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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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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

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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

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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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Multi-modal vision-language model for generalizable annotation-free pathology localization and clinical diagnosis

arXiv

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arXiv 2024年

作者： Yang, Hao Zhou, Hong-Yu Liu, Jiarun Huang, Weijian Li, Zhihuan Gao, Yuanxu Li, Cheng Liu, Qiegen Liang, Yong Yang, Qi Wu, Song Tan, Tao Zheng, Hairong Zhang, Kang Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Pengcheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China National Clinical Research Center for Ocular Diseases Eye Hospital Advanced Institute for Eye Health and Diseases Wenzhou Medical University Wenzhou China Department of Computer Science The University of Hong Kong Pokfulam Hong Kong Institute for Artificial Intelligence in Medicine Faculty of Medicine Macau University of Science and Technology China Faculty of Applied Sciences Macao Polytechnic University China Department of Electronic Information Engineering Nanchang University Nanchang China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Beijing China Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Department of Urology South China Hospital Medical School Shenzhen University Shenzhen China Chinese Medicine Guangdong Laboratory Hengqin China

Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics. However, existing deep learning models heavily rely on expert annotations and lack generalization capabilities in open clinical environments. In this study, we present a generalizable vision-language model for Annotation-Free pathology Localization (AFLoc). The core strength of AFLoc lies in its extensive multi-level semantic structure-based contrastive learning, which comprehensively aligns multi-granularity medical concepts from reports with abundant image features, to adapt to the diverse expressions of pathologies and unseen pathologies without the reliance on image annotations from experts. We conducted primary experiments on a dataset of 220K pairs of image-report chest X-ray images, and performed extensive validation across six external datasets encompassing 20 types of chest pathologies. The results demonstrate that AFLoc outperforms state-of-the-art methods in both annotation-free localization and classification tasks. Additionally, we assessed the generalizability of AFLoc on other modalities, including histopathology and retinal fundus images. Extensive experiments show that AFLoc exhibits robust generalization capabilities, even surpassing human benchmarks in localizing five different types of pathological images. These results highlight the potential of AFLoc in reducing annotation requirements and its applicability in complex clinical environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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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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Matrix-Variate Restricted Boltzmann Machine Classification Model 11th

Matrix-Variate Restricted Boltzmann Machine Classification M...

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11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Li, Jinghua Tian, Pengyu Kong, Dehui Wang, Lichun Wang, Shaofan Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China College of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116620 China

ISBN: (纸本)9783030322151

Recently, Restricted Boltzmann Machine (RBM) has demonstrated excellent capacity of modelling vector variable. A variant of RBM, Matrix-variate Restricted Boltzmann Machine (MVRBM), extends the ability of RBM and is able to model matrix-variate data directly without vectorized process. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers are necessary. This paper proposes a Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM) to classify 2D data directly. In the novel ClassMVRBM, classification constraint is introduced to MVRBM. On one hand, the features extracted by MVRBM are more discriminative, on the other hand, the proposed model can be directly used to classify. Experiments on some publicly available databases demonstrate that the classification performance of ClassMVRBM has been largely improved, resulting in higher image classification accuracy than conventional unsupervised RBM, its variants and Restricted Boltzmann Machine Classification Model (ClassRBM). © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Deep neural networks

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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