless sensor network (WSN). The joint optimization problem is transformed into a quota matching problem. Through continuous iteration, a stable matching between sensors and servers is established to improve the weighted difference of delay and energy efficiency. The effectiveness of the proposed algorithm is validated through simulations. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Wireless sensor networks

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Trajectory set Empowered Hypergraph Transformer for Mobile sensor Based Traffic Prediction

Trajectory set Empowered Hypergraph Transformer for Mobile S...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hanyuan Zhang Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Traffic speed prediction is vital for intelligent transportation systems. However, most existing methods focus on costly static sensors. In contrast, utilizing GPS devices from vehicles as mobile sensors offers a cost-effective means to gather dynamic traffic data. Despite the presence of historical trajectory data, mobile sensor-based traffic prediction remains under-explored. Existing methods often treat trajectories as substitutes for static sensors, missing the full utilization of the spatial-temporal signals within the complete trajectory set. To address this, we propose TrajHGT, a novel trajectory set empowered hypergraph transformer model that captures trafficrelated spatial-temporal features through adaptive attention and fusion mechanisms in both the trajectory hypergraph space and the road graph space. Real dataset experiments demonstrate the superiority of TrajHGT.

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Automatic Parking System Using Deep Learning and Internet of Things 4

Automatic Parking System Using Deep Learning and Internet of...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Jyothi, Saddi Rami Reddy, M. Prasad, Chinthalapudi Siva Vara Rishita, Nimmagadda Prasad, Toka Shyam Satya Vara Ali, Shaik Irfan Lakireddy Bali Reddy College of Engineering Department of Information Technology Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Freshman Engineering Department Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350349856

To address the labour - intensive requirements of traditional parking lot operations, we have developed an automated system for access control, occupancy tracking, and revenue processing. The proposed system employs computer vision and deep learning techniques to autonomously handle parking lot ingress and egress while reducing operating costs. Vehicles entering and exiting the automated parking facility will be detected using infrared sensors interfaced to a Raspberry Pi microcomputer. License plate images will be captured by a Pi camera module and processed in real-time using an optical character recognition (OCR) algorithm based on a convolutional recurrent neural network architecture optimized for number plate recognition. Human access control can be handled in parallel by leveraging object detection models like Faster R-CNN or YOLO to differentiate pedestrian from vehicle ingress. The onboard occupancy tracking functionality will alleviate the need for manual counting of available spaces. Additionally, real-time parking availability stats will be published to a monitoring website allowing customers to view open slots prior to arrival. Lot capacity constraints can be configured to automatically disable entrance gates when maximum capacity is reached. The system's sensor fusion and computer vision capabilities eliminate tedious manual tallying and oversight required by legacy parking systems. By autonomously controlling access gates, tracking and publishing occupancy stats, and extracting revenue data from license plates, the proposed automated parking lot system aims to reduce operating costs while creating a scalable solution applicable to facilities ranging from apartment complexes to large shopping malls. The use of a low-cost, open-source development platform promotes maintainability while allowing for customized enhancements. © 2024 IEEE.

关键词： Operating costs

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IMPROVING INFERENCE FOR SPATIAL SIGNALS BY CONTEXTUAL FALSE DISCOVERY RATES 47

IMPROVING INFERENCE FOR SPATIAL SIGNALS BY CONTEXTUAL FALSE ...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Goelz, Martin Zoubir, Abdelhak M. Koivunen, Visa Tech Univ Darmstadt Signal Proc Grp D-64283 Darmstadt Germany Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland

ISBN: (纸本)9781665405409

A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.

关键词： sensor networks multiple hypothesis testing local false discovery rate spatial inference information fusion

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External Interaction Estimation of 6-PSS Parallel Robots with Embodied Mechanical Intelligence

External Interaction Estimation of 6-PSS Parallel Robots wit...

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IEEE/RSJ International conference on intelligent Robots and Systems (IROS)

作者： Jingyuan Xia Zecai Lin Xiaojie Ai Guangjun Yu Anzhu Gao Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China Second Affiliated Hospital The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Traditional interaction perception of parallel robots relies on a six-dimensional force sensor for contact sensing at their distal end. However, the sensor body occupies the space of moving platform and also increases the load on the robot actuations. To enable both minimization and embodied intelligence, this paper proposes an external interaction estimation method with embodied mechanical intelligence by embedding two single-axis force sensors in each leg of 6-PSS parallel robot. The method uses a backward propagation neural network optimized by sparrow search algorithm, and it can simultaneously estimate the external force and its position using information from multiple single-axis force sensors and the encoder of driving motor. The experimental platform is established to collect the data and train the network. The result shows that the force estimation mean error is 2.4% and the position estimation error is 2.9%. A demonstration with a virtual display interface showing the reconstructed parallel robot pose, and the interaction force and its pose using the proposed estimation method, indicates the effectiveness of the proposed interaction method with embodied mechanical intelligence for 6-PSS parallel robot

关键词： Robot motion Legged locomotion Parallel robots Force Neural networks Estimation Robot sensing systems Minimization Force sensors Payloads

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A New Type of Prefabricated Cabin Monitoring System for Distribution Rooms

A New Type of Prefabricated Cabin Monitoring System for Dist...

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IEEE International conference on Power and Power and Renewable Energy (ICPRE)

作者： Xiang Zhang Huicheng Li Qingming Zhang Junlin Lu Dongguan Power Supply Bureau Dongguan China Guangdong University of Technology Dongguan China National Open University Dongguan China Electric Power College South China University of Technology Guangzhou China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

In response to the common problems of low work efficiency and difficult quality assurance in traditional equipment operation status monitoring systems for prefabricated cabins in distribution rooms, a new type of monitoring system for prefabricated cabins in distribution rooms is proposed by adding infrared dual view, intelligent air switch, air conditioning controller, and connecting sensors such as temperature and humidity sensors, door status sensors, water immersion sensors, and smoke monitoring sensors to the intelligent gateway of the distribution network. The system adopts a message integrity authentication method to prevent some key information from being maliciously tampered with by third parties, thereby improving the intelligent monitoring level of prefabricated cabins in distribution rooms.

关键词： Temperature sensors Temperature distribution Switches Humidity Distribution networks Logic gates sensor systems Temperature control Monitoring intelligent sensors

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