One of the principal cause of mortality in the countryis liver disease. Each year, liver disease kills more than 2.4 percent of all Indians. As a result,for the early intervention of liver disease, anautomated program...
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Data’s role is pivotal in the era of internet technologies, but unstructured data poses comprehension challenges. Data visualizations like charts have emerged as crucial tools for condensing complex information. Clas...
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Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications,sensing and metrology,as well as for the exploration of fundamental p...
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Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications,sensing and metrology,as well as for the exploration of fundamental properties of light-matter interaction and optical ***-time feedback for active control over light can provide a significant advantage in these endeavors,compensating for ever-changing experimental conditions and inherent or accumulated device *** nearfield microscopy,being slow in essence,cannot provide such a real-time feedback that was thus far possible only by scattering-based ***,we present active control over nanophotonic near-fields with direct feedback facilitated by real-time near-field *** use far-field wavefront shaping to control nanophotonic patterns in surface waves,demonstrating translation and splitting of near-field focal spots at nanometer-scale precision,active toggling of different near-field angular momenta and correction of patterns damaged by structural defects using feedback enabled by the real-time *** ability to simultaneously shape and observe nanophotonic fields can significantly impact various applications such as nanoscale optical manipulation,optical addressing of integrated quantum emitters and near-field adaptive optics.
Many electronic infection detection systems often use binary classification techniques, which identify patient data as either pathology-indicating or normal in relation to different forms of infections. The Moni-ICU s...
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In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-dep...
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In this paper, we present an efficient convolutional neural network (CNN)-based model to estimate both elevation and azimuth arrival angles of multiple sources with high resolution (small source angular separation). T...
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The convergence of blockchain technology and artificial intelligence (AI) presents a promising solution for enhancing safety within the Internet of Vehicles (IoV) ecosystem. This paper introduces the "Blockchain-...
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The convergence of blockchain technology and artificial intelligence (AI) presents a promising solution for enhancing safety within the Internet of Vehicles (IoV) ecosystem. This paper introduces the "Blockchain-Based Collision Avoidance with AI for Vehicles" (BCA-CAR) algorithm, which aims to provide advanced and intelligent collision avoidance capabilities in IoV. BCA-CAR combines the security and data integrity features of blockchain with the real-time decision-making capabilities of AI to prevent collisions and improve road safety. The algorithm consists of five key phases: Data Collection and Processing, AI Collision Risk Assessment, Decision and Smart Contract Execution, Data Validation and Trust (Blockchain Integration), and Learning and Improvement. In the Data Collection and Processing phase, data from vehicle sensors, cameras, V2V and V2I communication, and external infrastructure is collected and preprocessed. The AI Collision Risk Assessment phase utilizes machine learning models to analyze real-time data and predict collision risks. In the Decision and Smart Contract Execution phase, smart contracts on the blockchain automate collision avoidance actions. The Data Validation and Trust phase ensures the authenticity and integrity of data through blockchain technology. Finally, the Learning and Improvement phase leverages historical collision data to enhance predictive models and overall system performance. BCA-CAR's primary objective is to enhance safety by preventing collisions, ensuring data trustworthiness, and providing intelligent collision avoidance capabilities. This innovative algorithm has the potential to revolutionize road safety in the era of IoV by reducing accidents, improving traffic management, and enhancing the security and privacy of vehicular communication. The findings highlight that Support Vector Regression (SVR) demonstrates strong predictive accuracy and adaptability within the Internet of Vehicles (IoV), offering a reliable modeli
The learning and teaching power of the students in different courses can be different according to their intelligence and talent. One student can be smart in a single course while he/she is lazy in other courses. Afte...
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Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlus...
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Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of *** research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed *** this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely *** DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded *** there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object *** in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by *** train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP *** of CenterNet can be justified through the huge improved *** performance of our model is evaluated on KITTI(Karlsruhe Institute of technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature *** also compare our model with other SOTA techniques.
Wireless Sensor Networks (WSNs) are a cornerstone of modern IoT applications, enabling data collection across various environments. The efficiency of these networks, however, is hampered by the limited energy resource...
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