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共 10660 条记录，以下是411-420 订阅

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A Cost-Effective Method to Detect Structural Damage by Employing IOT

A Cost-Effective Method to Detect Structural Damage by Emplo...

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2023 International conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

作者： Nayak, Deekshitha S Pandey, Anubhav Kumar Mangalore Institute of Technology & Engineering Department of Electronics & Communication Engineering Karnataka Moodabidri India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electrical & Electronics Engineering Karnataka Manipal India

ISBN: (纸本)9798350306927

Each structure is unique in its composition, form, and behavior, all of which could change over time due to wear and tear, usage, or environmental factors. Examples of infrastructure that need ongoing monitoring include dams, electrical grids, nuclear power plants, and bridges. The procedure of inspecting any structure to detect the damage and get access to it is called as structural damage detection. Structural health monitoring (SHM) has become increasingly important in the current environment. In order to monitor the structures and conduct critical analysis so that steps can be taken to determine and improve the structures remaining life, remote data collection has become significantly more affordable due to advances in sensors and connection. The Internet of Things (IoT) is a network of intelligent sensors that combines the detection of different structural responses with their transmission to distant computers for ongoing processing and monitoring. It refers to linking any gadget to the Internet and making it interconnected through other connected devices. A vast network of linked individuals and objects known as the Internet of Things (IoT) collects and disseminates information about its users and their surroundings. The use of IoT-based structural damage detection utilizing simulation and hardware testing is the primary focus of this paper. © 2023 IEEE.

关键词： Wireless sensor networks

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Implementation and health monitoring system of vehicle by Fog computing

Implementation and health monitoring system of vehicle by Fo...

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2023 International conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Rani, R. Suneetha Raghavendra, C. Murthy, K. Sreerama Reddy, Nalavala Ramanjaneya BVRlT Hyderabad Engineering College for Women Department of Cse Hyderabad India Cvr College of Engineering Department of Emerging Technology Hyderabad India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India Ksrm College of Engineering Dept. of Cse Kadapa India

ISBN: (纸本)9798350334357

The field of health informatics makes extensive use of WSNs (wireless sensor networks). There has been a rise in the use of wireless and wearable sensors to monitor the vitals of individuals at risk for chronic illnesses, which not only protects patients against abrupt assaults but also ensures that they stick to their treatment regimens. There are a plethora of sensors that provide massive amounts of data. In this study, we suggest using fog computing to improve the monitoring of patients with chronic conditions, which would allow for more effective data collection and processing. The primary difficulty would be filtering out irrelevant information that is not directly related to the patient's health. A fog computing layer is needed in addition to the traditional sensor-to-cloud architecture to solve this problem. It improves the system's overall efficiency by removing the risk associated with a data center failure and decreasing the data sent between the cloud and the sensors. Additionally, we examine the safety and implementation concerns related to this fog computing layer. © 2023 IEEE.

关键词： Patient treatment

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Neural Network Vehicle Modelling for Underwater Vehicles

Neural Network Vehicle Modelling for Underwater Vehicles

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OCEANS conference

作者： Rozenbroek, Tijs Rojer, Jim Steenstra, Lukas Hildmann, Hanno Netherlands Org Appl Sci Res TNO Intelligent Autonomous Syst IAS The Hague Netherlands Netherlands Org Appl Sci Res TNO Heat Transfer & Fluid Dynam HTFD The Hague Netherlands Netherlands Org Appl Sci Res TNO Acoust & Sonar AS The Hague Netherlands

ISBN: (纸本)9798350332261

Underwater assets such as wind turbine foundations and other infrastructure require regular inspections. These inspections are currently performed by extensively trained divers or pilots of remotely operated underwater vehicles. Compact unmanned underwater vehicles could perform many of these tasks, and myriad others, with reduced risk and cost. However, navigation and position estimation is difficult under water, especially due to the absence of satellite navigation signals. Therefore, the fusion of information from sensors into a single position estimate is a common approach. These approaches often suffer from drift in the position estimate, which can accumulate during sub-surface operation. Furthermore, critical sensors can be unavailable in certain segments of the operation. In addition, information from the vehicle's actuators is often disregarded, while these signals could improve position estimation. In this work, the focus is therefore on the inclusion of actuator signals for modelling vehicle behaviour. In particular, the use of neural networks for modelling the vehicle dynamics is explored in a machine learning approach, using real-world data. It is shown that these neural network models are able to capture the relevant dynamics in these data. The feedforward neural network severely limited the effects of position drift when used as plant model in a Kalman filter together with IMU measurements. The presented approach is therefore a promising method for mitigating the error due to unavailability of critical sensors in a real-world setting.

关键词： Neural networks Vehicle Dynamics PNT Underwater Navigation sensor Fusion

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Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses

Non-contact temporalis muscle monitoring to detect eating in...

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IEEE-EMBS International conference on Wearable and Implantable Body sensor networks (BSN) / 18th IEEE-EMBS International conference on Biomedical and Health Informatics (BHI)

作者： Saphala, Addythia Zhang, Rui Thai, Trinh Nam Amft, Oliver FAU Erlangen Nurnberg Chair Digital Hlth Erlangen Germany Univ Freiburg Intelligent Embedded Syst Lab Freiburg Germany Hahn Schickard Stuttgart Germany

ISBN: (数字)9781665459259

ISBN: (纸本)9781665459259

We investigate non-contact sensing of temporalis muscle contraction in smart eyeglasses frames to detect eating activity. Our approach is based on infra-red proximity sensors that were integrated into sleek eyeglasses frame temples. The proximity sensors capture distance variations between frame temple and skin at the frontal, hair-free section of the temporal head region. To analyse distance variations during chewing and other activities, we initially perform an in-lab study, where proximity signals and Electromyography (EMG) readings were simultaneously recorded while eating foods with varying texture and hardness. Subsequently, we performed a free-living study with 15 participants wearing integrated, fully functional 3D-printed eyeglasses frames, including proximity sensors, processing, storage, and battery, for an average recording duration of 8.3 hours per participant. We propose a new chewing sequence and eating event detection method to process proximity signals. Free-living retrieval performance ranged between the precision of 0.83 and 0.68, and recall of 0.93 and 0.90, for personalised and general detection models, respectively. We conclude that non-contact proximity-based estimation of chewing sequences and eating integrated into eyeglasses frames is a highly promising tool for automated dietary monitoring. While personalised models can improve performance, already general models can be practically useful to minimise manual food journalling.

关键词： automatic dietary monitoring eating detection chewing detection smart eyeglasses wearable accessory

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3-D Heterogeneous Wireless Charger Empowered Wireless Rechargeable sensor Network 3

3-D Heterogeneous Wireless Charger Empowered Wireless Rechar...

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3rd International conference on Electronic information Engineering and Computer Science, EIECS 2023

作者： Wang, Zihao Liu, Xilong Yu, Lixing Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350316674

Owing to the rise of wireless power transfer technology, wireless rechargeable sensor network (WRSN) becomes a hot research topic in recent years. Compared to traditional wired sensor networks, WRSNs have no cable constraints. Numerous existing studies assume wireless sensors and chargers co-exist in a two-dimensional (2-D) space. In real-world scenarios, however, three-dimensional (3-D) networks account for the majority, making these previous research results difficult to implement. In this paper, we consider a 3-D WRSN with heterogeneous wireless chargers (i.e., the wireless charging vehicle (WCV), the unmanned aerial vehicle (UAV) and the charging base station (BS)) to efficiently power the WRSN. To reduce energy cost, we use green energy to power the chargers. To extend the lifespan of a WRSN, we aim to fully charge as many sensor nodes as possible to minimize the sleep time of all the sensor nodes. As this sleep time minimization problem is NP-hard, we further propose a 3-D hEterogeneous chArger scheduLing (3DEAL) algorithm to efficiently dispatch wireless chargers to prolong the lifetimes of the sensor nodes. In this algorithm, charging tasks are assigned to each charger according to the residual energy of each sensor node, which greatly improves charging efficiency by wisely determining each sensor node's charging time. Finally, we demonstrate the advantages of the proposed algorithm through extensive experimental simulations. © 2023 IEEE.

关键词： green energy heterogeneous wireless chargers wireless power transfer wireless rechargeable sensor network

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Optimizing sensory Data Accumulation in IoMT Framework Using Energy-Efficient Compressed Sensing Techniques 12

Optimizing Sensory Data Accumulation in IoMT Framework Using...

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12th International conference on intelligent Systems and Embedded Design, ISED 2024

作者： Chakraborty, Tamosa Satyaki, Abhijit Nesa, Nashreen Haldia Institute of Technology Department of Information Technology WB India Aliah University Department of Computer Science and Engineering Kolkata India Jalpaiguri Govt Engineering College Department of Information Technology Jalpaiguri India

ISBN: (纸本)9798331522612

Concurrent sensory data gathering is essential for the Internet-of-Medical-Things (IoMT) networks, since detecting and addressing anomalies is vital in the health-care domain. Given the energy and computational constraints of sensor nodes, it is essential to have an efficient method for gathering sensory data. In this context, the range of sensory data, rather than precise values, is more relevant to health-care industry. This range is expressed through the categories of sensed data. To minimize power utilization in IoMT networks, This research proposes an energy-efficient data accumulation mechanism. This approach leverages a compressed sensing algorithm to process different categories of sensory data. Moreover, a novel mechanism is introduced to propagate data over the cloud for further investigation. Data is only transmitted to the cloud if a sensor reading falls outside the acceptable threshold range. Experimental results demonstrate that this method outperforms in reconstructing original data with above 98% reliability, minimizing network traffic and energy usages less than 8.91% compared to traditional methods. © 2024 IEEE.

关键词： Data compression Energy Efficiency IoMT sensors

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Street Smart: An intelligent Traffic Management System 10

Street Smart: An Intelligent Traffic Management System

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17th INDIACom;2023 10th International conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Hegde, Adwait Patil, Ojas Belurgikar, Soham Godbole, Anand Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9789380544472

Traffic congestion is a critical issue not only in all major cities but also in many rural areas of India. One of the most dangerous side effects of traffic congestion is the unnecessary delay of emergency vehicles. There are several approaches to managing traffic, including wireless sensor networks, inductive loop detection, video data processing, and infrared sensors which are successful strategies for intelligent traffic management. However, the issue with these systems is that they are incredibly expensive to install, maintain, and take a long time to implement. In this paper, we propose a novel method of traffic management along with pedestrian crossing that uses Fuzzy Logic based on lane density which is sensitive to emergency vehicle in traffic. This method, when used in conjunction with the current signalling system, can be the answer to effective real-time traffic control. When compared to current techniques of reducing traffic congestion, this new plan will be easier to implement and cost less. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fuzzy logic

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Research on Autonomous Driving Scenario and Decision-making based on Deep Neural Network 3

Research on Autonomous Driving Scenario and Decision-making ...

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3rd International conference on Electronics and information Technology, EIT 2024

作者： Xiao, Daoyi Hu, Yulu He, Zhiyang Chang'an University School of Electronics and Control Engineering Xi'an China Chang'an University School of Transportation Engineering Xi'an China Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798350369151

The foundation of autonomous driving safety is high-precision environmental perception. As a mission-critical task, object detection has attracted considerable attention in recent years. It employs a range of sensors to gather data on factors such as target position, size, and direction of movement, thereby providing a crucial foundation for driving decisions. This study employs an enhanced Deep Convolutional Neural Network (CNN) to investigate decision-making processes in autonomous driving scenarios, thereby advancing the field of artificial intelligence. The introduction of a multi-scale feature extraction module and a self-attention mechanism serves to enhance the model's capacity for perception of complex traffic environments and recognition of key scenes. The model is capable of efficiently processing sensor data, thereby facilitating accurate scene understanding and real-time decision optimisation. The experimental analysis demonstrates that the enhanced model exhibits superior performance compared to the conventional approach across a spectrum of driving scenarios, thereby enhancing the precision and dependability of decision-making. © 2024 IEEE.

关键词： Convolutional neural networks

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Wireless sensor networks Data Anomaly Detection: A Smart Approach 3

Wireless Sensor Networks Data Anomaly Detection: A Smart App...

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3rd IEEE International conference on intelligent Technologies, CONIT 2023

作者： Haque, Ahshanul Soliman, Hamdy Chowdhury, Md Naseef-Ur-Rahman New Mexico Tech Department of Computer Science SocorroNM United States

ISBN: (纸本)9798350338607

Wireless sensor networks (WSNs) are at the core of many critical civil and military applications where they are very amenable for deployment in some harsh/unsafe environments. The WSNs sensed data integrity and validity are very important in all applications, yet vital in some civil and military applications, where any faulty data might result in very serious consequences such as loss of human lives and precious commodities. In WSNs, sensors might report anomalous readings due to many factors related to their environment of deployment, energy level, computing capabilities, hardware failure, wireless media vulnerabilities, etc., which is a serious challenge. Hence, the need arises for immediate detection of such anomalies at the WSNs sink node, to guarantee the processing of accurately sensed data. In this paper, we devise a smart model to address a solution to the aforementioned problematic challenge. In our approach, we utilize different Machine Learning (ML) models in the process of anomaly detection in sensed data. The performances of the utilized ML models are compared, and the top performance model is incorporated into our smart anomaly detection model (SADM). In the design process of our new SADM, we experimented with over 2.2 million sensed data records from the Intel-MIT lab dataset to train and test many different ML models. The preliminary results of our SADM detection accuracy are very promising, which is very encouraging to proceed in this research direction, with different encoding data sets, to generalize the utilization of the new SADM model over a wider set of WSNs applications. © 2023 IEEE.

关键词： Decision trees

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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