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computer science and Blockchain (CCSB), International Conference on

作者： Hanlin Mo Zhikai Yang Pengcheng Li Qibiao Wang School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong China School of Physics and Electronic Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

The attention mechanism can extract task-relevant vital information while suppressing less important information, playing an increasingly critical role in deep feature representation for semantic segmentation. This research proposes a neural network model based on the widely used U-Net architecture, incorporating the ECA (Efficient Channel Attention) attention mechanism. To address the issue of edge segmentation blur, an improved module based on ASPP (Atrous Spatial Pyramid Pooling) is introduced into both the compression and expansion paths of U-Net. Additionally, to tackle the issue of neglected global context information at different scales in portrait segmentation networks, a multi-scale prediction fusion method is employed to utilize global information at various scales, and the ECA attention module is employed to aggregate attention information from each layer in the compression path. Extensive experiments demonstrate that the proposed method achieves performance comparable to or better than networks such as Deeplabv3+, FCN, and UNet++.

关键词： Attention mechanisms Accuracy Image coding Computational modeling Semantic segmentation Neural networks Memory management Feature extraction Mobile handsets Data mining

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Complementary memtransistors for neuromorphic computing: How, what and why

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Journal of Semiconductors 2024年第6期45卷 64-80页

作者： Qi Chen Yue Zhou Weiwei Xiong Zirui Chen Yasai Wang Xiangshui Miao Yuhui He School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering University of CaliforniaSan DiegoUSA Dipartimento di Elettronica e Informazione and *** Politecnico di MilanoMilanoItaly Department of Applied Physics Hong Kong Polytechnic UniversityHong KongChina

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit ***,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges *** the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,*** exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

关键词： complementary memtransistor neuromorphic computing reward-modulated spike timing-dependent plasticity remote supervise method in-sensor computing

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Machine Learning-based Feature Extraction Method for Sleep Stage Classification

Machine Learning-based Feature Extraction Method for Sleep S...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Tagimae, Henry Kumar, Shiu Groza, Voicu Rakaria, Joeli Assaf, Mansour H. Kumar, Rahul R Petriu, Emil M. The University of the South Pacific School of Information Technology Engineering Mathematics and Physics Suva Fiji Fiji National University Department of Electrical & Electronic Engineering Suva Fiji University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9781665493840

Sleep stage classification is important in diagnosing and treating sleep disorders, but current methods have limitations. An automatic feature extraction method for sleep stage classification using a convolutional neural network (CNN) is proposed. The method extracts channels from raw data, resizes them into sequential functions, and feeds them into a CNN architecture designed to extract relevant features. The proposed method is evaluated on the ISRUC raw sleep dataset, achieving an accuracy of 72.6% on 60 subjects with 11 channels. Comparison with state-of-the-art methods showed that the proposed strategy using PhysioNet Database achieved a higher accuracy of 74%. Learned feature extraction methods are more effective than other preset feature extraction methods. However, challenges still exist, such as using multichannel data and big data. Further research is needed to address these challenges and improve performance. The proposed strategy shows promise in improving the accuracy of sleep stage classification, which can aid in diagnosing and treating sleep disorders. © 2023 IEEE.

关键词： Feature extraction

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A Novel Double-Sided Printed Omnidirectional Antenna with Broad Bandwidth

A Novel Double-Sided Printed Omnidirectional Antenna with Br...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Gao, Feifan Li, Jinxin Xiao, Pei Liu, Zhu Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China Hunan Normal University School of Physics and Electronics Changsha410081 China

ISBN: (纸本)9798350355581

This paper proposes a novel printed omnidirectional antenna. It consists of two FR4 dielectric substrates, each of which is printed on one side with different radiation units. Each radiating unit comprises two symmetrical monopole antenna arms, with the upper and lower radiating units forming a set of symmetrical dipole antennas. Additionally, the shape of each monopole is altered to enhance the radiation performance. The connection between the feedline and the arms is made by a stepped impedance converter to improve the broadband characteristics of the antenna. A gap is left between the top and bottom plates to introduce an air layer to improve the antenna radiation performance. The -10 dB bandwidth of the antenna simulation is 15.6% (from 2.66 GHz to 3.11 GHz, centered at 2.885 GHz), demonstrating excellent omni-directional radiation characteristics within this frequency range. © 2024 IEEE.

关键词： Dipole antennas

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Deep Learning Based Lightweight Neural Network for Massive MIMO CSI Feedback 10

Deep Learning Based Lightweight Neural Network for Massive M...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Wang, Yujia Li, Jinxin Liu, Zhu Li, Gaosheng College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Physics and Electronics Hunan Normal University Changsha410082 China College of Electrical and Information Engineering Hunan University Changsha410082 China

ISBN: (纸本)9798331533922

In Massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) is usually estimated and fed back to the BS from the user equipment (UE), and the quality of CSI received by the BS has a large impact on the performance of the large-scale MIMO system. To address the problem that existing DL-based CSI feedback methods have difficulty in balancing model complexity and feedback performance, in this work, we propose CFNet, an efficient and lightweight model based on MetaFormer, which employs convolution to replace the attention module in the traditional Transformer model. CFNet combines the lightweight of convolutional neural networks with the high performance of the Vision Transformer (VIT) model. CFNet combines the lightweight of convolutional neural network and the high performance of Vision Transformer (VIT) model. Experimental results show that CFNet can maintain high feedback accuracy with very low computational complexity under different compression ratios. © 2024 IEEE.

关键词： Convolutional neural networks

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engineering synthetic gauge fields through the coupling phases in cavity magnonics

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Physical Review Applied 2024年第6期21卷 064033-064033页

作者： Alan Gardin Guillaume Bourcin Jeremy Bourhill Vincent Vlaminck Christian Person Christophe Fumeaux Giuseppe C. Tettamanzi Vincent Castel School of Physics Lab-STICC UMR CNRS 6285 ARC Centre of Excellence for Engineered Quantum Systems and ARC Centre of Excellence for Dark Matter Particle Physics Department of Physics School of Electrical Engineering and Computer Science School of Chemical Engineering and Advanced Materials

Cavity magnonics, which studies the interaction of light with magnetic systems in a cavity, is a promising platform for quantum transducers and quantum memories. At microwave frequencies, the coupling between a cavity photon and a magnon, the quasiparticle of a spin-wave excitation, is a consequence of the Zeeman interaction between the cavity’s magnetic field and the magnet’s macroscopic spin. For each photon-magnon interaction, a coupling phase factor exists, and this is often neglected in simple systems; however, in “loop-coupled” systems, where there are at least as many couplings as modes, the coupling phases become relevant for the physics and lead to synthetic gauge fields. We present experimental evidence of the existence of such coupling phases by considering two spheres made of yttrium-iron-garnet and two different re-entrant cavities. We predict numerically the values of the coupling phases, and we find good agreement between the theory and the experimental data. These results show that in cavity magnonics, one can engineer synthetic gauge fields, which can be useful for cavity-mediated coupling and engineering dark-mode physics.

关键词： Magnetic coupling Magnons Optical & microwave phenomena Ferrimagnets Ferromagnetic resonance Gauge symmetries Holstein-Primakoff method Microwave techniques

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The rice canopy density prediction model research based on SVM 3

The rice canopy density prediction model research based on S...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Chen, Keyin Xie, Jinzhen School of Physics and Electronic Engineering Jiaying University Meizhou514015 China School of Computer Science Wuhan University Wuhan430072 China School Work Department Hezhou University Hezhou542899 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

This paper mainly researched the basic principle of support vector machine based on Gaussian kernel function. Aiming at the defects of existing Gaussian kernel function of support vector machine, particle swarm optimization algorithm was used to optimize the penalty factor and Gaussian radial parameters to improve the generalization prediction ability of the support vector machine. Moreover, based on the support vector machine and particle swarm optimization, the rice canopy density prediction model and correlation analysis experiment were established using the rice canopy image data. The experiment shows that the absolute error and relative error of the rice canopy density prediction model based on support vector machine and particle swarm optimization can meet the requirements of real-time control of the feed amount of the combined harvest under normal and back light conditions. © 2023 SPIE.

关键词： Support vector machines

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physics-informed tracking of qubit fluctuations

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Physical Review Applied 2024年第1期22卷 014033-014033页

作者： Fabrizio Berritta Jan A. Krzywda Jacob Benestad Joost van der Heijden Federico Fedele Saeed Fallahi Geoffrey C. Gardner Michael J. Manfra Evert van Nieuwenburg Jeroen Danon Anasua Chatterjee Ferdinand Kuemmeth Center for Quantum Devices Lorentz Institute and Leiden Institute of Advanced Computer Science Center for Quantum Spintronics Department of Physics QDevil Quantum Machines 2750 Ballerup Denmark Department of Engineering Science Department of Physics and Astronomy Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering School of Materials Engineering

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources.

关键词： Quantum control Quantum fluctuations & noise Quantum information with solid state qubits Double quantum dots Bayesian methods Fokker–Planck equation Machine learning Radio frequency techniques

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Emergence of composite many-body exciton states in WS2 and MoSe2 monolayers

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Physical Review B 2024年第4期109卷 L041304-L041304页

作者： J. Choi J. Li D. Van Tuan H. Dery S. A. Crooker National High Magnetic Field Laboratory Los Alamos New Mexico 87545 USA Advanced Instrumentation Institute Korea Research Institute of Standards and Science Daejeon 34113 Korea Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology Hubei 430074 China Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA

When doped with a high density of mobile charge carriers, monolayer transition-metal dichalcogenide (TMD) semiconductors can host new types of composite many-particle exciton states that do not exist in conventional semiconductors. Such multiparticle bound states arise when a photoexcited electron-hole pair couples not to just a single Fermi sea that is quantum-mechanically distinguishable (as in the case of conventional charged excitons or trions), but rather couples simultaneously to multiple Fermi seas, each having distinct spin and valley quantum numbers. Composite six-particle “hexciton” states were recently identified in electron-doped WSe2 monolayers, but under suitable conditions they should also form in all other members of the monolayer TMD family. Here we present spectroscopic evidence demonstrating the emergence of many-body hexcitons in charge-tunable WS2 monolayers (at the A-exciton) and MoSe2 monolayers (at the B-exciton). The roles of distinguishability and carrier screening on the stability of hexcitons are discussed.

关键词： Excitons Trions Transition metal dichalcogenides Two-dimensional electron system

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High-repetition-rate strong-field terahertz source by optical rectification in DSTMS crystals

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High Power Laser science and engineering 2024年第5期12卷 70-75页

作者： Zhuorui Zheng Kang Wang Hongyang Li Xianze Meng Ye Tian Liwei Song State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghaiChina School of Physics Science and Engineering Tongji UniversityShanghaiChina

We present the generation of high-repetition-rate strong-field terahertz(THz)pulses from a thin 4-N,N-dimethylamino-4’-N’-methyl-stilbazolium 2,4,6-trimethylbenzenesulfonate(DSTMS)organic crystal pumped by an ytterbium-doped yttrium aluminum garnet *** generated THz pulse energy reaches 932.8 nJ at 1 kHz repetition rate,with a conversion efficiency of 0.19%and a peak electric field of 819 kV/*** a repetition rate of 10 kHz,it is able to maintain a peak electric field of 236 kV/cm and an average THz power of 0.77 *** high-repetition-rate,strong-field THz source provides a convenient tool for the study of THz matter manipulation and THz spectroscopy.

关键词： high repetition rate optical rectification strong-field terahertz source

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