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IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

作者： Linh T. Hoang Chuyen T. Nguyen Hoang D. Le Anh T. Pham Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Vietnam

Deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (ABSs) has emerged as a promising solution to enable high-quality services in next-generation mobile networks. One key challenge is how to place the UAV-mounted Base Stations (UAV-BSs) dynamically over time in response to changes in network situations. Conventional methods usually utilize location information to place the UAV-BSs at the centroid of all users to maximize the average downlink rate. This approach might become ineffective when the traffic demand is highly heterogeneous among users, causing the spatial distribution of traffic demand not to correlate with the distribution of users. This paper considers a time-evolving multi-user multi-server system with heterogeneous traffic, where a group of UAV-BSs provides different services to ground mobile users. We formulate a long-term maximization problem of the user’s time-average download speed with the queue stability constraint under random movements of ground users. An actor-critic framework of deep reinforcement learning is proposed to position multiple UAV-BSs efficiently based on a comprehensive view of the network situation, including spatial statistics of the users, traffic demand, and the network’s downlink rate. The framework’s core module is a deep neural network (DNN) that obtains the UAV’s location and three encoded heatmaps of network statistics to predict the optimal movement for UAV-BSs. Extensive simulations demonstrate the guaranteed convergence and great potential of the proposed method regarding the backlog queue length and the average download speed of users.

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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Theoretical Analysis on Localization Error Bound for Wireless System Using Leaky Coaxial Cable

Theoretical Analysis on Localization Error Bound for Wireles...

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International Conference on Advanced computer Theory and engineering, ICACTE

作者： Ji Ziying Junjie Zhu Yafei Hou Satoshi Denno Graduate School of Environmental Life Natural Science and Technology Okayama University Japan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Leaky CoaXial (LCX) cable is a line-shaped cable used as antenna for wireless system. Due to its stable and uniform radiation characteristics with changeable shape, LCX cable is largely used for wireless coverage over many linear-cell environments such as shopping mall, tunnel, underground mall or walkway etc. These scenarios usually require location-aware services for many applications. We have investigated many localization methods for LCX system. However, its theoretical localization error bound is still unknown, In this paper, using the LCX channel model which is developed based on the Geometrically-Based Single-Bounce (GBSB) model, we give a brief Fisher Information analysis for LCX-based localization and the impact of different LCX configurations on the position error bound (PEB) is evaluated through the Cramér-Rao lower bound.

关键词： Location awareness Wireless communication Lower bound Analytical models Coaxial cables Shape Channel models Indoor positioning systems Antennas Information analysis

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Facial Age Synthesis by StyleGAN2 with Attention

Facial Age Synthesis by StyleGAN2 with Attention

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Tung-Ke Hsieh Tsung-Jung Liu Kuan-Hsien Liu Dept. of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Dept. of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

The task of age synthesis involves capturing the changing appearance of a person over time. Efficiently representing such complex transformations on facial images is very challenging. We utilize the attention mechanism for enhanced feature extraction combined with expressive semantic latent space of StyleGAN2, which facilitates additional refinement and editing of the generated images, In addition, we incorporate an identity discriminator so that the identity of the generated images can be preserved. Finally, through qualitative and quantitative evaluations, we demonstrate that our method achieves better age synthesis accuracy compared to state-of-the-art methods.

关键词： Accuracy Attention mechanisms Semantics Feature extraction Vectors Consumer electronics

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A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

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Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-14页

作者： Dong, Shaoyang Jiang, Yusheng Yu, Xiong Graduate Research Assistant Department of Civil and Environmental Engineering Case Western Reserve University ClevelandOH United States Opal J. and Richard A. Vanderhoof Professor and Chair Department of Civil and Environmental Engineering Professor of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH United States

This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions. © The Author(s) 2022.

关键词： Pavements

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Multi-step ahead wind power forecasting based on N-BEATS model

Multi-step ahead wind power forecasting based on N-BEATS mod...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Luis Fernando Rodrigues Agottani Matheus Melara Girardi Wendel Rafael de Souza Chaves Lucas de Azevedo Takara Leandro dos Santos Coelho Viviana Cocco Mariani Graduate Program in Mechanical Eng. Federal University of Parana Curitiba Brazil Department of Computer Science Pontifical Catholic University of Parana Curitiba Brazil Graduate Program in Electrical Eng. Federal University of Parana Curitiba Brazil

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

For wind power systems to operate successfully and efficiently, accurate forecasting is critical. The integration of large wind farms with smart grid technology mainly depend on accurate models for wind power output prediction. This study introduces a neural basis expansion approach named N-BEATS (neural basis expansion analysis for interpretable time series forecasting) for time series multi-step wind power forecasting. N-BEATS is a deep neural network architecture composed of multiple fully connected layers, arranged with both forward and backward residual connections. The performance of N-BEATS was benchmarked using classical approaches including AutoRegressive Integrated Moving Average (ARIMA), Seasonal AutoRegressive Integrated Moving Average (SARIMA), Holt-Winters, and the recurrent neural network named Long Short-Term Memory (LSTM). All models hyperparameters were tuned by Bayesian optimization. The N-BEATS model demonstrated superior forecasting scores and outperformed traditional methods in metrics such as mean absolute error and root mean square error for different forecasting horizons.

关键词： Accuracy Time series analysis Europe Wind power generation Predictive models Wind farms Smart grids Forecasting Wind forecasting Long short term memory

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Dynamics of self-hybridized exciton–polaritons in 2D halide perovskites

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Light(science & Applications) 2024年第1期13卷 13-22页

作者： Surendra B.Anantharaman Jason Lynch Christopher E.Stevens Christopher Munley Chentao Li Jin Hou Hao Zhang Andrew Torma Thomas Darlington Francis Coen Kevin Li Arka Majumdar PJames Schuck Aditya Mohite Hayk Harutyunyan Joshua R.Hendrickson Deep Jariwala Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA19104USA KBR Inc. BeavercreekOH45431USA Air Force Research Laboratory Sensors DirectorateWright-Patterson Air Force BaseOH45433USA Department of Physics University of WashingtonSeattleWA98195USA Department of Physics Emory UniversityAtlantaGA30322USA Department of Chemical and Biomolecular Engineering Rice UniversityHoustonTX77005USA Department of Materials Science and Nanoengineering Rice UniversityHoustonTX77005USA Applied Physics Program Smalley‐Curl InstituteRice UniversityHoustonTX77005USA Department of Mechanical Engineering Columbia UniversityNew YorkNY10027USA Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Excitons,bound electron–hole pairs,in two-dimensional hybrid organic inorganic perovskites(2D HOIPs)are capable of forming hybrid light-matter states known as exciton-polaritons(E–Ps)when the excitonic medium is confined in an optical *** the case of 2D HOIPs,they can self-hybridize into E–Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the ***,the fundamental properties of these self-hybridized E–Ps in 2D HOIPs,including their role in ultrafast energy and/or charge transfer at interfaces,remain ***,we demonstrate that>0.5µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E–P *** E–Ps have high Q factors(>100)and modulate the optical dispersion for the crystal to enhance sub-gap absorption and *** varying excitation energy and ultrafast measurements,we also confirm energy transfer from higher energy E–Ps to lower energy E–***,we also demonstrate that E–Ps are capable of charge transport and transfer at *** findings provide new insights into charge and energy transfer in E–Ps opening new opportunities towards their manipulation for polaritonic devices.

关键词： charge polar excitation

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1T-TaS2 based dynamically tunable plasmonic metasurface

1T-TaS2 based dynamically tunable plasmonic metasurface

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CLEO: QELS_Fundamental science, QELS 2022

作者： Li, Weijian Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Electrical & Computer Engineering Rice University HoustonTX77005 United States

We demonstrate a dynamically tunable plasmonic metasurface enabled by light-tunable optical constants of a quantum material - 1T-TaS2. We observe a relative reflectance change of 10% under low-intensity incoherent ill... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781557528209

关键词： Reflectivity MIM devices Lighting Lasers and electrooptics Metasurfaces Plasmons Optical materials

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