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arXiv 2024年

作者： You, Changsheng Cai, Yunlong Liu, Yuanwei Di Renzo, Marco Duman, Tolga M. Yener, Aylin Swindlehurst, A. Lee Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette91192 France Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Departments of Electrical and Computer Engineering Computer Science and Engineering and Integrated Systems Engineering The Ohio State University OH United States Center for Pervasive Communications and Computing University of California IrvineCA92697 United States

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Near field communication

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Continuous Symmetry Analysis of the Effects of City Infrastructures on Invariant Metrics for House Market Volatilities

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computer Modeling in engineering & sciences 2022年第12期133卷 619-638页

作者： Chien-Wen Lin Jen-Cheng Wang Bo-Yan Zhong Joe-Air Jiang Ya-Fen Wu Shao-Wei Leu Tzer-En Nee Department of Electrical Engineering Taiwan Ocean UniversityKeelung20224Taiwan Department of Computer Science National Taipei University of EducationTaipei10671Taiwan Department of Electronic Engineering Chang Gung UniversityTao-Yuan City33302Taiwan Department of Biomechatronics Engineering National Taiwan UniversityTaipei10617Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung40447Taiwan Department of Electronic Engineering Ming Chi University of TechnologyNew Taipei City24301Taiwan Artificial Intelligence Research Center Chang Gung UniversityTao-Yuan City33302Taiwan

The invariant metrics of the effects of park size and distance to public transportation on housing value volatilities in Boston,Milwaukee,Taipei and Tokyo are *** reveal a Cobb-Douglas-like *** scaleinvariant exponents corresponding to the percentage of a green area(a)are 7.4,8.41,14.1 and 15.5 for Boston,Milwaukee,Taipei and Tokyo,respectively,while the corresponding direct distances to the nearest metro station(d)are−5,−5.88,−10 and−10,for Boston,Milwaukee,Taipei and Tokyo,*** multiphysics-based analysis provides a powerful approach for the symmetry characterization of market *** scaling exponent ratio between park area percentages and distances to metro stations is approximately 3/*** scaling exponent ratio expressed in the perceptual stimuli will remain invariant under group *** to Stevens’power law,the perception-dependent feature spaces for parks and public transportation can be described as two-and three-dimensional conceptual *** on the prolongation structure of the Schroinger equation,the SL(2,R)models are used to analyze the house-price *** with Shepard’s law,the rotational group leads to a Gaussian pattern,exhibiting an extension of the special linear group structure by embedding SO(3)■R(3)in SL(2,R).The influencing factors related to cognitive functioning exhibit substantially different scaleinvariant characteristics corresponding to the complexity of the socio-economic ***,the contour shapes of the price volatilities obtained from the group-theoretical analysis not only corroborate the impact of the housing pricing estimation in these cities but also reveal the invariant features of their housing markets are faced with the forthcoming sustainable development of big data technologies and computational urban science research.

关键词： Invariant metrics Schroinger equation group theory

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Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising

arXiv

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arXiv 2023年

作者： Pan, Haodong Gao, Feng Dong, Junyu Du, Qian The School of Computer Science and Technology Ocean University of China Qingdao266100 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States The Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Software Engineering Chongqing University Chongqing China Computer Science and Technology Beihang University Beijing China Electrical Engineering The University of Maryland at Baltimore BaltimoreMD United States

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at https://***/summitgao/MAFNet. © 2023, CC BY.

关键词： Image denoising

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Quantifying Strain Dependence of Multi-Frequency Shearwave Elasticity Imaging

Quantifying Strain Dependence of Multi-Frequency Shearwave E...

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Western New York Image Processing Workshop (WNYIPW)

作者： Siladitya Khan Soumya Goswami Fan Feng Marvin M. Doyley Stephen A. McAleavey Departments of Biomedical & Computer Engineering University of Rochester Rochester NY USA Rochester Center for Biomedical Ultrasound University of Rochester Rochester NY USA Goergen Institute for Data Science University of Rochester Rochester NY USA Departments of Electrical & Computer Engineering University of Rochester Rochester NY USA

Nonlinear shear modulus (NLSM) can potentially differentiate benign and malignant pathologies. Previous studies demonstrated the discriminatory value of mapping quantitative estimates of material non-linearity, yet estimates remain biased due to unaccounted shear-wave dispersion within the tissue. We demonstrate a finite element simulation of an inversion technique that combines traditional quasi-static and dynamic elasticity imaging to generate a multi-frequency estimate of the third-order shear modulus A(x,z,ω) over a broad bandwidth (200-600 Hz). We show that our reconstruction results are superior to those obtained by traditional group speed by a factor of two in terms of contrast enhancement measures. Experimental ultrasound elastography on a custom oil-in-gelatin emulsion confirms the presence of dispersion in the NLSM parameter. Constitutive hyper-viscoelastic model responses of spectral NLSM are simulated based on matched linear shear modulus data of experimental phantoms. Hyper-elastic Ogden with a Voigt loss is shown to describe NLSM behavior at frequencies greater than 400 Hz. Reconstruction accuracy in the presence of simulated tracking and correlation noise demonstrates the robustness of our estimates.

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Dynamics of an entangled pair of spin-12 particles under correlated random magnetic fields

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Physical Review E 2023年第1期107卷 014135-014135页

作者： Venkata Satya Surya Phaneendra Pydimarri Timothy R. Field School of Computational Science and Engineering McMaster University Hamilton Ontario Canada L8S 4L8 Departments of Electrical & Computer Engineering and Mathematics & Statistics McMaster University Hamilton Ontario Canada L8S 4L8

The dynamics of a maximally entangled pair of spin-12 particles is obtained in the presence of random magnetic fields which are correlated. The two spin-12 particles are assumed to be maximally entangled initially and are then disturbed by the magnetic fields modeled as Gaussian vector random processes whose corresponding spatial components are correlated. The dynamics is derived in terms of the joint density matrix of the entangled pair using the ideas of stochastic calculus, from which the steady-state density matrix and the associated timescale for it to be reached are obtained. The asymptotic density matrix represents a state of (partial) disentanglement.

关键词： Entanglement measures Fluctuations & noise Quantum entanglement Quantum stochastic processes Spin dynamics Quantum spin models Brownian dynamics Fokker–Planck equation Stochastic analysis methods Stochastic differential equations Stochastic processes & statistics

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Dynamic Battery Type Detection Using Neural Networks

Dynamic Battery Type Detection Using Neural Networks

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Hector K. Lopez Ali Zilouchian Amir Abtahi Department of Electrical Engineering & Computer Science Florida Atlantic University Boca Raton FL United States Department of Ocean and Mechanical Engineering Florida Atlantic University Boca Raton FL United States

Dynamically detecting battery chemistries, including LiFePO4, Ni-MH, and Lead Acid, is explored through extensive simulations. Utilizing discharge curves as training data, three neural network architectures-Single Hidden Layer, Double Hidden Layer, and Radial Basis Transfer Function-are employed for pattern recognition across diverse discharge profiles. The objective is to enable the identification of connected battery types and optimize charging control. This research holds significance in real-time Electric Vehicle (EV) charging optimization, offering the capability to discern various battery chemistries. Additionally, in Peer-to-Peer (P2P) energy markets, the dynamic contribution of batteries to the grid requires safe and efficient charging for interoperable systems. The findings presented in this study introduce adaptable systems, fostering innovation in sustainable energy practices.

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Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

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computers, Materials & Continua 2022年第10期73卷 1449-1462页

作者： Wei Song Haolin Wang Xinping Zhang Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Beijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology CenterGuangzhou510300China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and *** application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of *** existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly *** order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral *** model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality *** test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other *** evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net *** is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.

关键词： Deep-sea mineral image segmentation generative adversarial network

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Exoplanet Detection via Differentiable Rendering

arXiv

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arXiv 2025年

作者： Feng, Brandon Y. Ferrer-Chávez, Rodrigo Levis, Aviad Wang, Jason J. Bouman, Katherine L. Freeman, William T. The Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States The Department of Physics & Astronomy Northwestern University United States The Department of Computer Science The University of Toronto Canada The Departments of Computing and Mathematical Sciences Electrical Engineering and Astronomy California Institute of Technology United States

Direct imaging of exoplanets is crucial for advancing our understanding of planetary systems beyond our solar system, but it faces significant challenges due to the high contrast between host stars and their planets. Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffracted starlight that can mimic the appearance of planets, complicating the detection of faint exoplanet signals. Traditional post-processing methods, operating primarily in the image intensity domain, do not integrate wavefront sensing data. These data, measured mainly for adaptive optics corrections, have been overlooked as a potential resource for post-processing, partly due to the challenge of the evolving nature of wavefront aberrations. In this paper, we present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection. Our differentiable renderer models wave-based light propagation through a coronagraphic telescope system, allowing gradient-based optimization to significantly improve starlight subtraction and increase sensitivity to faint exoplanets. Simulation experiments based on the James Webb Space Telescope configuration demonstrate the effectiveness of our approach, achieving substantial improvements in contrast and planet detection limits. Our results showcase how the computational advancements enabled by differentiable rendering can revitalize previously underexploited wavefront data, opening new avenues for enhancing exoplanet imaging and characterization. © 2025, CC BY.

关键词： Adaptive optics

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Selective Detection in Impulsive Low-Frequency Raman Imaging Using Shaped Probe Pulses

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Physical Review Applied 2023年第5期19卷 054075-054075页

作者： Siddarth Shivkumar Dekel Ranann Samuel Metais Sisira Suresh Nicolas Forget Randy Bartels Dan Oron Hervé Rigneault Aix Marseille Université CNRS Centrale Marseille Institut Fresnel Marseille France Department of Physics of Complex Systems The Weizmann Institute of Science Rehovot 76100 Israel Fastlite Antibes 06600 France Departments of Electrical and Computer Engineering and Biomedical Engineering Colorado State University Fort Collins USA

Impulsive stimulated Raman scattering (ISRS) using a single short femtosecond pump pulse to excite molecular vibrations offers an elegant pump-probe approach to perform vibrational imaging below 200cm−1. One shortcoming of ISRS is its inability to offer vibrational selectivity as all the vibrational bonds whose frequencies lie within the short pump-pulse bandwidth are excited. To date, several coherent control techniques have been explored to address this issue and selectively excite a specific molecular vibration by shaping the pump pulse. There has not been any systematic work that reports an analogous shaping of the probe pulse to implement preferential detection. In this work, we focus on vibrational imaging and report vibrational selective detection by shaping the probe pulse in time. We demonstrate numerically and experimentally two pulse-shaping strategies with one functioning as a vibrational notch filter and the other functioning as a vibrational low-pass filter. This enables fast (25μs/pixel) and selective hyperspectral imaging in the low-frequency regime (<200cm−1).

关键词： Analytical chemistry Molecular spectra Optical transient phenomena Biomolecules Time-resolved light scattering spectroscopy Ultrafast femtosecond pump probe

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