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arXiv 2021年

作者： Salimi, Majid Mala, Hamid Martin, Honorio Peris-Popez, Pedro Faculty of Computer Engineering University of Isfahan Isfahan Iran Department of Electronic Technology University Carlos III of Madrid Avda. de la Universidad 30 Leganés28911 Spain Department of Computer Science University Carlos III of Madrid Avda. de la Universidad 30 Leganés28911 Spain

Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental cryptographic primitive in which users register into a key generation centre and receive a public/private key pair each. After that, any subset of these users can compute a shared key without any interaction. Nowadays, IoT devices suffer from a high number and large size of messages exchanged in the Key Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate the airtime and latency of messages transferred between IoT devices. MP-NIKE schemes can be realized by using multilinear maps. There are several attempts for constructing multilinear maps based on indistinguishable obfuscation, lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes are inefficient in terms of computation cost and memory overhead. Besides, several attacks have been recently reported against CRT-based and lattice-based multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme in the literature which has been claimed to be both secure and efficient. In this article, we present an attack on this scheme based on the Euclidean algorithm, in which two colluding users can obtain the shared key of any arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE scheme. We show how our proposal is secure in the random oracle model assuming the hardness of the root extraction modulo a composite number. © 2021, CC BY.

关键词： Internet of things

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Adapting to dynamic LEO-B5G systems: Meta-critic learning based efficient resource scheduling

arXiv

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arXiv 2021年

作者： Yuan, Yaxiong Lei, Lei Vu, Thang X. Chang, Zheng Chatzinotas, Symeon Sun, Sumei The Interdisciplinary Centre for Security Reliability and Trust Luxembourg University Kirchberg1855 Luxembourg The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China The Faculty of Information Technology University of Jyväskylä JyväskyläFI-40014 Finland The Institute for Infocomm Research Agency for Science Technology and Research Singapore138632 Singapore

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. To address them, we first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Intelligent COVID-19 forecasting, diagnoses and monitoring systems: A survey

TechRxiv

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TechRxiv 2021年

作者： Anjum, Nasreen Asif, Amna Kiran, Mehreen Jabeen, Fouzia Yang, Zhahoui Huang, Chongwen Noor, Salma Imran, Kanwal Ali, Ihsan Mohamed, Ehab Mahmoud The Department of Computer Science Shaheed Benazir Bhutto Women University Peshawar Pakistan The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China Faculty of Computer Science and Information Technology University of Malaya Malaysia Electrical Engineering Dept. Colleague of Engineering Prince Sattam Bin Abdulaziz University Wadi Addwasir Saudi Arabia Electrical Engineering Dept. Faculty of Engineering Aswan University Aswan Egypt

To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19. © 2021, CC BY.

关键词： Coronavirus

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Employing derivatives for creating texture masks for digital images

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AIP Conference Proceedings 2024年第1期2898卷

作者： Taha Basheer Taha Ruzelita Ngadiran Dujan B. Taha IT Department Faculty of Applied Science Tishk International University Erbil Kurdistan Region Iraq Faculty of Electronic Engineering Technology Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Centre of Excellence Advanced Computing (AdvComp) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Software Engineering Department College of Computer Science and Mathematics Mosul University Mosul Iraq

Texture masking has been used as a significant factor in creating perceptual maps that simulates human vision system in observing noise in digital images. In this paper, the possibility of creating texture masks using first and second derivatives is investigated and tested. Results shows that after applying the derivatives, areas of higher texture were highlighted and recognized, which will lead to simple implementation of texture masks using numerical analysis.

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Near-Field Hybrid Beamforming for Extremely Large-Scale (XL)-MIMO Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Yang, Songjie Elbir, Ahmet M. Chen, Hua Xiong, Youzhi Zhang, Zhongpei Yuen, Chau University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Istinye University Department of Electrical and Electronics Engineering Istanbul34396 Turkey at the University of Luxembourg L-1855 Luxembourg Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China Sichuan Normal University College of Physics and Electronic Engineering Chengdu610101 China Nanyang Technological University School of Electrical and Electronics Engineering China

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant *** this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1, j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF. © 2002-2012 IEEE.

关键词： Hadamard transforms

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AI-Driven Interaction and Collaborative Optimization of Vehicle,Charging Station and Grid:Challenges and Prospects

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Energy engineering 2025年第6期122卷 2187-2195页

作者： Bo Yang Zhe Jiang Jianfeng Wen Ning Yang Kaiping Qu Shuai Zhou Faculty of Electric Power Engineering Kunming University of Science and TechnologyKunming650500China Department of Electrical Engineering and Electronics University of LiverpoolLiverpoolL697ZXUK Department of Electronic&Electrical Engineering University of StrathclydeGlasgowG11XWUK The Department of Information Engineering The Chinese University of Hong KongHong Kong999077China School of Engineering Computer and Mathematical SciencesAuckland University of TechnologyAuckland1010New Zealand

1 Introduction Amid escalating global climate change,the“dual carbon”goals of carbon peak and carbon neutrality have become a focal point of global attention and an important strategy for sustainable development[1].With the rapid development of renewable energy technologies and the increasing public demand for environmental protection and low-carbon living,the adoption of new energy vehicles,particularly electric vehicles(EVs).

关键词： Vehicle-to-grid(V2G)interaction artificial intelligence collaborative optimization electric vehicles grid scheduling

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Reduced Annotation Based on Deep Active Learning for Arabic Text Detection in Natural Scene Images

TechRxiv

引用

TechRxiv 2021年

作者： Boukthir, Khalil Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. BP 1173 Sfax3038 Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia

Providing labeled Arabic text images dataset for scene text detection is inherently difficult and costly at the same time. Consequently, only few small datasets are available for this task. Previous work has only focused on the data augmentation technique of small datasets;however, the images generated with these techniques cannot reproduce the complexity and variability of natural images. In this paper, we propose a new Arabic text images dataset using the Google Street View service named TSVD. The dataset contains 7k images collected from different Tunisian cities. It is much more diverse and complex than current image datasets. Taking advantage of this dataset to train a CNN models, annotation is required for building high performance models. The annotation task consumes a lot of time and effort for researchers due to its repetitiveness. The development time of text detection systems in natural images is valuable with an effective use. We believe that we have developed a Deep Active Learning algorithm for the annotation phase. A Deep Active Learning algorithm for the annotation phase has been developed by approaching the annotation suggestion task using a deep learning text detector. Deep convolutional neural networks (CNN) are used to perform the text detection in natural scene images. Our deep active learning framework combines convolutional neural network (CNN) and active learning approach. This reduces annotation effort by making pertinent suggestions on the most effective annotation areas. We utilize uncertainty provided by CNN models to determine the maximum uncertain areas for annotation. Deep active learning is shown in order to reduce significantly the number of training samples required and also to minimize the annotation work of our dataset up to 1/5. © 2021, CC BY-NC-SA.

关键词： Convolutional neural networks

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Publisher Correction: A conformable phased-array ultrasound patch for bladder volume monitoring

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Nature electronics 2024年 91页

作者： Lin Zhang Colin Marcus David Mejorado Sara V. Fernandez Ikra Iftekhar Shuvo David Sadat Wenya Du Canan Dagdeviren Anantha P. Chandrakasan Dabin Lin Weiguo Liu Scott Joseph Schoen Jr Theodore T. Pierce Viksit Kumar David Hunt Qian Li Hannah Edenbaum Anthony E. Samir Li Jin Fei Li Yonina C. Eldar Media Lab Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA USA School of Opto-electronical Engineering Xi’an Technological University Xi’an China Center for Ultrasound Research & Translation Department of Radiology Massachusetts General Hospital Boston MA USA Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education School of Electronic Science and Engineering Xi’an Jiaotong University Xi’an China Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

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Control motor speed through variable frequency drive by using Arduino

引用

AIP Conference Proceedings 2023年第1期2564卷

作者： Samra Urooj Khan Nik Shahidah Afifi Md Taujuddin Sami Ud Din Zoya Khan Sundas Naqeeb Khan Zaidatul Hafiszah Hussein Tara Othman Qadir Faculty of Electronic and Electrical Engineering University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia Department of Electrical Engineering Namal Institute of Mianwali Punjab Pakistan Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia

People are already employing fuel or grid-powered machines for all processes due to advancements in technology as well as productivity expansion. However, an unsustainable spike in fuel prices or the restriction of electricity is supplies into off regions which become a barrier towards this group’s growth. Because of absorption of the smoke, ozone environmental impacts from both fuel combustion and electricity generation cause climate change and health problems. Because of an excellent solar exposure trend, a solar panel may be employed as an energy source to reduce the world’s load. Speed of any machine is the key feature for running it and take high performance regarding productivity, and it need very efficient processing. The goal of this study is to use a variable frequency drive and a solar panel as an energy source to develop and assess the behavior of a motor speed. Physical demonstration was made via installing Solar panel to generate DC by setting up a 3-phase motor speed with VFD based H-bridge inverter to convert into AC while step up this output with Transformer whereas the faults and fluctuations reduce through many protection components to improve the performance. The anticipated output is calculated via hardware simulation based on the maximum solar panel power.

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Bessel-beam direct-write of the etch-mask in a nano-film of alumina for high-efficiency Si solar cells

arXiv

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arXiv 2024年

作者： Katkus, Tomas Ng, Soon Hock Mu, Haoran An Le, Nguyen Hoai Stonyte, Dominyka Khajehsaeidimahabadi, Zahra Seniutinas, Gediminas Baltrukonis, Justas Ulčinas, Orestas Mikutis, Mindaugas Sabonis, Vytautas Nishijima, Yoshiaki Rienäcker, Michael Krügener, Jan Peibst, Robby John, Sajeev Juodkazis, Saulius Swinburne University of Technology HawthornVIC3122 Australia Laser Research Center Physics Faculty Vilnius University Saultekio Ave. 10 Vilnius10223 Lithuania Altechna R&D Mokslininķ u str. 6A Vilnius08412 Lithuania Department of Electrical and Computer Engineering Graduate School of Engineering Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Am Ohrberg 1 Emmerthal31860 Germany Institute of Electronic Materials and Devices Leibniz Universität Hannover Schneiderberg 32 Hannover30167 Germany Department of Physics University of Toronto 60 St. George Street TorontoONM5S 1A7 Canada Tokyo Institute of Technology Nagatsuta-cho Midori-ku Kanagawa Yokohama226-8503 Japan

Large surface area applications such as high-efficiency > 26% solar cells require surface patterning with 1-10 µm periodic patterns at high fidelity over 1-10 cm2 areas (before up scaling to 1 m2) to perform at, or exceed, the Lambertian (ray optics) limit of light trapping. Here we show a pathway to high-resolution sub-1 µm etch mask patterning by ablation using direct femtosecond laser writing performed at room conditions (without the need for a vacuum-based lithography approach). A Bessel beam was used to alleviate the required high surface tracking tolerance for ablation of 0.3-0.8 µm diameter holes in ∼ 40 nm alumina Al2O3-mask at high writing speed, 7.5 cm/s;a patterning rate 1 cm2 per 20 min. Plasma etching protocol was optimised for a zero-mesa formation of photonic crystal (PhC) trapping structures and smooth surfaces at the nanoscale level. Scaling up in area and throughput of the demonstrated approach is outlined. © 2024, CC BY.

关键词： Alumina

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