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American control Conference (ACC)

作者： H. Bessafa C. Delattre Z. Belkhatir A. Zemouche R. Rajamani CRAN CNRS UMR 7039 Université de Lorraine Cosnes et Romain France Department of Electronics and Computer Science University of Southampton Southampton UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester UK Department of Mechanical Engineering Laboratory for Innovations in Sensing Estimation and Control University of Minnesota Minneapolis USA

The main objective of this work is to propose a solution to observer design for triangular systems where additional output measurements are available, which may improve the estimation quality. In fact, such additional measurements prevent the standard high-gain observer methodology to provide solutions for the estimation problem. In this paper, motivated by this issue, we propose two novel observer design methods to handle the additional output measurements. The first one can be viewed as an extension of the standard high-gain observer by introducing a weighting matrix as a tuning parameter, while the second method, which can be viewed as an alternative method, exploits jointly the high-gain methodology and the LPV/LMI technique to overcome some limitations related to the first design method. The proposed methods are applied to a vehicle trajectory estimation problem using the well-known kinematic model. The efficiency of the estimation using both proposed methods and a comparative study between them are provided.

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An Efficient Wireless Sensor Network based Intrusion Detection System

An Efficient Wireless Sensor Network based Intrusion Detecti...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Dinesh Rajassekharan V Ramachandran Dilip Kumar Sharma Thirumoorthy Palanisamy Y. Varatharaj Myilsam Ramesh Chandra Poonia Faculty of Computer Science Peninsula College Malaysia Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%.

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IoT Based Alcohol Detection and Vehicle control System

IoT Based Alcohol Detection and Vehicle Control System

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D.

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Label Cluster Chains for Multi-Label Classification

arXiv

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arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： C. Sivasankar Saravanan G Pradeepa H V. Arun E. N. Ganesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai Tamil Nadu India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Chennai Tamil Nadu India

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy.

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud computing and NLP Techniques

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： L. N. Jayanthi Kannan Shanmugam Challapalli Suma Sagar B S S. P. Maniraj T. R. GaneshBabu Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhapradesh India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Electrical and Electronics Engineering REVA University Bengaluru Karnataka India Department of Data Science and Business Systems Faculty of Engineering & Technology Kattankulathur - Chennai Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience.

关键词： Surveys Text mining Cloud computing Technological innovation Accuracy Medical services Strategic planning Market research Natural language processing Real-time systems

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Comprehensive electrical models for a wireless sensor network device

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Heliyon 2024年第23期10卷 e40415页

作者： León Ávila, Bernardo Yaser García Vázquez, Carlos Alberto Pérez Baluja, Osmel Cotfas, Daniel Tudor Cotfas, Petru Adrian Transilvania University of Brașov Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Brasov 500036 Romania University of Sancti Spiritus “José Martí Pérez” Department of Computer Engineering Faculty of Business and Technical Sciences Comandante Manuel Fajardo s/n Olivos 1 Sancti Spíritus 60100 Cuba Technological University of Havana Department of Automation and Computing Faculty of Automation and Biomedical Engineering Street 114 No. 11901 between Ciclovía and Rotonda Havana 19390 Cuba

This paper proposes a novel circuit model of a Wireless Sensor Network (WSN) device. The model is designed to accurately represent the behavior of a WSN device operating in a duty cycle, capturing the essential characteristics of its components, including the microcontroller, sensor, transceiver, and power supply. The proposed model incorporates static and dynamic power consumption aspects, reflecting the energy usage patterns during various operational states such as sensing, processing, and communication. The model's accuracy is validated through a comparative analysis with a LTspice® implementation and a real mote implemented for this study. Simulation results closely match the empirical data, demonstrating the model's effectiveness in predicting the mote's behavior. This approach provides a valuable tool for optimizing power consumption and extending the lifetime of WSN deployments within a simulation environment, offering significant insights for designers and researchers in this field. © 2024 The Authors

关键词： Electric model SPICE model Wireless sensor network node

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Exploring Defect Behavior in Helium-Irradiated Single-Crystal and Nano-Engineered 3C-SiC at 800°C: A Synergy of Experimental and Simulation Techniques

SSRN

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SSRN 2024年

作者： Wang, Zhiqiang Zhang, Limin AlMotasem, Ahmed Tamer Li, Bingsheng Polcar, Tomas Daghbouj, Nabil School of Nuclear Science and Technology Lanzhou University Gansu Lanzhou China Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Technicka ´ 2 Prague 6160 00 Czech Republic State Key Laboratory for Environment-friendly Energy Materials Southwest University and Technology Sichuan Mianyang621010 China

In this study, sc-and nc-3C-SiC samples were subjected to 30 keV He ion irradiation across various doses while maintaining a temperature 800°C. Employing techniques including Raman spectroscopy, TEM and nanoindentation, the alterations in microstructure and hardness resulting from He irradiation with various fluences were *** sc-SiC, irradiation prompted the formation of He platelets, resulting in a hardness increase of 6 GPa. In contrast, nc-SiC, characterized by a higher stacking fault density, exhibited the formation of bubbles, primarily at GBs, with fewer occurrences within the grain interior, leading to a hardness increase of 1 GPa. Notably, in both sc- and nc-SiC, hardness reached saturation and subsequently stabilized or declined with increasing *** MD cascade simulations, we discerned that various planar defects do not uniformly contribute to enhancing radiation resistance. For example, ISF and twins in SiC played a substantial role in altering defect density and configurations, thereby facilitating point defect annihilation. Conversely, ESF and Σ3 grain boundaries had a limited impact on defect production during a *** of cluster diffusivity revealed an accelerated movement of He-vacancy towards Gbs compared to other planar defects. Moreover, the scarcity of point defects and constrained mobility of He towards stacking faults in nc-SiC elucidated the marked tendency of He to form platelets in ***, our findings established a correlation between the calculated indentation hardness and the geometry of He defects, consistent with experimental results from nanoindentation. These results significantly contribute to ongoing efforts to design SiC materials with heightened radiation tolerance. © 2024, The Authors. All rights reserved.

关键词： Silicon carbide

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A Systematic review of Multi-Mode Fiber based on Dimensional Code in Optical-CDMA 3

A Systematic review of Multi-Mode Fiber based on Dimensional...

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3rd International Conference on Applied and Practical sciences, ICAPS 2021

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Rashidi, C.B.M. Al-Dawoodi, Aras Mahmood, Baban A. Fareed, Alaa Zaenal, Mohammed U. Qasim, Nameer Hashim Rafeeq, Ranjdr M. Faculty of Applied and Human Sciences University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq Computer Technical Engineering Al-Qalam University College Iraq School of Computing University Utara Malaysia Malaysia

This paper deals with optical code division multiple access (optical-CDMA) and provides a general analysis of multi-mode fiber (MMF). The purpose of this work is to classify the works in the literature that are related to Optical-CDMA based on MMF and to find the limitation of the researches done in this field. Several challenges that occur in the medium such as multiple-access interference (MAI), pulse dispersion (PD) and nonlinearity in optical fibers deteriorate system performances and become a major performance-limiting factor. © Published under licence by IOP Publishing Ltd.

关键词： Multiple access interference

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