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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Tomkos, I. Papapavlou, Ch. Uzunidis, D. Moschopoulos, K. Muñoz, R. Marom, D.Μ. Nazarathy, M. Department of Electrical and Computer Engineering University of Patras Patras26504 Greece Castelldefels08860 Spain Institute of Applied Physics Hebrew University of Jerusalem Jerusalem91904 Israel Faculty of Electrical and Computer Engineering Technion Israel Institute of Technology Haifa3200 Israel

We describe innovative concepts associated with optical switching nodes and their transceiver interfaces that enable energy-efficient flexible capacity scaling (≥10 Tb/s per interface, ≥1 Pb/s capacity per link and ... 详细信息

关键词： Optical fiber communication

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Free-electron interactions with van der Waals heterostructures: a source of focused X-ray radiation

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Light(Science & Applications) 2023年第7期12卷 1370-1380页

作者： Xihang Shi Yaniv Kurman Michael Shentcis Liang Jie Wong F.Javier Garcia de Abajo ldo Kaminero Solid State Institute and Faculty of Electrical and Computer Engineering Technion-Israel Institute of TechnologyHaifa 32000Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798Singapore ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and TechnologyCastelldefels 08860Spain iCREA-lnstitucio Catalana de Recerca i Estudis Avangats Passeig Lluis Companys23Barcelona08010Spain

The science and technology of X-ray optics have come far,enabling the focusing of X-rays for applications in highresolution X-ray spectroscopy,imaging,and *** spite of this,many forms of tailoring waves that had substantial impact on applications in the optical regime have remained out of reach in the X-ray *** disparity fundamentally arises from the tendency of refractive indices of all materials to approach unity at high frequencies,making X-ray-optical components such as lenses and mirrors much harder to create and often less ***,we propose a new concept for X-ray focusing based on inducing a curved wavefront into the X-ray generation process,resulting in the intrinsic focusing of X-ray *** concept can be seen as effectively integrating the optics to be part of the emission mechanism,thus bypassing the effciency limits imposed by X-ray optical components,enabling the creation of nanobeams with nanoscale focal spot sizes and micrometer-scale focal ***,we implement this concept by designing aperiodic vdw heterostructures that shape X-rays when driven by free *** parameters of the focused hotspot,such as lateral size and focal depth,are tunable as a function of an interlayer spacing chirp and electron *** forward,ongoing advances in the creation of many-layer vdw heterostructures open unprecedented horizons of focusing and arbitrary shaping of X-ray nanobeams.

关键词： optics integrating arbitrary

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A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM 12

A Study on the Potency of Hybrid Models: Detecting Diseases ...

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12th International electrical engineering Congress, iEECON 2024

作者： Yodrot, Teerapon Sutacha, Chalermkiat Orachon, Teerapong Jangjongdee, Nattapon Boonyasuwanno, Sompol Rajamangala University Of Technology Rattanakosin Faculty Of Industry And Technology Department Of Electrical Engineering Technology Prachuap Khiri Khan Thailand Ubon Ratchathani Rajabhat University Faculty Of Industrial Technology Department Of Computer Network Engineering Ubon Ratchathani Thailand Kosen Of King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures - VGG16, ResNet50, AlexNet, and EfficientNetV2 - for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants. © 2024 IEEE.

关键词： Convolutional neural networks

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Domain and Modality Adaptation Using Multi-Kernel Matching 31

Domain and Modality Adaptation Using Multi-Kernel Matching

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Yampolsky, Tamir Baruch Talmon, Ronen Lindenbaum, Ofir Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Faculty of Engineering Bar-Ilan University Ramat Gan Israel

ISBN: (纸本)9789464593600

In this paper, we propose a new method for domain and modality adaptation using multi-kernel matching. Our method is based on the representation of the source and target sets with several local kernels centered at a small number of apriori known corresponding samples. We propose to match the local kernels and, in turn, aggregate the local matches and find a mapping between the source and target sets. We showcase the applicability of our method on simulations and real-world data sets that include EEG recordings for mental arithmetic identification and single-cell multi-omics. In these applications, we demonstrate the advantages of our method over recent competing schemes. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Signal processing

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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An authenticated spectrum acquirement and transmission of encrypted images over fusion centre

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Multimedia Tools and Applications 2024年 1-24页

作者： V, Thanikaiselvan Y, PiyaDessi S, Renuga devi Shanthini, S. Suba Mahalingam, Hemalatha S, Aashiq Banu R, Dhivya V.M, Padmapriya Amirtharajan, Rengarajan School of Electronics Engineering Vellore Institute of Technology Vellore Vellore India School of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia System Engineering Department Military Technological College Muscat Oman Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & amp Electronics Engineering SASTRA Deemed University 613 401 Thanjavur India

These days, spectrum insufficiency is the most common issue. If the number of smartphone users who access the radio spectrum grows, the radio spectrum becomes increasingly scarce. Certain types of technology equipment are designed to work only within a specific spectrum range, and there is rivalry in specifications for various other applications. As a result, this paper effectively exploits a licensed user’s unused spectrum to reduce the crowded spectrum and eradicate the Radio spectrum shortage. Licensed primary users (PUs) and secondary users (SUs) would be accepted into a Fusion Centre (FC) based on a spectrum framework with authentication. The approved PUs begin by updating their information to FC1;thenceforth, encryption is performed on the information uploaded to FC1. This information from FC1 is stored in Cloud Storage after encryption using the Blowfish algorithm & De-oxy DNA, a fast encryption algorithm. Following that, FC2, connected to the same cloud environment, downloads the PU data uploaded. The spectrum is then allocated to the authenticated SU linked to FC2 based on signal conditions and priority. This proposed method also avoids any delay in the network and encryption by acquiring a fast encryption algorithm, making the system faster and avoiding the Primary User Emulation (PUE) attack. Later, the time taken for the Tiny Encryption Algorithm (TEA) and the Blowfish/DNA algorithm are compared to validate the run time for different input images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Forecasting EV Market Trends in India: A Deep Learning Approach for Two/Three-Wheelers 12

Forecasting EV Market Trends in India: A Deep Learning Appro...

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12th International electrical engineering Congress, iEECON 2024

作者： Mishra, Rishav Dev Kumar Dash, Santanu Chudjuarjeen, Saichol Korkua, Suratsavadee School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Vellore Institute Of Technology TIFAC-CORE Vellore India Rajamangala University Of Technology Krungthep Faculty Of Engineering Dept. Electrical Engineering Bangkok Thailand School Of Engineering And Technology Walailak University Thailand

ISBN: (纸本)9798350383591

This research paper presents a comprehensive analysis of machine learning models for predicting future sales of electric vehicles (EVs) in the Indian Market. With a specific focus on 2 and 3 wheeler sales. The study compares different machine learning approaches, including Linear Regression, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network, Through an evaluation of their respective errors, LSTM Neural Network method demonstrates superior prediction accuracy, making it the preferred model for forecasting EV sales, specifically 2 wheeler and 3 wheeler sales over the next five years. The paper aims to present the forecasted sales figures, in hopes of providing valuable insights into the future trajectory of EV sales and serving as a vital resource for stakeholders in the electric vehicle industry. © 2024 IEEE.

关键词： Long short-term memory

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Gesture recognition using elastic data glove based on FFT-BIGRU 9

Gesture recognition using elastic data glove based on FFT-BI...

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9th International Symposium on Advances in electrical, Electronics, and computer engineering, ISAEECE 2024

作者： Tao, Yehao Fan, Jianbo Shang, Jie Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9781510683303

Gesture recognition, as one of the important components of human-computer interaction, has a wide range of applications in important fields such as rehabilitation medicine, machine control, and virtual reality. Gesture recognition based on wearable data gloves has attracted widespread attention due to its strong universality and scalability. This paper utilizes an elastic data glove independently developed by our team to collect data. The glove integrates independently developed high-precision stretchable angle sensors and common inertial sensors, which can effectively recognize the bending and spatial position of the fingers. The innovative use of angular velocity thresholds for gesture segmentation, the utilization of linear interpolation and combination mean for data resampling, and the development of a network structure combining Fast Fourier Transform (FFT) with Bidirectional Gated Recurrent Unit (BIGRU) not only effectively captures the periodic patterns in the data but also handles long sequence dependencies. Achieving an accuracy of 94.70% in the classification of 14 sign language gestures, this approach outperforms other classical models. Ablation experiments have validated the effectiveness of the proposed network modules and stretchable angle sensors. © 2024 SPIE.

关键词： Gesture recognition

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