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A Multi-Carrier Deep Learning CSK Communication System

IEEE Communications Letters 2025年第6期29卷 1191-1195页

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

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Semi-implicit Lagrangian Voronoi Approximation for the incompressible Navier-Stokes equations

arXiv

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arXiv 2024年

作者： Kincl, Ondřej Peshkov, Ilya Boscheri, Walter Laboratory of Applied Mathematics DICAM University of Trento via Mesiano 77 Trento38123 Italy Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara Italy

We introduce Semi-Implicit Lagrangian Voronoi Approximation (SILVA), a novel numerical method for the solution of the incompressible Euler and Navier-Stokes equations, which combines the efficiency of semi-implicit time marching schemes with the robustness of time-dependent Voronoi tessellations. In SILVA, the numerical solution is stored at particles, which move with the fluid velocity and also play the role of the generators of the computational mesh. The Voronoi mesh is rapidly regenerated at each time step, allowing large deformations with topology changes. As opposed to the reconnection-based Arbitrary-Lagrangian-Eulerian schemes, we need no remapping stage. A semi-implicit scheme is devised in the context of moving Voronoi meshes to project the velocity field onto a divergence-free manifold. We validate SILVA by illustrative benchmarks, including viscous, inviscid, and multi-phase flows. Compared to its closest competitor, the Incompressible Smoothed Particle Hydrodynamics (ISPH) method, SILVA offers a sparser stiffness matrix and facilitates the implementation of no-slip and free-slip boundary conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stiffness matrix

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Broadband Sheet Parametric Oscillator for x^((2)) Optical Frequency Comb Generation via Cavity Phase Matching

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Chinese Physics Letters 2021年第6期38卷 35-39页

作者： Xin Ni Kunpeng Jia Xiaohan Wang Huaying Liu Jian Guo Shu-Wei Huang Baicheng Yao Nicolo Sernicola Zhenlin Wang Xinjie Lv Gang Zhao Zhenda Xie Shi-Ning Zhu National Laboratory of Solid State Microstructures School of Electronic Science and EngineeringSchool of Physicsand College of Engineering and Applied SciencesNanjing UniversityNanjing 210093China Department of Electrical Computerand Energy EngineeringUniversity of Colorado BoulderBoulderCO 80301USA Key Laboratory of Optical Fiber Sensing and Communications(Ministry of Education) University of Electronic Science and Technology of ChinaChengdu 611731China Institute for Optics Information and PhotonicsFriedrich-Alexander-Universitat Erlangen-NürnbergSchloßplatz 4Erlangen 91054Germany

We demonstrate a broadband optical parametric oscillation,using a sheet cavity,via cavity phase-matching.A21.2 THz broad comb-like spectrum is achieved,with a uniform line spacing of 133.0 GHz,despite a relatively large dispersion of 275.4 fs^(2)/mm around 1064 *** 22.6% high slope efficiency,and 14.9 kW peak power handling,this sheet optical parametric oscillator can be further developed for x^((2)) comb.

关键词： cavity parametric oscillator

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Spectrochemical, medicinal, and toxicological studies of ketoprofen and its newly designed analogs;quantum chemical, and drug discovery approach

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Informatics in Medicine Unlocked 2023年 43卷

作者： Afrin, Mst. Farhana Kabir, Emranul Noyon, M.R.O. Khan Bhuiyan, Md Mosharef H. Shimu, Mst Sharmin Sultana Alam, Md Jahangir Uzzaman, Monir Talukder, Md Walid Hossain Department of Chemistry Carmichael College Rangpur 5400 Bangladesh Department of Electrical and Electronic Engineering International Islamic University Chittagong Chittagong 4338 Bangladesh Faculty of Science Department of Chemistry University of Chittagong Chittagong 4331 Bangladesh Department of Drug Design Computer in Chemistry and Medicine Laboratory Dhaka Bangladesh Department of Genetic Engineering and Biotechnology University of Rajshahi Rajshahi 6205 Bangladesh Faculty of Science Department of Applied Chemistry and Chemical Engineering University of Chittagong Chittagong 4331 Bangladesh

Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used as analgesics, antipyretics, anti-clotting, and anti-inflammatory agents. One such NSAID, ketoprofen (KTP), is prescribed to treat pain, sprains, menstrual irregularities, osteoarthritis (OA), and rheumatoid arthritis (RA). By inhibiting cyclooxygenase, it halts prostaglandin synthesis. However, it exhibits significant side effects in humans and other living things. Herein, the chemical structure of KTP has been modified to search for improved chemical and medicinal effects. Density functional theory (DFT) has been employed for geometry optimization. Molecular docking and molecular dynamics (MD) simulations were conducted to investigate the binding affinity and stability of the protein-drug complex with prostaglandin synthase protein. ADMET predictions were carried out to calculate and compare the pharmacokinetic behavior of KTP and its new analogs. Spectral and chemical results support the structural conformation of the new analogs, all of which exhibit improved physicochemical, and binding properties compared to the parent drug. After a 100 ns MD simulation, the drugs remained tightly bound to the binding site of the receptor protein, showing significant interactions with shorter distances among the new analogs. The ADMET predictions confirmed better pharmacokinetic properties of novel analogs compared to KTP. The study compares several KTP metabolites using thermo-chemical, spectral, biological, and toxicological analysis. Results showed the reactivity and stability, with spectral data evaluating all KTP analogs. ADMET predicts that all KTP derivatives are less toxic and have better pharmacokinetic features than KTP. Most KTP analogs exhibit excellent oral bioavailability and significant analgesic, antipyretic, and anti-inflammatory functions. These findings support the potential use of these drugs in the future drugs. © 2023 The Author(s)

关键词： ADMET and PASS prediction Cyclooxygenase Ketoprofen Molecular docking and dynamics simulation Nonbonded interactions

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Indirect influence in social networks as an induced percolation phenomenon (vol 119, e2100151119, 2022)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF scienceS OF THE UNITED STATES OF AMERICA 2022年第24期119卷 e2100151119-e2100151119页

作者： Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing School of Computer Science and Engineering Sun Yat-sen University 510006 Guangzhou China Institute of Future Networks Southern University of Science and Technology 518055 Shenzhen China Peng Cheng Laboratory 518066 Shenzhen China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University 510006 Guangzhou China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University 510006 Guangzhou China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Institute for Biocomputation and Physics of Complex Systems University of Zaragoza 50018 Zaragoza Spain Department of Theoretical Physics University of Zaragoza 50018 Zaragoza Spain ISI Foundation 10126 Torino Italy Department of Statistics and Data Science College of Science Southern University of Science and Technology 518055 Shenzhen China

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

关键词： percolation indirect interactions social network phase transition behavioral contagion

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Electrochemical studies of nickel oxide nanoparticles via solution combustion method using green and chemical fuels

Sustainable Chemistry for the Environment

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Sustainable Chemistry for the Environment 2024年 5卷

作者： Ahamed, N. Nasir Pattar, Jayadev Murthy, R.N. Keshava Kumar, M.R. Anil Bhoomika, V. Raghavendra, N. Ravikumar, C.R. Department of Physics Government College for women Chintamani 563125 India Department of Science School of applied sciences REVA University Bangalore India Department of Chemistry Government College for women Chintamani 563125 India Department of Chemical and Materials Engineering Gina Cody School of Engineering and Computer Science Concordia University Montreal H3G 2W1 Canada Department of Civil Engineering East West Institute of Technology VTU Bangalore 560091 India Research Centre Department of chemistry East West Institute of Technology Bangalore India

The efficient manufacture of nickel oxide nanoparticles (NiO NPs) using a novel solution combustion method that makes use of chemical (glucose) and green (Aloe vera gel extract) fuels is reported in this work. As revealed by electron microscopy, the synthesized samples were spongy, spherical, agglomerated, and porous;in contrast, the face-centered cubic crystal structure (FCC) phase for NiO NPs was corroborated by PXRD. The energy band gaps of NiO NPs are 4.09 and 4.21 eV. Using an eco-friendly NiO electrode, cyclic voltammetry investigations demonstrated good reversibility (a reduced value of E_O-E_R). Its great conductivity was confirmed by electrochemical impedance tests, which also showed a low charge-transfer resistance. Using NiO NPs, stable electrode materials for application in supercapacitors can be readily created. In comparison to chemically synthesized NiO green generated NiO in a 3-electrode setup demonstrated a capacitance of 156 Fg⁻¹ and 183 Fg⁻¹ at a scan rate of 5 Ag⁻¹, respectively, according to the GCD investigation. It also had good cycling stability, retaining more than 85% of its original capacitance after 2000 cycles. The results suggest that green produced NiO could be a cheap and practical material for use in supercapacitor applications in the future. © 2024 The Authors

关键词： Aloe vera extract Cyclic voltammetry Electrochemical impedance spectroscopy Photocatalyst Supercapacitor

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

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

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%.

关键词：

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A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage

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Procedia computer science 2022年 214卷 621-627页

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 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

Today, biosensors are effectively utilized in illness detection, prevention, rehabilitation, patient medical monitoring, and personal health promotion. The advancement of biosensing technology offers rigorous methods for measuring an individual's motor function, which contributes significantly to restoring motor function. An smart knee bandage was recently designed to analyze and decode diverse biosignals recorded by knee bandage-integrated biosensors. This research aims to examine human activity recognition (HAR) using biosignals gathered by a smart knee bandage. To accomplish this, a deep residual neural network was established for biosensor-based HAR. For performance assessment, we utilized a significant public biosensor dataset known as the CSL-SHARE dataset, which gathers electromyography (EMG), electrogoniometer (EGM), and inertial measurement unit (IMU) signals of individuals completing 22 simplex and complicated everyday tasks. Five-fold cross-validation was used to train and assess deep learning models. According to our findings, integrating features derived from the EMG, EGM, and IMU signal produced the maximum accuracy of 91.60% and the highest F1-score of 92.13%.

关键词： biosensors human activity recognition electromyography smart knee bandage

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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Nernst-Ettingshausen effect in thin Pt and W films at low temperatures

arXiv

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arXiv 2023年

作者： Luo, Renjie Legvold, Tanner J. Chen, Liyang Natelson, Douglas Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

As spin caloritronic measurements become increasingly common techniques for characterizing material properties, it is important to quantify potentially confounding effects. We report measurements of the Nernst-Ettingshausen response from room temperature to 5 K in thin film wires of Pt and W, metals commonly used as inverse spin Hall detectors in spin Seebeck characterization. Johnson-Nyquist noise thermometry is used to assess the temperature change of the metals with heater power at low temperatures, and the thermal path is analyzed via finite-element modeling. The Nernst-Ettingshausen response of W is found to be approximately temperature-independent, while the response of Pt increases at low temperatures. These results are discussed in the context of theoretical expectations and the possible role of magnetic impurities in Pt. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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