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Optimum elements selection in IRS-assisted UAV communications with phase errors

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TechRxiv

TechRxiv 2021年

作者： Jangsher, Sobia Al-Dweik, Arafat Al-Jarrah, Mohammd Ahmad Alusa, Emad Alouini, Mohamed-Slim Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Wireless and Signal Processing Lab Institute of Space Technology Islamabad Pakistan School of Electrical and Electronic Engineering University of manchester ManchesterM13 9PL United Kingdom Center for Cyber Physical Systems Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Western University LondonON Canada Thuwal Makkah Province Saudi Arabia

This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction. © 2021, CC BY.

关键词： Bit error rate

Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and transfer learning

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arXiv 2021年

作者： Ghassemi, Navid Shoeibi, Afshin Khodatars, Marjane Heras, Jonathan Rahimi, Alireza Zare, Assef Pachori, Ram Bilas Gorriz, J. Manuel Clinical Studies Lab Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ferdowsi University of Mashhad Computer Engineering Department Mashhad Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Signal Theory Networking and Communications Universidad de Granada Spain

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. © 2021, CC BY-NC-ND.

关键词： Diagnosis

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

Optical MIMO Transmission focusing on photonic lanterns and optical couplers 1

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14th International Joint Conference on e-Business and Telecommunications, ICETE 2017

作者： Ahrens, Andreas Sandmann, André Lochmann, Steffen Department of Electrical Engineering and Computer Science Communications Signal Processing Group Hochschule Wismar University of Applied Sciences: Technology Business and Design Philipp-Müller-Straße 14 Wismar23966 Germany

ISBN: (数字)9783030110390

ISBN: (纸本)9783030110383

The exploitation of the spatial domain with the concept of multiple-input multiple-output (MIMO) is a promising approach to further improve the cost efficiency in fiber-optic communications. Historically, the first optical MIMO systems utilized multi-mode couplers for the purpose of mode multiplexing (MUX) and demultiplexing (DEMUX). However, their high insertion losses and asymmetries demand for alternative components. Nowadays, next to optical couplers, photonic lanterns have become considerably more attractive offering low insertion losses and being able to excite individual modes. Therefore, they are in the focus of this contribution. A setup of 6-port photonic lanterns is evaluated by measurements and compared with other multiplexing components. The measurement results and the simulated bit-error rate performances highlight that photonic lanterns are well-suited for optical MIMO communications. © Springer Nature Switzerland AG 2019.

关键词： Insertion losses

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

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arXiv 2022年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Khosravi, Abbas Zare, Assef Gorriz, Juan M. Chale-Chale, Amir Hossein Khadem, Ali Rajendra Acharya, U. Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University Geelong Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy. © 2022, CC BY-NC-ND.

关键词： Support vector machines

Distributed sparse SGD with majority voting

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arXiv 2020年

作者： Özfatura, Kerem Ozfatura, Emre Gündüz, Deniz Department of Computer Science Ozyegin University Istanbul Turkey Information Processing and Communications Lab Dept. of Electrical and Electronic Engineering Imperial College London London United Kingdom

Distributed learning, particularly variants of distributed stochastic gradient descent (DSGD), are widely employed to speed up training by leveraging computational resources of several workers. However, in practise, communication delay becomes a bottleneck due to the significant amount of information that needs to be exchanged between the workers and the parameter server. One of the most efficient strategies to mitigate the communication bottleneck is top-K sparsification. However, top-K sparsification requires additional communication load to represent the sparsity pattern, and the mismatch between the sparsity patterns of the workers prevents exploitation of efficient communication protocols. To address these issues, we introduce a novel majority voting based sparse communication strategy, in which the workers first seek a consensus on the structure of the sparse representation. This strategy provides a significant reduction in the communication load and allows using the same sparsity level in both communication directions. Through extensive simulations on the CIFAR-10 dataset, we show that it is possible to achieve up to ×4000 compression without any loss in the test accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

Correction: Recurrent neural networks for enhanced joint channel estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks

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EURASIP Journal on Advances in signal processing 2023年第1期2023卷 1-1页

作者： Al‑Makhlasawy, Rasha M. Khairy, Mayada El‑Shafai, Walid Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

Reconfigurable Intelligent Surface Assisted High-Speed Train communications: Coverage Performance Analysis and Placement Optimization

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

EEG-Based Preference Classification for Neuromarketing Application

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Injamamul Haque Sourov Faiyaz Alvi Ahmed Md. Tawhid Islam Opu Aunnoy K. Mutasim M. Raihanul Bashar Rayhan Sardar Tipu Md. Ashraful Amin Md. Kafiul Islam Biomedical Instrumentation and Signal Processing Lab Department of Electrical and Electronic Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd Agency Lab Department of Computer Science and Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd

Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

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