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IoT and BSN applications for Real Time Monitoring and Disease Prediction

IoT and BSN Applications for Real Time Monitoring and Diseas...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Meena, Jaishree Shukla, Vivek Jain, Amit Gupta, Vishan Kumar Jain, Paras Amity University Punjab Amity School of Biological Science Mohali India Amity University Punjab Amity Law School Mohali India Op Jindal University Department of Cse Raigarh India Amity University Punjab Amity School of Engineering and Technology Mohali India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India

ISBN: (纸本)9798350373783

IoT applications have been applied to health areas, so in the most advanced healthcare application environment, using them makes medical professionals and patients' lives easier. BSN (Body sensor Network) technology is essential to the Internet of Things (IoT) in the healthcare system because it allows for the use of low-power, lightweight wireless sensor nodes to monitor patients. Healthcare systems utilizing BSN and IoT technologies are the subject of this paper. There are several sensors in this system, including blood pressure, temperature, and pulse rate sensors. The controller receives the data from these sensors after they have detected the parameters. When the temperature rises above the specified range, the buzzer will sound, per the conditions. For display on the LCD, it transports the sensed data. Doctors receive data via the internet at the same time, enabling them to provide prompt, appropriate solutions in real time. Many patients endure suffering because of not receiving prompt, appropriate assistance and solutions to their problems. Thus, the suggested system aids in an emergency and a real-time solution. Because this system is portable, it can be carried around by an individual. As a result, ongoing health monitoring is feasible. Moreover, the system uses a variety of supervised learning algorithms to forecast the illness for a specific patient based on their current reading. © 2024 IEEE.

关键词： Body sensor networks

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An Investigation on Energy Consumption in Wireless sensor Network 8

An Investigation on Energy Consumption in Wireless Sensor Ne...

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8th International conference on advanced Computing and Communication systems, ICACCS 2022

作者： Thiyagarajan, N. Shanmugasundaram, N. Sri Eshwar College of Engineering Dept. of Ece Coimbatore India

ISBN: (纸本)9781665408165

Wireless sensor Networks (WSNs) has gained undisputed importance in the past decade in wide range of applications. WSN consists of densely deployed sensor nodes to monitor the environment. The sensed and collected data is communicated to the end user via the sink node using multi-hop transmission. There are several constraints in the construction of a sensor node such as processing power, memory and battery capacity. When the sensor nodes spend energy in an inefficient manner, it will reduce the network lifetime (NL) and hence the components of sensor node and the protocols governing the network must be optimally designed to extend its lifetime by reducing energy consumption. Many investigators have done their work in identifying energy consumed by the various activities carried out in WSNs. A detailed study is required for complete analysis of energy consumed by various elements in the WSN for the development of efficient protocols. This paper summarizes the energy consumed in a WSN and provides an insight for further research in WSN. © 2022 IEEE.

关键词： Energy utilization

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Comparative Analysis of Human Hand Gesture Recognition in Real-Time Healthcare applications 1st

Comparative Analysis of Human Hand Gesture Recognition in Re...

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1st International conference on Expert Clouds and applications, ICOECA 2023

作者： Dhande, Archita Mantri, Shamla Pande, Himangi MIT World Peace University Pune India

ISBN: (纸本)9789819917440

There is strong evidence that people will be able to communicate with various sensor-based devices more effortlessly and intuitively thanks to future human–computer interfaces (HCI), creating a more human-like interaction. Human–computer interaction has advanced as a result, and new technologies have been created that allow humans to connect with computers in increasingly intuitive and natural ways. Computer systems that use these technologies exhibit improved efficiency, speed, power, and realism. Real-time gesture interpretation is utilized by a vision-based system to modify items in a medical data visualization environment. This study suggests a technique for recognizing gestures that makes use of a convolutional neural network's sequential model made out of a collection of near-infrared pictures obtained by a Leap Motion sensor. The CNN model is made up of 13 different layers. The study also discusses numerous hand gesture recognition applications in the field of healthcare as well as the methodologies employed by these applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human computer interaction

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High Definition Mapping for Inland Waterways: Techniques, Challenges and Prospects 26

High Definition Mapping for Inland Waterways: Techniques, Ch...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Hoesch, Lukas Llorente, Alonso An, Xiangdong Pedro Llerena, Juan Medina, Daniel German Aerosp Ctr DLR Inst Commun & Nav Cologne Germany Carlos III Univ Madrid Appl Artificial Intelligence Grp GIAA Getafe Spain

ISBN: (纸本)9798350399462

Inland waterway transport (IWT) is an efficient way of mass good transportation with low energy consumption and reasonable ecological impact. As for other transport systems, there is a need for reliable advanced driver-assistance functions and increased autonomy. In this regard, High Definition (HD) Maps play a role by enhancing vessels' localisation and perception capabilities. Obtaining HD Maps is an important step towards the ongoing digitalisation of waterbased transport and forms the basis for assistant systems to captains of inland vessels or even higher automated ships. This work discusses the workflow for HD map generation by using vessels as information platforms (i.e., by having precise localisation and environment-detecting perception capabilities). An architecture for HD map generation from vessels provided with geodetic equipment for precise localisation and environment-detecting perception is presented. An overview on standard techniques for HD mapping within the automobile and robotics domain are discussed, as well as the particular challenges and needs present for water-based applications. Finally, the conceptual use and appropriate data exchange of HD Maps are drafted.

关键词： Mapping

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Multimodal imaging sensor based on lidar for advanced perception tasks 3

Multimodal imaging sensor based on lidar for advanced percep...

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Multimodal Sensing and Artificial Intelligence: Technologies and applications iii 2023

作者： García-Gómez, P. Bernal, E. Rodríguez-Aramendía, A. Rodrigo, N. Riu, J. Royo, S. Beamagine S.L. Carrer de Bellesguard 16 CastellbisbalE-08755 Spain Rambla de Sant Nebridi 10 TerrassaE-08222 Spain

ISBN: (纸本)9781510664517

Automated systems increase their requirements in all fields, tightening their performance requirements in aspects like reliability, and ease of manipulation. Within this communication, we will present the development of a compact perception unit which includes a 3D lidar, RGB and thermal imaging for advanced perception purposes. The proposed unit intends to solve the usual hardware problems that software developers intend to solve in field applications. The basic features and performance of the system will be presented, and the applicability of the multimodal sensing approach presented to different applications in security, autonomous vehicles, and other application areas will be overviewed with examples. © 2023 SPIE.

关键词： Optical radar

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The BOHEMIAN Project: Versatile Hybrid in-Fiber Optical-Electrochemical systems for Widely Applicable Biosensing 3

The BOHEMIAN Project: Versatile Hybrid in-Fiber Optical-Elec...

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3rd IEEE International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Andò, Bruno Manenti, Mattia Greco, Danilo Guascito, Maria Rachele Lamberti, Giuseppe Martina, Laura Arcadio, Francesco Zeni, Luigi Cennamo, Nunzio Trono, Cosimo Tombelli, Sara Berneschi, Simone Marcucci, Niccolò Pasquardini, Laura Dieei University of Catania Catania Italy DiSTeBA University of Salento Lecce Italy Ennio De Giorgi University of Salento Dep. of Mathematics and Physics Lecce Italy University of Campania L. Vanvitelli Dep. of Engineering Aversa Italy Istituto di Fisica Applicata 'Nella Carrara' CNR-IFAC Firenze Italy Indivenire srl Trento Italy

ISBN: (纸本)9798350378009

Thereis a growing interest in the development of advanced chemical and biochemical detection methodologies enabling applications in the agri-food, environmental and healthcare sectors. This paper reports the main tasks accomplished in this field by the BOHEMIAN project. The project aims to develop an integrated sensing device implementing different detection methodologies for biosensing, such as electrochemical, capacitive and optical. In particular, exploiting features of the above sensing strategies, the following two solutions will be investigated by using aptamers as bioreceptors: a 'hybrid opto-electrochemical intrinsic fiber sensor' and a 'opto-capacitive sensor'. © 2024 IEEE.

关键词： Chemical detection

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Optimized Bidirectional Long Short-Term Memory Model for Distributed Denial of Service Attack Detection in Wireless sensor Networks 3

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

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3rd International conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Poongodi, K. Mithuna, S. Rajkumar, S. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350391565

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. © 2024 IEEE.

关键词： Denial-of-service attack

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International conference on advanced Engineering Theory and applications, AETA 2022

International Conference on Advanced Engineering Theory and ...

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International conference on advanced Engineering Theory and applications, AETA 2022

ISBN: (纸本)9789819987023

The proceedings contain 61 papers. The special focus in this conference is on advanced Engineering Theory and applications. The topics include: User Grouping and Relay Selection for Improving Outage Performance of Downlink Wireless systems;Priority-Based Uplink Raw Slot Utilization in the IEEE 802.11ah Networks;a Perfect Knife—Bulk Decompilation and Preprocessing Tool;The Enhancement Chromatic Uniformity and Illuminating Flux of WLEDs with Dual-Layer Phosphorus Configuration;study of Sr,Ba3Si6O3N8:Eu phosphor for Improving Color Quality of White Light-Emitting Diodes;toward an Autonomous Operation of Wheel Loaders in Collaboration with a Truck for a Construction Site;simulation of Fluid Flow and Temperature of Automatic Hand Washing Machine;design of Negative Pressure Isolation Room Container Modularization;a Computational Fluid Dynamics Study of Force Generation by Bio-Inspired Continuous Undulating Fins;lyapunov-Based Controller for Bilinear Model Applied to Gas-Fired Furnace;design of Automatic Sliding Door System for Negative Pressure Isolation Room Modularization;a Current sensor Fault-Tolerant Control Solution for Induction Motor Drive;PMSM Drive with Sliding Mode Direct Torque Control;fuzzy Luenberger Observer for sensorless Control of Induction Motor Drive;Design of Torque Balance Controller for Coaxial BLDC Motor Using Hierarchical Sliding Mode Control Applied for Underwater Vehicle;a Kafka-Based Robot Automation Testing Using Genetic Algorithm;applying a Genetic Algorithm to Optimize Linear Quadratic Regulator for Ball and Beam System;apply the Metaheuristic Algorithm to Allocate Distributed Generation and Minimize the Cost of Energy Losses in the Distribution System;a Search Method for Power Transmission System Planning Problem in Ben Tre Province, Vietnam.

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3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms 36

3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive...

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36th IEEE International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Baber, Junaid Aycard, Olivier GIPSA-LAB University of Grenoble Alpes France

ISBN: (纸本)9798331527235

The advent of 3D LiDAR technology has revolutionized object detection in applications such as autonomous driving, robotics, and advanced driver assistance systems. However, existing methods often require substantial computational resources, limiting their practicality for real-time applications on devices with constrained hardware capabilities. This paper presents an efficient and lightweight 3D LiDAR object detection framework, 3D-PSH, that combines adaptive clustering with 3D Point Spatial Histograms (3D-PSH) and classical classification techniques to address these challenges. Our framework begins with an adaptive clustering algorithm that segments the point cloud data into distinct clusters, representing potential objects. 3D Point Spatial Histograms (3D-PSH) are then computed from these clusters and subsequently quantized into a Bag of Visual Words (BoVW) to create a compact and informative representation. These representations are then classified using robust classical classification methods to identify object types, such as pedestrians and vehicles. This multi-step approach ensures a balance between computational efficiency and detection accuracy, making it suitable for real-time deployment. Extensive experiments on the KITTI dataset and our live sensor data demonstrate the effectiveness and efficiency of our proposed framework. The results indicate that our method achieves competitive accuracy while significantly reducing computational requirements compared to traditional approaches. This framework offers a practical solution for deploying 3D object detection in a wide range of applications, particularly where computational resources are limited. © 2024 IEEE.

关键词： advanced driver assistance systems

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Simultaneous Fault Diagnosis for sensor and Railway Point Machine for Autonomous Rail System 27

Simultaneous Fault Diagnosis for Sensor and Railway Point Ma...

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27th IEEE International conference on Intelligent Transportation systems, ITSC 2024

作者： Hu, Xiaoxi Zhang, Xiaohan Chen, Fei Liu, Zongyang Liu, Jin Tan, Lei Tang, Tao Polytechnic Institute Zhejiang University Hangzhou310015 China Wuhan University State Key Laboratory of Water Resources Engineering and Management Wuhan430072 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Institute for Transport Studies University of Leeds LeedsLS2 9JT United Kingdom Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Municipal Engineering Research Institute Beijing100037 China

ISBN: (纸本)9798331505929

Since the safe and reliable behavior of a Railway Point Machine (RPM) is definitely pivotal for rail transportation, scholars and engineers have studied a considerable number of algorithms for RPM fault diagnosis via many kinds of sensor. However, few scholars have considered the possibility that sensors can also experience faults, which cannot be ignored in practical railway applications. To fill the gap, we propose an end-to-end deep learning network named Simultaneous Fault Diagnosis Network (SFDNet) for the simultaneous fault diagnosis for sensor and RPM. Our approach includes Information Aggregate and Update Module (IAUM) and Fault Diagnosis Module (FDM). The IAUM module leverages the concept of Spatial Attention Mechanism (SAM) and Channel Attention Mechanism (CAM) to effectively filter and aggregate information from diverse sensors. It enhances the salience of normal sensor observations while attenuating the influence of anomalous signals, thereby enabling the resultant features to more accurately capture the operational state of the RPMs. The features yielded by SAM are directly employed for sensor fault diagnosis. The FDM integrates Bidirectional Gated Recurrent Units (BiGRUs) and Time-series Feature Pyramid Network (TFPN) to respectively extract and integrate long-term dependencies and multi-scale features, enhancing fault diagnosis in RPM by capturing comprehensive temporal patterns and correlations. Lastly, ablation experiments and comparison studies shows the superiority of our SFDNet. © 2024 IEEE.

关键词： Railroads

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