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Estimation Large- Scale Fading Channels for Transmit Orthogonal Pilot Reuse Sequences in Massive MIMO System

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arXiv 2022年

作者： Abdullah, Qazwan Shah, Nor Shahida Mohd Hamzah, Shipun Salh, Adeb Mohamad, Mahathir Nordin, Shahilah Abu, Maisarah Albaom, Mohammed Abdo Sadeq, Safwan Community College of Yareem IBB Yemen Faculty of Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Muar Pagoh Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Muar Pagoh Malaysia Malaysia Pulau Pinang Malaysia Faculty of Electronic and Computer Engineering University Technical Malaysia Malacca Malacca Malacca City Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Serdang Malaysia Department of Mechanical Engineering Universiti Teknologi Petronas Perak Malaysia

Massive multiple-input multiple-output (MIMO) is a critical technology for future fifth-generation (5G) systems. Reduced pilot contamination (PC) enhanced system performance, and reduced inter-cell interference and improved channel estimation. However, because the pilot sequence transmitted by users in a single cell to neighboring cells is not orthogonal, massive MIMO systems are still constrained. We propose channel evaluation using orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques to eliminate channel quality in end users with poor channel quality based on channel evaluation, large-scale shutdown evaluation, and analysis of maximum transmission efficiency. We derived the lower bounds on the downlink data rate (DR) and signal-to-interference noise ratio (SINR) that can be achieved based on PRS assignment to a group of users where the number of antenna elements mitigated the interference when the number of antennas reaches infinity. The channel coherence interval limitation, the orthogonal PRS cannot be allocated to all UEs in each cell. The short coherence intervals able to reduce the PC and improve the quality of channel. The results of the modelling showed that higher DR can be achieved due to better channel evaluation and lower loss. © 2022, CC BY.

关键词： Fading channels

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Wealth rheology

arXiv 2021年

作者： Burda, Zdzislaw Krawczyk, Malgorzata J. Malarz, Krzysztof Snarska, Malgorzata AGH University of Science and Technology Faculty of Physics and Applied Computer Science Mickiewicza 30 KrakówPL-30059 Poland Cracow University of Economics Department of Financial Markets Rakowicka 27 KrakówPL-31510 Poland

We study wealth rank correlations in a simple model of macroeconomy. To quantify rank correlations between wealth rankings at different times, we use Kendall's t and Spearman's r, Goodman-Kruskal's γ, and the lists' overlap ratio. We show that the dynamics of wealth flow and the speed of reshuffling in the ranking list depend on parameters of the model controlling the wealth exchange rate and the wealth growth volatility. As an example of the rheology of wealth in real data, we analyze the lists of the richest people in Poland, Germany, the USA and the world. © 2021, CC BY.

关键词： Elasticity

ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition

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ResNet-based Deep Neural Network using Transfer Learning for...

International Conference on Information technology (INCIT)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Animal activity recognition (AAR) is a challenging and complex task for monitoring animal behavior to determine their health conditions and physical responses. To address this requirement, AAR research aims to develop an internet of things (IoT) platform that anticipates animal behavior based primarily on sensor data collected from wearable devices connected to animals. Because of its superior effectiveness on multi-dimensional sensor data, deep learning (DL) currently dominates sensor-based AAR applications. Nevertheless, DL techniques require significantly more training time than other learning-based strategies. This study employs transfer learning (TL) to generalize the DL model to overcome the issue of training time, indicating significant promise for future research on animal activity identification. Furthermore, we apply the TL to allow the reusability of a pre-trained DL model on entirely new data without the time-consuming operations of model training and data labeling. We conducted experiments on a publicly available AAR benchmark dataset (SAR) utilizing deep residual networks with automatically learned features. Utilizing source data, our technique achieved an overall accuracy of 96.46% (dataset collected by the first sensor) and 98.40% (dataset collected by the second sensor) based on evaluating findings.

关键词： Training Deep learning Animals Wearable computers Transfer learning Animal behavior Activity recognition

Studies of Big Data Processing at Linear Accelerator Sources Using Machine Learning 9th

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Studies of Big Data Processing at Linear Accelerator Sources...

9th computer science On-line Conference, CSOC 2020

作者： Bawatna, Mohammed Green, Bertram Faculty of Computer Science Technische Universität Dresden01069 Germany Institute of Radiation Physics HZDR Dresden01328 Germany Magnet Science and Technology TallahasseeFL32310 United States

ISBN: (纸本)9783030519704

In linear accelerator sources such as the electron beam of the super-conducting linear accelerator at the radiation source Electron Linear accelerator for beams with high Brilliance and low Emittance (ELBE), different kinds of secondary radiation can be produced for various research purposes from materials science up to medicine. A variety of different beam detectors generate a huge amount of data, which take a great deal of computing power to capture and analyse. In this contribution, we will discuss the possibilities of using Machine Learning method to solve the big data challenges. Moreover, we will present a technique that employ the machine learning strategy for the diagnostics of high-field terahertz pulses generated at the ELBE accelerator with extremely flexible parameters such as repetition rate, pulse form and polarization. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

Optimizing electrochemical removal of perfluorooctanoic acid in landfill leachate using ceramic carbon foam electrodes by coupling CFD simulation and reactor design

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Chemosphere 2025年 376卷 144282页

作者： Pierpaoli, Mattia Jakóbczyk, Pawel Szopińska, Malgorzata Ryl, Jacek Giosué, Chiara Wróbel, Maciej Strugala, Gabriel Luczkiewicz, Aneta Fudala-Książek, Sylwia Bogdanowicz, Robert Faculty of Electronics Telecommunications and Informatics Department of Metrology and Optoelectronics Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland Faculty of Civil and Environmental Engineering Department of Environmental Engineering Technology Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland Faculty of Applied Physics and Mathematics Division of Electrochemistry and Surface Physical Chemistry Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland Università Politecnica Delle Marche Via Brecce Bianche 12 Ancona60131 Italy Faculty of Mechanical Engineering and Ship Technology Department of Materials Science and Technology Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland Faculty of Civil and Environmental Engineering Department of Sanitary Engineering Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland

Perfluorooctanoic acid (PFOA), a persistent and bioaccumulative pollutant classified as a 'forever chemical', poses a global environmental and health risk due to its widespread use and resistance to degradation. The development of effective and efficient removal technologies is crucial to mitigate its long-term impacts. In this study, we present a novel approach to address the growing concern of emerging contaminants, particularly PFOA, in landfill leachate. We investigate the use of ceramic carbon foam electrodes (CCFE) as a cost-effective and efficient alternative to traditional electrode materials for the electrochemical degradation of PFOA. Computed microtomography was used to reconstruct the actual three-dimensional geometries of the samples from which porosities were calculated. We also coupled computational fluid dynamics simulations of the actual geometries and optimized the working conditions to minimize pressure drop and improve treatment efficiency. Our design significantly reduces energy requirements by operating at lower potentials, a critical factor in sustainable waste management practices. The optimized CCFE system demonstrated superior performance in the degradation of PFOA in landfill leachate, offering a promising solution for the treatment of emerging contaminants. This study not only provides a viable method for mitigating the environmental impact of PFOA but also sets a precedent for the development of low-energy, high-efficiency treatment technologies for various persistent pollutants. In addition, the proposed solution, as part of closed-loop water systems, will enhance water reuse and recycling, thereby preserving and regenerating natural water bodies. © 2025 Elsevier Ltd

关键词： Leachate treatment

Unraveling the Role of Boron Dimers in the Electrical Anisotropy and Superconductivity in Boron-Doped Diamond

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SSRN

SSRN 2024年

作者： Sobaszek, Michal Kwon, Soonho Winiarski, Michal Rutkowski, Bogdan Ryl, Jacek Klimczuk, Tomasz Wang, Dongying Li, Xinwei Bockrath, Marc Bogdanowicz, Robert Goddard, William A. Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Metrology and Optoelectronics 11/12 Narutowicza Str. 80-233 Poland Materials and Process Simulation Center California Institute of Technology 1200 East California Blvd. CA91125 United States Gdańsk University of Technology Faculty of Applied Physics and Mathematics and Advanced Materials Centre Department of Solid State Physics 11/12 Narutowicza Str. Gdańsk80-233 Poland Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Al. A. Mickiewicza 30 Kraków30-059 Poland Advanced Materials Center Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland Department of Physics The Ohio State University ColumbusOH43210 United States

We use quantum mechanics (QM) to determine the states formed by B dopants in diamond. We find that isolated B sites prefer to form BB dimers and that the dimers pair up to form tetramers (BBCBB) that prefer to aggregate parallel to the (111) surface in the direction, one double layer below the H-terminated surface double layer. These tetramers lead to metallic character (Mott metal Insulator Transition) with holes in the valence band near the Γ point and electrons in the BBCBB tetramer promoted band along the X direction. Our experiments find very significant anisotropy in the superconductivity for boron-doped diamond thin films prepared with Microwave Plasma Assisted Chemical Vapor Deposition using deuterium-rich plasma. This leads to much higher conductivity in the X direction than the Y direction, as predicted by the QM. This phase transition to the anomalous phase is linked with the emergence of boson quantum entanglement states behaving as a bosonic insulating state. These anisotropic superconducting properties of the diamond film might enable applications such as single-photon detectors. We expect that this formation of a dirty superconductivity state is related to the BBCBB tetramers found in our QM calculations. © 2024, The Authors. All rights reserved.

关键词： Dimers

Highly tunable and strongly bound exciton in MoSi2N4 via strain engineering

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Physical Review B 2022年第19期105卷 195302-195302页

作者： Dan Liang Shi Xu Pengfei Lu Yongqing Cai State Key Laboratory of Information Photonics and Optical Communications and School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Macao SAR 999078 China Department of Physics and Chemistry Faculty of Science and Technology University of Macau Macao SAR 999078 China

Motivated by the recently synthesized layered material MoSi2N4, we investigate excitonic response of quasiparticle of monolayer MoSi2N4 by using G0W0 and Bethe-Salpeter equation calculations. With a dually sandwiched structure consisting of a central MoN2 layer analog of 2H−MoS2 capped with silicon-nitrogen (SiN) honeycomb outer layers, MoSi2N4 possesses frontier orbitals confined at the central MoN2 layer with similar subvalley at the K point as 2H−MoS2. The valley splitting (∼130meV) due to the spin-orbital coupling gives rise to a doublet in the spectrum. Excitons in MoSi2N4 show a strong binding energy up to 0.95 eV with the optical band gap of 2.44 eV. Both electronic and optical gaps are highly sensitive to tensile strains and become redshifted, albeit a marginal change of exciton binding energy. With the protection of capped SiN layers, quantum confined excitons in MoSi2N4 without the need of additional passivation layer like BN would provide a bright platform for robust emission with partially screened disturbance from environment.

关键词： Electronic structure Excitons 2-dimensional systems Semiconductor compounds Density functional calculations GW method Strain engineering

Quantum Dynamics Simulation of the Advection-Diffusion Equation

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arXiv 2025年

作者： Alipanah, Hirad Zhang, Feng Yao, Yongxin Thompson, Richard Nguyen, Nam Liu, Junyu Givi, Peyman McDermott, Brian J. Mendoza-Arenas, Juan José Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15261 United States Ames National Laboratory U.S. Department of Energy AmesIA50011 United States Department of Physics and Astronomy Iowa State University AmesIA50011 United States Integrated Vehicle Systems Applied Mathematics Boeing Research & Technology HuntsvilleAL35824 United States Integrated Vehicle Systems Applied Mathematics Boeing Research & Technology Huntington BeachCA92647 United States Department of Computer Science University of Pittsburgh PittsburghPA15261 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15261 United States Naval Nuclear Laboratory SchenectadyNY12301 United States

The advection-diffusion equation is simulated on a superconducting quantum computer via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and an actual quantum hardware (IBM Fez). The former emulates the latter without the noise. The predicted results are compared with direct numerical simulation (DNS) data with infidelities of the order 10−5. In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and the lowest circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelming large number of operations. Meanwhile, AVQDS and VarQTE can be executed, but suffer from large errors due to significant hardware noise. These algorithms present a new paradigm for computational transport phenomena on quantum computers. © 2025, CC BY.

关键词： Hamiltonians

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish

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arXiv 2025年

作者： Lango, Marta Naglik, Borys Lango, Mateusz Naglik, Iwo Poznan University of Technology Faculty of Computing and Telecommunications Poland Adam Mickiewicz University Faculty of Mathematics and Computer Science Poland Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic deepsense.ai Poland

Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learning methods being proposed to address it, the number of datasets for ASTE is very limited. In particular, no dataset is available for any of the Slavic languages. In this paper, we present two new datasets for ASTE containing customer opinions about hotels and purchased products expressed in Polish. We also perform experiments with two ASTE techniques combined with two large language models for Polish to investigate their performance and the difficulty of the assembled datasets. The new datasets are available under a permissive licence and have the same file format as the English datasets, facilitating their use in future research. © 2025, CC BY.

关键词： Emotion Recognition