检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Bernstein-type theorem for zero mean curvature hypersurfaces without time-like points in lorentz-minkowski space

arXiv

引用

arXiv 2019年

作者： Akamine, S. Honda, A. Umehara, M. Yamada, K. Graduate School of Mathematics Nagoya University Chikusa-ku Nagoya464-8602 Japan Department of Applied Mathematics Faculty of Engineering Yokohama National University 79-5 Tokiwadai Hodogaya Yokohama240-8501 Japan Department of Mathematical and Computing Sciences Tokyo Institute of Technology Tokyo152-8552 Japan Department of Mathematics Tokyo Institute of Technology Tokyo152-8551 Japan

Calabi and Cheng-Yau's Bernstein-type theorem asserts that an entire zero mean curvature graph in Lorentz-Minkowski (n + 1)-space Rn1+1 which admits only space-like points is a hyperplane. Recently, the third and fourth authors proved a line theorem for hypersurfaces at their degenerate light-like points. Using this, we give an improvement of the Bernstein-type theorem, and we show that an entire zero mean curvature graph in Rn1+1 consisting only of space-like or light-like points is a hyperplane. This is a generalization of the first, third and fourth authors' previous result for n = *** Codes 53A10, 53C42 Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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A new class of nonlinear conjugate gradient with global convergence properties

引用

Materials Today: Proceedings 2018年第10期5卷 22029-22035页

作者： Srimazzura Basri Mustafa Mamat Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus of Tembila 22200 Terengganu Darul Iman Malaysia Faculty of Applied Sciences & Technology Universiti Tun Hussein Onn Malaysia 86400 Johor Darul Takzim Malaysia

Nonlinear conjugate gradient methods are widely used in solving large scale unconstrained optimization. Their wide application in many fields are due to their low memory. Numerous studies have been conducted recently to improve these methods. In this paper, a new class of conjugate gradient coefficients that possess global convergence properties is proposed. The global convergence result using exact line searches are discussed. Numerical result shows that the proposed method is more efficient compared with the other classical conjugate gradient.

关键词： unconstrained optimization conjugate gradient search direction global convergence

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Bayesian stochastic multi-scale analysis via energy considerations

arXiv

引用

arXiv 2019年

作者： Sarfaraz, M.S. Rosić, B. Matthies, H.G. Ibrahimbegović, A. Institute of Scientific Computing TU Braunschweig Muehlenpfordtstrasse 23 Braunschweig38106 Germany Applied Mechanics and Data Analysis Faculty of Engineering Technology University of Twente P.O. Box 217 Enschede7500 Netherlands Centre de Recherches Lab. Roberval Mecanique U.C.T. Compiegne60203 France

In this paper physical multi-scale processes governed by their own principles for evolution or equilibrium on each scale are coupled by matching the stored and dissipated energy, in line with the Hill-Mandel principle. In our view the correct representations of stored and dissipated energy is essential to the representation irreversible material behaviour, and this matching is also used for upscaling. The small scales, here the meso-scale, is assumed to be described probabilistically, and so on the macroscale also a probabilistic model is identified in a Bayesian setting, reflecting the randomness of the meso-scale, the loss of resolution due to upscaling, and the uncertainty involved in the Bayesian process. In this way multi-scale processes become hierarchical systems in which the information is transferred across the scales by Bayesian identification on coarser levels. The quantities to be matched on the coarse-scale model are the stored and dissipated energies. In this way probability distributions of macro-scale material parameters are determined, and not only in the elastic region, but also for the irreversible and highly nonlinear elasto-damage regimes, refelcting the aleatory uncetainty at the meso-scale level. For this purpose high dimensional meso-scale stochastic simulations in a non-intrusive functional approximation forms are mapped to the macro-scale models in an approximative manner by employing a generalised version of the Kalman filter. To reduce the overall computational cost, a model reduction of the meso-scale simulation is achieved by combining the unsupervised learning techniques based on the Bayesian copula variartional inference with the classical functional approximation forms from the field of uncertainty quantification. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic models

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Some bounds on zeroth-order general randic index

arXiv

引用

arXiv 2019年

作者： Jamil, Muhammad Kamran Tomescu, Ioan Imran, Muhammad Department of Mathematical Sciences United Arab University Al Ain United Arab Emirates Faculty of Mathematics and Computer Science University of Bucharest Bucharest Romania Department of Mathematics School of Natural Sciences National University of Science and Technology Islamabad Pakistan Department of Mathematics Riphah Institute of Computing and Applied Sciences Riphah International University Lahore

For a graph G without isolated vertices, the inverse degree of a graph G is defined as ID(G) = £u∈V(G)d(u)-1where d(u) is the number of vertices adjacent to the vertex u in G By replacing -1 by any non-zero real number we obtain zeroth-order general Randic index, i.e. 0Rγ(G) = T,u∈V(G)d(u)γwhere γ is any non-zero real number. In [12], Xu et. al. determined some upper and lower bounds on the inverse degree for a connected graph G in terms of chromatic number, clique number, connectivity, number of cut edges. In this paper, we extend their results and investigate if the same results hold for γ Copyright © 2019, The Authors. All rights reserved.

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Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection

引用

IOP Conference Series: Materials Science and Engineering 2020年第1期864卷

作者： Muhalim Mohamed Amin Anazida Zainal Nurulhuda Firdaus Mohd. Azmi Nor Azizah Ali School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Malaysia Cyber Threat Intelligence Lab Information Assurance & Security Research Group UTM Johor Malaysia Razak Faculty of Technology and Informatics UTM Kuala Lumpur Malaysia Applied Industrial Analytics Research Group UTM Johor Malaysia

Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving the highest accuracy (97.44%) compared to other classifiers.

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Metalla-Carbaporphyrinoids Consisting of an Acyclic N-Confused Tetrapyrrole Analogue Served as Stable Near-Infrared-II Dyes

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Angewandte Chemie 2024年第23期136卷

作者： Dr. Biju Basumatary Hidetoshi Tsuruda Prof. Dr. Dariusz W. Szczepanik Jiyeon Lee Jaehyeok Ryu Dr. Shigeki Mori Kyo Yamagata Prof. Dr. Takayuki Tanaka Dr. Atsuya Muranaka Prof. Masanobu Uchiyama Prof. Dr. Jiwon Kim Prof. Dr. Masatoshi Ishida Prof. Dr. Hiroyuki Furuta Department of Applied Chemistry Graduate School of Engineering Kyushu University Fukuoka 819-0395 Japan Department of Theoretical Chemistry Jagiellonian University Faculty of Chemistry Gronostajowa 2 30-387 Krakow Poland School of Integrated Technology College of Computing Integrated Science and Engineering Division Underwood International College Integrative Biotechnology and Translational Medicine Graduate School Yonsei University Incheon 21983 Korea Advanced Research Support Center Ehime University Matsuyama 790-8577 Japan Department of Molecular Engineering Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8195 Japan Molecular Structure Characterization Unit RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan Graduate School of Pharmaceutical Sciences The University of Tokyo Tokyo 113-0033 Japan Department of Chemistry Graduate School of Sciences Tokyo Metropolitan University Hachioji 192-0397 Japan

We present herein the synthesis of novel pseudo -metalla-carbaporphyrinoid species ( 1M : M=Pd and Pt) achieved through the inner coordination of palladium(II) and platinum(II) with an acyclic N-confused tetrapyrrin analogue. Despite their tetrapyrrole frameworks being small, akin to well-known porphyrins, these species exhibit an unusually narrow HOMO–LUMO gap, resulting in an unprecedentedly low-energy absorption in the second near-infrared (NIR-II) region. Density functional theory (DFT) calculations revealed unique d π -p π -conjugated electronic structures involving the metal d π -ligand p π hybridized molecular orbitals of 1M . Magnetic circular dichroism (MCD) spectroscopy confirmed distinct electronic structures. Remarkably, the complexes feature an open-metal coordination site in the peripheral NN dipyrrin site, forming hetero-metal complexes ( 1Pd-BF 2 and 1Pt-BF 2 ) through boron difluoride complexation. The resulting hetero metalla-carbaporphyrinoid species displayed further redshifted NIR-II absorption, highly efficient photothermal conversion efficiencies ( η ; 62–65 %), and exceptional photostability. Despite the challenges associated with the theoretical and experimental assessment of d π -p π -conjugated metalla-aromaticity in relatively larger (more than 18π electrons) polycyclic ring systems, these organometallic planar tetrapyrrole systems could serve as potential molecular platforms for aromaticity-relevant NIR-II dyes.

关键词： porphyrinoid N-confusion near-infrared photothermal conversion metalla-(anti)aromaticity

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Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

引用

Physics of Plasmas 2024年第5期31卷

作者： Kuramitsu, Y. Taguchi, T. Nikaido, F. Minami, T. Hihara, T. Suzuki, S. Oda, K. Kuramoto, K. Yasui, T. Abe, Y. Ibano, K. Takabe, H. Chu, C.M. Wu, K.T. Woon, W.Y. Chen, S.H. Jao, C.S. Chen, Y.C. Liu, Y.L. Morace, A. Yogo, A. Arikawa, Y. Kohri, H. Tokiyasu, A. Kodaira, S. Kusumoto, T. Kanasaki, M. Asai, T. Fukuda, Y. Kondo, K. Kiriyama, H. Hayakawa, T. Tanaka, S.J. Isayama, S. Watamura, N. Suzuki, H. Kumar, H.S. Ohnishi, N. Pikuz, T. Filippov, E. Sakai, K. Yasuhara, R. Nakata, M. Ishikawa, R. Hoshi, T. Mizuta, A. Bolouki, N. Saura, N. Benkadda, S. Koenig, M. Hamaguchi, S. Graduate School of Engineering Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Institute of Laser Engineering Osaka University 2-6 Yamadaoka Osaka Suita565-0871 Japan 8-1-7 Umemidai Kyoto Kizugawa619-0215 Japan Department of Physics National Central University No. 300 Jhongda Rd. Taoyuan Jhongli320 Taiwan Department of Physics National Cheng Kung University Tainan701 Taiwan National Center for High-Performance Computing National Applied Research Laboratories Tainan City711010 Taiwan Institute of Space and Plasma Sciences National Cheng Kung University Tainan701 Taiwan Research Center for Nuclear Physics Osaka University 10-1 Mihogaoka Osaka Ibaraki567-0047 Japan Research Center for Accelerator and Radioisotope Science Tohoku University 1-2-1 Mikamine Taihaku-ku Miyagi Sendai982-0826 Japan Inage Chiba263-8555 Japan Graduate School of Maritime Sciences Kobe University Kobe658-0022 Japan Department of Physical Sciences Aoyama Gakuin University 5-10-1 Fuchinobe Kanagawa Sagamihara252-5258 Japan Department of Advanced Environmental Science and Engineering Kyushu University 6-1 Kasuga-Kohen Fukuoka Kasuga816-8580 Japan Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka Suita565-0871 Japan Department of Aerospace Engineering Tohoku University 6-6-01 Aramakiazaaoba Aoba-ku Sendai980-8579 Japan Institute for Open and Transdisciplinary Research Initiatives Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Villamayor Salamanca37185 Spain National Institute for Fusion Science 322-6 Oroshicho Gifu Toki509-5292 Japan Faculty of Arts and Sciences Komazawa University 1-23-1 Komazawa Setagaya-ku Tokyo154-8525 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Department of Plasma Physics and Technology Faculty of Science Masaryk University Kotlářská 2 Brno611 37 Czech Republic Aix-Marseille Université CNRS PIIM UMR 7345 Marseille France LULI CN

We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions. © 2024 Author(s).

关键词： Ions

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Energy harvesting in 5G networks: Taxonomy, requirements, challenges, and future directions

arXiv

引用

arXiv 2019年

作者： Imran, Muhammad Khan, Latif U. Yaqoob, Ibrar Ahmed, Ejaz Qureshi, Muhammad Ahsan Ahmed, Arif College of Applied Computer Science King Saud University Riyadh Saudi Arabia Department of Telecommunication Engineering University of Engineering & Technology Mardan Pakistan Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of University of Malaya Kuala Lumpur50603 Malaysia Faculty of Computing and Information technology University of Jeddah Khulais Saudi Arabia Department of Computer Science & Engineering National Institute of Technology Silchar Silchar India

Consciousness of energy saving is increasing in fifth-generation (5G) wireless networks due to the high energy consumption issue. Energy harvesting technology is a possible appealing solution for ultimately prolonging the lifetime of devices and networks. Although considerable research efforts have been conducted in the context of using energy harvesting technology in 5G wireless networks, these efforts are in their infancy, and a tutorial on this topic is still lacking. This study aims to discuss the beneficial role of energy harvesting technology in 5G networks. We categorize and classify the literature available on energy harvesting in 5G networks by devising a taxonomy based on energy sources;energy harvesting devices, phases, and models;energy conversion methods, and energy propagation medium. The key requirements for enabling energy harvesting in 5G networks are also outlined. Several core research challenges that remain to be addressed are discussed. Furthermore, future research directions are provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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