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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Optical transmitter bandwidth requirements for POF telecom links

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WSEAS Transactions on Circuits and Systems 2005年第11期4卷 1608-1613页

作者： Louvros, S. Ioannou, K. Vassiliadis, B. Drossos, L. Kotsopoulos, S. Wireless Telecommunication Laboratory Department of Electrical and Computer Engineering University of Patras Rion 26500 Patras Greece Computer Science Hellenic Open University 13-15 Tsamadou st. GR-26222 Patras Greece Department of Applied Informatics in Management and Economy Technical Education Institute of Messologi Messologi Greece

Light emitting diodes (LED) are usually the alternative optoelectronic devices for optical communications with moderate bandwidth efficiency and bit rates, compared to semiconductor lasers. Laser applications dominate the long distance communication networks. However LED seems to be a very good solution, especially in very short distance communication networks, where Plastic Optical Fibers, (POF) are used as the optical transmission medium. This paper investigates a qualitative technique of calculating the bandwidth, based on first principles of semiconductor physics. This technique is more convenient for a first approach of the subject on optical communication networks and its simplicity provides a very useful analytical tool. Finally these results are compared with the existing experimental ones.

关键词： Optical links

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Full-Duplex Spectrum Sharing in Cooperative Single Carrier Systems

Full-Duplex Spectrum Sharing in Cooperative Single Carrier S...

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作者： Deng, Yansha Kim, Kyeong Jin Duong, Trung Q. Elkashlan, Maged Karagiannidis, George K. Nallanathan, Arumugam Department of informatics King's College London LondonWC2R 2LS United Kingdom Mitsubishi Electric Research Laboratories CambridgeMA02139 United States School of Electronics Electrical Engineering and Computer Science Queen's Belfast University BelfastBT7 1NN United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Thessaloniki54 124 Greece

We propose cyclic prefix single carrier full-duplex transmission in amplify-and-forward cooperative spectrum sharing networks to achieve multipath diversity and full-duplex spectral efficiency. Integrating full-duplex transmission into cooperative spectrum sharing systems results in two intrinsic problems: 1) the residual loop interference occurs between the transmit and the receive antennas at the secondary relays and 2) the primary users simultaneously suffer interference from the secondary source (SS) and the secondary relays (SRs). Thus, examining the effects of residual loop interference under peak interference power constraint at the primary users and maximum transmit power constraints at the SS and the SRs is a particularly challenging problem in frequency selective fading channels. To do so, we derive and quantitatively compare the lower bounds on the outage probability and the corresponding asymptotic outage probability for max-min relay selection, partial relay selection, and maximum interference relay selection policies in frequency selective fading channels. To facilitate comparison, we provide the corresponding analysis for half-duplex. Our results show two complementary regions, named as the signal-to-noise ratio (SNR) dominant region and the residual loop interference dominant region, where the multipath diversity and spatial diversity can be achievable only in the SNR dominant region, however the diversity gain collapses to zero in the residual loop interference dominant region. © 2016 IEEE.

关键词： Signal to noise ratio

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Evolutionary identification and synthesis of predictive models

Evolutionary identification and synthesis of predictive mode...

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Conference on Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems, Nostradamus 2012

作者： Zelinka, Ivan Skanderova, Lenka Chadli, Mohammed Brandejsky, Tomas Senkerik, Roman VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science 17. listopadu 15 708 33 Ostrava-Poruba Czech Republic University of Picardie Jules Verne 7 Rue du Moulin Neuf 80000 Amiens France Faculty of Transportation Sciences CTU in Prague Prague Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T.G. Masaryka 5555 760 01 Zlin Czech Republic

ISBN: (纸本)9783642332265

In this contribution is demonstrated use of two evolutionary algorithms on parameter identification of selected predictive models. Both algorithms were used to indentify parameter of pre-selected ARMA models. At the end are discussed possibilities of use of synthesis of predictive models by means of methods of symbolic regression that has successfully been used on chaotic system identification by means of evolutionary algorithms on measured data. © 2013 Springer-Verlag.

关键词： Evolutionary algorithms

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Merging augmented reality and anatomicaly correct 3D models in the development of a training tool for endotracheal intubation

Merging augmented reality and anatomicaly correct 3D models ...

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IEEE International Symposium on Biomedical Imaging, ISBI 2002

作者： Rolland, Jannick Meyer, Catherine Davis, Larry Hamza-Lup, Felix Norflect, Jack Imielinska, Celina Kerner, Karen F. School of Optics/CREOL OrlandoFL United States School of Electrical Engineering and Computer Science OrlandoFL United States STRICOM University of Central Florida and Research Park OrlandoFL United States Office of Scholarly Resources Columbia University College of Physicians and Surgeons New YorkNY United States Department of Medical Informatics Columbia University College of Physicians and Surgeons New YorkNY United States Department of Computer Science Columbia University New YorkNY United States

ISBN: (纸本)078037584X

Augmented reality is often used for medical training systems in which the user visualizes 3D 'information superimposed on the real world. In this context, we introduce a augmented reality tool to train the medical practitioner handeye coordination in performing critical procedures such as endotracheal intubation. © 2002 IEEE.

关键词： Augmented reality

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Characterizing Continual Learning Scenarios and Strategies for Audio Analysis

arXiv

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arXiv 2024年

作者： Bhatt, Ruchi Kumari, Pratibha Mahapatra, Dwarikanath Saddik, Abdulmotaleb El Saini, Mukesh The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India The Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Inception Institute of AI Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Canada The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India Abu Dhabi United Arab Emirates

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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Reconfigurable proximity coupled patch antenna using magnetic bias

Reconfigurable proximity coupled patch antenna using magneti...

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Antennas and Propagation Conference, LAPC, Loughborough

作者： E. Andreou T. Zervos F. Lazarakis A.A. Alexandridis K. Dangakis E. Varouti G. Fikioris J.C. Vardaxoglou Institute of Informatics & Telecommunications National Centre for Scientific Research “Demokritos” Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Institute for Advanced Materials Physicochemical Processes Nanotechnology & Microsystems National Centre for Scientific Research “Demokritos” Athens Greece School of Electronic Electrical and Systems Engineering Loughborough University Loughborough UK

In this paper, we present the design, fabrication and characterization measurements of a proximity-coupled fed patch antenna that uses a ferrimagnetic compound, Yttrium Iron Garnet (YIG), as part of its substrate. External magnetic field is applied in order to achieve reconfigurability. Specifically, by applying a suitable external magnetic field, the linearly polarized single-band antenna exhibits dual-band behavior. Furthermore, it is proved via simulation and experimental measurements that we can achieve antenna polarization reconfigurability.

关键词： Antenna measurements Antennas Magnetic resonance Magnetic field measurement Compounds Magnetic fields

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Special issue on the 2nd PGTS 2002

Special issue on the 2nd PGTS 2002

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作者： Kuhn, Paul J. Lehnert, Ralf Pioro, Michal Wozniak, Josef Killat, Ulrich Institute of Communication Networks and Computer Engineering University of Stuttgart Pfaffenwaldring 47 70569 Stuttgart Germany Technische Universitaet Dresden Lehrsthul fuer Telekommunikation Department of Electrical Engineering Telecommunications Mommsenstr. 13 D-01062 Dresden Germany Department of Computer Networks and Switching Institute of Telecommunications Warsaw University of Technology Warsaw Poland Department of Communication Systems Lund University PO Box 118 SE-22100 Lund Sweden Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics 11/12 Narutowicza Str 80-952 Gdansk Poland Kommunikationsnetze AB 4-06 TU Hamburg-Harburg D-21071 Hamburg Germany

No abstract available

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Covariance estimation from compressive data partitions using a projected gradient-based algorithm

arXiv

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arXiv 2021年

作者： Monsalve, Jonathan Ramirez, Juan Esnaola, Iñaki Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Computer Science Universidad Rey Juan Carlos Madrid28933 Spain Department of Automatic Control and Systems Engineering The University of Sheffield Western Bank Sheffield United Kingdom Department Systems Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia

Covariance matrix estimation techniques require high acquisition costs that challenge the sampling systems’ storing and transmission capabilities. For this reason, various acquisition approaches have been developed to simultaneously sense and compress the relevant information of the signal using random projections. However, estimating the covariance matrix from the random projections is an ill-posed problem that requires further information about the data, such as sparsity, low rank, or stationary behavior. Furthermore, this approach fails using high compression ratios. Therefore, this paper proposes an algorithm based on the projected gradient method to recover a low-rank or Toeplitz approximation of the covariance matrix. The proposed algorithm divides the data into subsets projected onto different subspaces, assuming that each subset contains an approximation of the signal statistics, improving the inverse problem’s condition. The error induced by this assumption is analytically derived along with the convergence guarantees of the proposed method. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images with high compression ratios (8 − 15% approx) in noisy scenarios. Additionally, simulations and theoretical results show that filtering the gradient reduces the estimator’s error recovering up to twice the number of eigenvectors. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

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