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Dynamic control and manipulation of near-fields using direct feedback

Light(Science & Applications) 2024年第12期13卷 3151-3157页

作者： Jacob Kher-Aldeen Kobi Cohen Stav Lotan Kobi Frischwasser Bergin Gjonaj Shai Tsesses Guy Bartal The Andrew&Erna Viterbi Faculty of Electrical&Computer Engineering Technion—Israel Institute of TechnologyHaifa 3200003Israel Department of Physical Engineering Polytechnic University of Tirana—Faculty of Physical&Math EngineeringTirana 1000Albania Faculty of Medical Sciences Albanian UniversityDurrës StreetTirana 1000Albania Department of Physics MIT—Harvard Center for Ultracold Atoms and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeMAUSA

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.

关键词： scattering metrology feedback

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Development of Fuzzy Based Models for Detection System with More Accuracy for the Implementation of Healthcare/Clinical System 4

Development of Fuzzy Based Models for Detection System with ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

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 system, which is used in the critical care units of Vienna General Hospital, is noteworthy for introducing a novel third category called borderline. This categorization indicates patient data that partially satisfies the corresponding needs by closely aligning with virus-related clinical concepts or infections related to healthcare (HAI) surveillance categories. The probability of classifying patient data into normal, borderline, of pathological categories with respect to infection-related medical ideas and HAI definitions was calculated by integrating fuzzy sets of propositional fuzzy rules. This deviation from dichotomous approaches prevents the possible misunderstanding of borderline findings with normal ones, a major difficulty in binary classifications. Data collection included a thorough analysis of 5099 individuals treated over a two-year period, including adult patients across 10 ICUs at Vienna General Hospital. The Moni-ICU knowledge base, which includes fuzzy sets and rules for four top-level HAI definitions and 10 infection-related clinical concepts, made the categorization process easier. In order to verify the accuracy and dependability of the system, the assessment focused on how suitable borderline classifications were in relation to normal values for related HAI criteria. © 2024 IEEE.

关键词： Fuzzy rules

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High-Resolution Multiple-Source 2D DOA Estimation using Convolutional Neural Network with Robustness to Array Imperfections

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IEEE Access 2024年 13卷 93115-93123页

作者： Sallam, Tarek Wang, Qun Attiya, Ahmed M. Shandong Xiehe University School of Computer Science and Technology Shandong Jinan250109 China Benha University Faculty of Engineering at Shoubra Cairo Egypt Microwave Engineering Dept Cairo Egypt

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). The sources are considered closely spaced in both elevation and azimuth up to 0.5° resolution in both directions. Ten sources are supposed to be received by an 8 × 8 antenna array. Compared to multiple signal classification (MUSIC) algorithm and radial basis function neural network (RBFNN), numerical results indicate that the CNN model exhibits notable precision in two-dimensional (2D) direction of arrival (DOA) estimation of multiple-source with high resolution, along with less than one-tenth second execution time. Finally, it maintains its superior performance even in the presence of array imperfections and significantly reduced SNR. © 2013 IEEE.

关键词： Convolutional neural networks

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

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-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Blockchain-enhanced artificial intelligence for advanced collision avoidance in the Internet of Vehicles (IoV)

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Neural Computing and Applications 2025年第6期37卷 4915-4936页

作者： Talaat, Fatma M. Hamza, Alyaa A. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Engineering Technology Elsewedy University of Technology–Polytechnic of Egypt 10th of Ramadan City44629 Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Bahrain Polytechnic Isa Town Bahrain Badr City11829 Egypt

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

关键词： Risk assessment

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A new metaheuristic optimization algorithm based on the participation of smart students to increase the class performance

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Soft Computing 2025年第6期29卷 2829-2844页

作者： Pira, Einollah Rouhi, Alireza Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz-Maragheh East-Azarbaijan Tabriz*** Iran

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. After teaching all courses by the class teacher, the lazy students in each course are taught again by the smart student in the course. Inspired by this fact, we present a new metaheuristic optimization algorithm called Participation of Smart Students (PSS) in increasing the class efficiency. The effectiveness of the PSS algorithm has been analyzed by 10 general test functions, 9 test functions from CEC 2019, and 12 test functions from CEC 2022. The results of PSS algorithm are compared with the effectiveness of eight recently developed well-known algorithms, i.e., Teaching and Learning-based Optimization (TLBO) Algorithm, Black Widow Optimization (BWO), Political Optimization (PO), Barnacle Mating Optimizer (BMO),Chimpanzee Optimization Algorithm (CHOA), Aquila Optimizer (AO), Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), and Fick’s Law Optimization (FLA). Comparison of the results obtained by the Friedman rank and Wilcoxon signed-rank tests, for all 31 test functions, shows that PSS outperforms TLBO, BMO, BWO, CHOA, AO, RSA, PDO, and FLA algorithms by 38%, 38%, 64%, 74%, 45%, 64%, 58%, and 35%, respectively. Moreover, the PSS algorithm has a higher convergence speed and hit rate (= 35%) than all other compared algorithms. Finally, the reported results of solving two practical optimization problems by the PSS algorithm confirm its higher effectiveness compared to some good algorithms in the literature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Students

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Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

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computers, Materials & Continua 2023年第2期74卷 3647-3661页

作者： Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafhaSaudi Arabia

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.

关键词： CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI

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Selection of Rendezvous Points Using Fuzzy Logic for Mobile Data Collection in Wireless Sensor Networks

Selection of Rendezvous Points Using Fuzzy Logic for Mobile ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Biib Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

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 resources of sensor nodes and the challenges inherent in mobile data collection. This paper proposes a novel approach to address these challenges through the strategic selection of rendezvous points (RPs) using fuzzy logic. Our method leverages the flexibility and decision-making process of fuzzy logic to evaluate and integrate multiple dynamic variables, including node energy levels, collector mobility patterns, and data priority, to dynamically determine optimal RPs. This approach not only facilitates efficient data transmission but also significantly conserves the energy of sensor nodes, thereby extending the network's lifespan. We present a comprehensive model incorporating a fuzzy inference system for RP selection, tailored to adapt to the changing conditions of WSNs. Through extensive simulations, our results demonstrate a marked improvement in network performance, showcasing reduced energy consumption and minimized data collection latency. The proposed fuzzy logic-based selection method offers a robust and adaptive solution to the challenges of mobile data collection in WSNs, promising enhanced efficiency and prolonged operational longevity of sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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A 69MHz-Bandwidth 40V/μ s-Slew-Rate 3n V/√Hz-Noise 4.5 μ V-Offset Chopper Operational Amplifier

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第7期71卷 2977-2988页

作者： Koolivand, Yarallah Rezaeiyan, Yasser Zamani, Milad Akbari, Meysam Shoaei, Omid Tang, Kea-Tiong Moradi, Farshad Khajeh Nasir Toosi University of Technology Faculty of Electrical Engineering Tehran16317-14191 Iran Aarhus University Department of Electrical and Computer Engineering Aarhus8000 Denmark University of Kurdistan Department of Engineering Sanandaj66177-15177 Iran University of Tehran Department of Electrical and Computer Engineering Tehran*** Iran National Tsing Hua University Faculty of Electrical Engineering Hsinchu300044 Taiwan

This paper presents a chopper-stabilized three-stage operational amplifier (OpAmp) with a unity gain bandwidth of 69 MHz and an input referred noise density of 3 nV√Hz. The proposed design achieves a stable unity gain by proposing a new pole and zero scheme with very low power consumption, drawing only 3.3 mA from a 1.8 V power supply while driving a load capacitor as large as 100pF. To achieve rail-to-rail input swing, the design uses both NMOS and PMOS differential pairs at the input and biases them in the subthreshold region to provide an identical net trans-conductance over the rail-to-rail input common mode. Furthermore, an adaptive biasing is employed and the current sources are kept ON during large signal transitions at the input, thus eliminating crossover distortion and providing a high slew rate of 40 V/μs at a 100 pF load capacitor. The design employs chopping at 2.5 MHz and is enhanced with a local ripple reduction loop, making the OpAmp suitable for high gain and wide bandwidth applications with less filtering required. The design also reduces the input bias current significantly from 500 nA to 1.5 nA by buffering the input and applying it to the modified bootstrap switches. The proposed OpAmp, fabricated in a 0.18μm CMOS process, exhibits a maximum offset of 4.5μV , a flicker noise corner frequency of 246 Hz, a DC gain of 146 dB, a power supply rejection ratio of 123 dB, and a common mode rejection ratio of 116 dB. © 2004-2012 IEEE.

关键词： Operational amplifiers

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NLP Research: A Historical Survey and Current Trends in Global, Indic, and Gujarati Languages 4

NLP Research: A Historical Survey and Current Trends in Glob...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Panchal, Brijeshkumar Y. Shah, Apurva The Maharaja Sayajirao University of Baroda Faculty of Technology and Engineering Computer Science and Engineering Department Gujarat Vadodara India Computer Engineering Department Gujarat Anand India

ISBN: (纸本)9798331529635

This research study presents a comprehensive survey of Natural Language Processing (NLP) research, tracing its historical evolution from its inception to the present. The survey explores the key milestones and advancements in NLP, focusing on global trends, as well as specific contributions from Indic languages, with a particular focus on Gujarati. The paper analyzes the current state of Gujarati NLP research, identifying existing gaps and challenges. By providing a comprehensive overview of NLP research, this study aims to guide future research directions and foster advancements in the field, particularly in the context of Indic languages. © 2024 IEEE.

关键词： Natural language processing systems

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