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AI-Driven IoT Framework for Vehicle Accident Avoidance and Detection with Cloud Integrated Energy Efficient Solutions

AI-Driven IoT Framework for Vehicle Accident Avoidance and D...

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Innovations in Communications, Electrical and Computer Engineering (ICICEC), International conference on

作者： G Roopini Nagraj Rao P P Dankan Gowda V Balaji Shesharao Ingole Shatrudhan Pandey S Hema Chandra Department of Electronics and Communication and Engineering Sri Venkatesa Perumal College of Engineering and Technology Andhra Pradesh India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India GA USA Marwadi University Research Center Faculty of Management Studies Marwadi University Rajkot Gujarat India Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

IoT based machine learning solution formulated in this paper aims to enhance the real time accident analysis and prevention for vehicles. The IoT sensors interface with advanced deep learning models and the cloud computing system to form a real-time analytics system that acknowledges the data gathered by the sensors, assesses existing risks of accidents, and provides actions that can be taken to prevent them. Energy is one of the main characteristics implemented, where sleep scheduling and adaptive data transmission is used to maximize the battery life of battery driven IoT devices. The use of cloud integration also helps increase the scalability of the given system by referenced computation needs and the favorite continuous learning from accumulated logs. Simulation outputs show better identification accuracy well over 95% on accidents and reduced power usage relative to other IoT applications. Due to the capacity to optimally discern and integrate performance, energetic, and real-time responsiveness, the system offers a feasible strategy for increasing vehicular safety at smart transportation systems.

关键词： Cloud computing Accuracy Scalability sensor phenomena and characterization Real-time systems sensor systems Energy efficiency Safety Internet of Things Accidents

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Electrical Impedance Tomography Image Reconstruction with apriori Knowledge for Gesture Recognition 19

Electrical Impedance Tomography Image Reconstruction with ap...

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19th IEEE International Multi-conference on systems, Signals and Devices, SSD 2022

作者： Alasasfeh, Hayat Hafsa, Mariem Bader, Oumaima Ibbini, Mohammed Hasan, Sameer Kanoun, Olfa Jordan University of Science and Technology Biomedical Engineering Department Irbid Jordan Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical impedance tomography (EIT) reconstructs the conductivity images of an object based on the voltages and the currents at the surface of the object. Among the clinical applications of EIT thorax imaging, brain function monitoring and breast cancer screening. However, EIT suffers from a poor signal-to-noise ratio and spatial resolution. To improve the quality of reconstructed images and provide clear information about the structure, the paper proposes to include prior knowledge about the structure from MRI. The proposed techniques could be very promising with more work. To illustrate this technique in this paper, eight electrodes with the adjacent current pattern were employed in this research. For each gesture, forty data points are recorded. The Gauss-Newton and Back-Projection algorithms are used for image reconstruction with prior knowledge of the human forearm from MRI images. The resultant contours of the Forearm, Ulna, and Radius have been used to build the mesh. In the Gauss-Newton algorithm, the results reveal that structural information improves image quality and conductivity distribution, particularly in the region of two bones. In addition, the reconstructed images using the Gauss-Newton algorithm performed better than images reconstructed using the back-projection algorithm. © 2022 IEEE.

关键词： Image reconstruction

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Integration of a UHF Fractal Antenna into a 1U CubeSat for Low-Earth Orbit Mission 1

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International conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Inojosa, Raynell A. Co, Celso B. Cho, Mengu Spacecraft Payload and Communications Systems Development Division Philippine Space Agency Quezon City Philippines Graduate School of Engineering Batangas State University Batangas City Philippines Laboratory of Lean Satellite Enterprises and In-Orbit Experiments Department of Electrical and Space Systems Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

While a number of novel patch antennas have been designed and integrated into a cube satellite (CubeSat), very few attempts have been made to exploit the self-similarity features of the so-called fractals in the antenna design to improve the satellite communications performance. This research work presents an initial attempt to utilize these fractal properties in the design of a patch antenna, adopting a geometry that allows it to be incorporated into a standard 10 cm × 10 cm × 10 cm (or 1U) CubeSat. Prior to fabrication, a preliminary design was analyzed and simulated using a commercial finite element method (FEM) solver for electromagnetic structures software, High Frequency Structure Simulator (HFSS)™. The proposed antenna was designed at a solution frequency, f= 920 MHz and resonates well from 800 MHz to 1 GHz. The antenna was fabricated on an FR4 substrate (Formula presented). Comparison of the simulated and measured results showed a good agreement in terms of reflection coefficient and voltage standing wave ratio (VSWR) of the proposed antenna at the frequency of interest. For comparison, a commercial off-the-shelf (COTS) antenna was also characterized and measured. The measured peak gain of the proposed fractal antenna is at 3.148 dBi as confirmed by the radiation pattern results. It also exhibits a reflection coefficient of −26.110 dB, a VSWR of 1.104, and a wide bandwidth (BW) across the ultra-high frequency (UHF) band. This type of antenna has the potential to provide a reliable link between a CubeSat and a ground sensor terminal (GST) as confirmed by the long-range communication tests. The proposed fractal antenna is expected to meet the satellite data rate requirement of a CubeSat-based Internet of Things (IoT) for low-earth orbit (LEO) mission. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Microstrip antennas

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Towards Semantic Modelling of the Edge-Cloud Continuum 20th

Towards Semantic Modelling of the Edge-Cloud Continuum

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20th International conference on Practical applications of Agents and Multi-Agent systems, PAAMS 2022

作者： Bernabé, Iván Fernández, Alberto Billhardt, Holger Ossowski, Sascha Universidad Carlos III de Madrid Madrid Spain CETINIA Universidad Rey Juan Carlos Madrid Spain

ISBN: (纸本)9783031186967

Fog and edge computing paradigms increase the performance of IoT systems compared to those based exclusively on conventional cloud computing. Basically, they propose to move software services that process information to a nearby place where IoT data is collected instead of at the core of the network. The computing continuum concept goes a step further and proposes to run such software services in a transparent manner at any of the different computing paradigms and, if the execution context changes (for example due to unforeseen contingencies), to move the services to other devices if they may increase performance. In this context, advanced mechanisms are required to successfully transfer those software services to devices hosted in different computing platforms located anywhere from the edge to the cloud. This article proposes the Edge Cloud Computing ontology (ECO), an ontology for IoT systems composed of devices and data centers hosted in edge, fog or cloud computing environments. We also expose an example scenario based on a service architecture on which ECO facilitates management actions. These actions include detecting the overload status of system elements, proposals for suitable locations for software deployment or identifying elements potentially affected by problems in the system, such as connection link failures. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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Ultrasensitive Detection of Pb2+ Ions in Water Using WS2 Nanoflowers

Ultrasensitive Detection of Pb2+ Ions in Water Using WS2 Nan...

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IEEE conference on Nanotechnology

作者： Sumit Chaudhary Chandrabhan Patel Brahmadutta Mahapatra Kumari Jyoti Mayank Dubey Saurabh Yadav Shaibal Mukherjee Department of Electrical Engineering Hybrid Nanodevice Research Group (HNRG) Indian Institute of Technology Indore Indore India Center for Advanced Electronics (CAE) IIT Indore Madhya Pradesh India Centre for Advance Electronics (CAE) and Centre for Rural Development and Technology (CRDT) Indian Institute of Technology Indore Indore India School of Engineering RMIT University Melbourne Victoria Australia

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

The swift and accurate detection of waterborne pollutants is crucial due to the significant risks they pose to both human health and the environment. Lead and its compounds are highly toxic and can cause various illnesses. However, current lead detection systems face several limitations, including slow response times, high costs, and limited mobility. In this investigation, we have successfully engineered a sensor capable of detecting trace levels of toxic Pb 2 + ions with excellent sensitivity, remarkable selectivity, and rapid transient performance. The developed sensor comprised of CTAB-WS2 as a sensing layer drop-casted on interdigitated electrodes (IDEs) device, sensing layer synthesized through a facile hydrothermal method. Surface morphological, crystal structure and elemental composition of the synthesized CTAB-WS2 is investigated via FESEM, XRD and EDX analysis. Moreover, sensing performance of the developed sensor display outstanding selectivity towards Pb 2 + ions in the presence of other heavy metal ions. With a sensitivity of 418 µA/ppb and a remarkable detection limit of 89.9 ppt, the sensor demonstrated exceptional performance. Moreover, its rapid response time of less than 5 seconds makes it suitable for real-time detection applications. These superior attributes position CTAB-WS2-based sensors as a promising candidate for deployment in real- time environmental quality monitoring and toxic metal detection endeavors.

关键词： Performance evaluation Sensitivity X-ray scattering Tungsten Lead Ions Real-time systems

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Landsat8-9 Satellite Image Classification with ERDAS for Mapping the Urban Spaces of East Sikkim

Landsat8-9 Satellite Image Classification with ERDAS for Map...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Chunnu Khawas Mohan P. Pradhan Ratika Pradhan Ashis Pradhan Dept of Computer Applications Sikkim University Sikkim India Dept. of CSE Sikkim Manipal Ins. of Tech. Sikkim Manipal University Sikkim India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research initiative examines the categorization of Landsat 8-9 satellite imagery from the Operational Land Imager sensor(OLI) using remote sensing to quantify land cover and delineate urban areas in East Sikkim for the year 2023. It employs a system for satellite image processing utilizing supervised classification methods. The processing was transitioned to spectrum preparation and enhancement of spatial resolution utilizing the blue, green, red, near-infrared, and panchromatic channels. LandSat 8-9 OLI and the Thermal Infrared sensor (TIRS) satellites image has been used for this study. ERDAS Imagine software has been used for performing image classification. The validation of the findings from the categorization has been performed using multiple reference points, including a previously validated national processing result and a Google Earth image from the same year. Upon multiple iterations of image classification, the supervised method achieved overall accuracy of 91% and Kappa indices of 0.84 for the Maximum Likelihood classification technique where as the unsupervised method achieved overall accuracy of 76% and Kappa indices of 0.63 for ISODATA classification technique.

关键词： Earth Artificial satellites Accuracy Urban areas Landsat Thermal sensors Satellite images Spatial resolution Remote sensing Image classification

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LGCCF: A Linear Graph Convolutional Collaborative Filtering with Social Influence 26th

LGCCF: A Linear Graph Convolutional Collaborative Filtering ...

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26th International conference on Database systems for advanced applications (DASFAA)

作者： He, Ming Wen, Han Zhang, Hanyu Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9783030731991;9783030732004

Collaborative filtering (CF) is the dominant technique in personalized recommendation. It models user-item interactions to select the relevant items for a user, and it is widely applied in real recommender systems. Recently, graph convolutional network (GCN) has been incorporated into CF, and it achieves better performance in many recommendation scenarios. However, existing works usually suffer from limited performance due to data sparsity and high computational costs in large user-item graphs. In this paper, we propose a linear graph convolutional CF (LGCCF) framework that incorporates the social influence as side information to help improve recommendation and address the aforementioned issues. Specifically, LGCCF integrates the user-item interactions and the social influence into a unified GCN model to alleviate data sparsity. Furthermore, in the graph convolutional operations of LGCCF, we remove the nonlinear transformations and replace them with linear embedding propagations to overcome training difficulty and improve the recommendation performance. Finally, extensive experiments conducted on two real datasets show that LGCCF consistently outperforms the state-of-the-art recommendation methods.

关键词： Recommender systems Graph convolutional network Collaborative filtering Social network

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A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless sensor Networks Using Supervised Training Principles

A Novel Machine Learning Strategy for Anomaly Identification...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： S. Agnes Shifani A. Antony Mary M.I. Mary Metilda G. Vinoth Rajkumar Muppudathi Sutha S S. Maheshwari Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Division of Mathematics School of Sciences SRM Institute of Science and Technology Trichy Department of Mathematics Sathyabama Institute of Science and Technology Chennai Department of ECE J P College of Engineering Tenkasi Department of ECE Sardar Raja College of Engineering Alangulam

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Anomaly detection in Wireless sensor Networks (WSN s) is critical for maintaining the integrity and reliability of various monitoring systems, especially in agricultural settings. The core of this study is the development of a hybrid anomaly detection model, SVM-RFNet, which integrates Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NN). By applying noise reduction techniques, such as smoothing algorithms, the data quality is significantly enhanced, resulting in cleaner and more stable datasets. Feature engineering, including temporal trend analysis and Principal Component Analysis (PCA), further refines the dataset by extracting and selecting the most informative features. This model combines the strengths of each method, leveraging SVM's robustness in high-dimensional data, RF's ensemble learning capabilities, and NN's ability to learn complex patterns. The SVM-RFNet model achieved an impressive accuracy of 96%, demonstrating its efficacy in real-time anomaly detection. The paper concludes with a discussion on future research directions, including the integration of advanced deep learning techniques and the development of self-healing networks, which promise to further enhance the capabilities and applications of WSN s in various domains.

关键词： Support vector machines Radio frequency Training Wireless sensor networks Smoothing methods Artificial neural networks Feature extraction Vectors Anomaly detection Principal component analysis

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Synchronous high-speed OCT imaging with sensorless brushless DC motor and FDML laser in a phase-locked loop 27

Synchronous high-speed OCT imaging with sensorless brushless...

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conference on Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII

作者： Singh, Awanish Pratap Goeb, Madita Ahrens, Martin Eixmann, Tim Schulz-Hildebrand, Hinnerk Huettmanna, Gereon Huber, Robert Rahlves, Maik Univ Lubeck Inst Biomed Opt Peter Monnik Weg 4 D-23562 Lubeck Germany Med Laser Ctr Lubeck Peter Monnik Weg 4 D-23562 Lubeck Germany Harvard Med Sch 25 Shattuck St Boston MA 02115 USA

ISBN: (纸本)9781510658394;9781510658400

High-speed endoscopic optical coherence tomography (OCT) imaging in the MHz range has shown great potential in various medical applications ranging from cancer screening to vascular disease monitoring. High-speed imaging always suffers from non-uniform rotational distortion (NURD) due to asynchronous motor rotation with the OCT system. Several research groups have previously attempted to solve this problem, using either an expensive motor with a sensor or numerical correction after data acquisition. However, both techniques pose challenges for practical use. Therefore, in this study, we use an inexpensive sensorless brushless DC motor with a Fourier domain mode-locked (FDML) laser-based MHz OCT system and try to resolve the problem of synchronization using three different modalities, (i) Slave-mode: The FDML frequency serves as a master frequency for the motor, which is phase-locked to the FDML frequency, (ii) Master-mode: The revolution trigger obtained from the motor's back electromotive force (BEMF) signal serves as a trigger signal for the OCT imaging system, (iii) Both: Fully synchronized setup, where the motor rotation is synchronized with the laser and the imaging system is synchronized with the motor to achieve phase-stable OCT imaging. The first case slightly fluctuates in live preview and imaging due to the absence of a revolution trigger, while the second has varying motor speeds. Therefore, we use the third case to phase-lock the motor with FDML and get a distortion-free live preview and image acquisition. Finally, we demonstrate high-speed SS-OCT structural imaging (at 3.3 MHz A-scan rates) of a finger with a 16 mm diameter probe (at 40,000 rpm).

关键词： Imaging systems Distortion Laser frequency Image sensors Optical sensors Tissues

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Integrating KLV Metadata with UAV Motion Imagery and Geo-Tagging for Enhanced GIS Playback

Integrating KLV Metadata with UAV Motion Imagery and Geo-Tag...

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Advancement in Technology (ICONAT), International conference for

作者： Sakshi Charu Gupta Tushar Kanti Patra Central Research Laborartory Bharat Electronics Limited Uttar Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

With the growth of motion imagery, the ability to visually analyze video data and extract key features has advanced significantly. This technology incorporates essential geo-tagging, sensor, and platform orientation data into the video stream, providing geographic context for viewing and analysis. Recently, the use of multiple drones for detection and rescue operations has surged. These services leverage high-speed 5G networks to capture 4 K videos, processed through MEC (Mobile Edge Computing) servers for functions such as object detection. This paper focuses on reading, extracting, and interpreting metadata embedded in KLV Tags, associating location data with spatio-temporal motion imagery. The Precision Time Stamps of the MISP Time System in the metadata help correlate motion imagery with metadata, overlaying synchronized imagery on GIS at the respective location. The process involves reading the KLV Tag video stream, extracting KLV Tags in Hex format, converting them to text, and interpreting them using the MISB format. The system filters required information from the tags to associate location, extent of video frames, and sensor location with image frames of archived video based on precision time stamps. Geographical boundaries of video frames are overlaid on GIS, synchronizing spatio-temporal motion imagery with location metadata, enhancing the utility of motion imagery in various applications.

关键词： Decision making Streaming media Metadata Feature extraction Real-time systems Data mining Synchronization Servers Usability Geographic information systems

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