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IEEE International Conference on Communications (ICC)

作者： Anny Xijia Zheng Vincent W.S. Chan Department of Electrical Engineering and Computer Science Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We investigate the design of an efficient rerouting scheme for dynamic bursty high granularity traffic as a part of an agile network management and control scheme in cognitive optical networks. An efficient rerouting scheme allows more efficient use of wavelengths resources. The design includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers. Rerouting is necessary to maintain users' quality of service when the network load exceeds the load limit of the primary path. Our design is to trigger reconfiguration when a load threshold is crossed, and the threshold is determined by requirements on both the delay and the blocking (i.e. no available lightpath) probability. The delay is dependent on the probability of exhausting all wavelengths on the primary route, the availability of secondary routes, the holder (buffer) size, and the topology of the network. A dynamic rerouting scheme implemented with a shortest-path algorithm with immediate wavelength availability is proposed to find the alternate paths. It is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths. In addition, a good candidate network topology should have high edge-connectivity. Wavelength reservation for rerouted traffic does not maximize wavelength utilization. Traffic with different sizes should be broken up into multiclasses with dedicated partitioned resources and will have different maximum load thresholds for triggering rerouting to meet the performance requirements.

关键词： Integrated optics Network topology Heuristic algorithms Wavelength assignment Lighting Quality of service Optical fiber networks

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HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

arXiv

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotics

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Shortest Trajectory of a Dubins Vehicle with a Controllable Laser

arXiv

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arXiv 2024年

作者： Bajaj, Shivam Jha, Bhargav Bopardikar, Shaunak D. Von Moll, Alexander Casbeer, David W. The Electrical and Computer Engineering Department Purdue University West LafayetteIN47906 United States The Electrical and Computer Engineering Department Michigan State University East LansingMI48823 United States Control Science Center Air Force Research Laboratory Ohio United States

We formulate a novel planar motion planning problem for a Dubins-Laser system that consists of a Dubins vehicle with an attached controllable laser. The vehicle moves with unit speed and the laser, having a finite range, can rotate in a clockwise or anti-clockwise direction with a bounded angular rate. From an arbitrary initial position and orientation, the objective is to steer the system so that a given static target is within the range of the laser and the laser is oriented at it in minimum time. We characterize multiple properties of the optimal trajectory and establish that the optimal trajectory for the Dubins-laser system is one out of a total of 16 candidates. Finally, we provide numerical insights that illustrate the properties characterized in this work. © 2024, CC BY-NC-SA.

关键词： Vehicles

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Estimating Floating Ice Coverage in Tidewater Glacier Bays Automatically from Aerial Imagery

Estimating Floating Ice Coverage in Tidewater Glacier Bays A...

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OCEANS

作者： Hari Vishnu Lin Tianyue Mandar Chitre Bharath Kalyan Emily J. Venables Acoustic Research Laboratory Tropical Marine Science Institute National University of Singapore Department of Electrical and Computer Engineering National University of Singapore UiT The Arctic University of Norway

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

This study focuses on the estimation of floating ice distribution on the sea surface from aerial imagery at tidewater glaciers. The presence of ice-mélange in the glacial bay affects ice-loss at the glacier terminus via buttressing of the glacier face, insulation from ocean heat and breaking of oceanic currents. Thus, estimation of its formation, evolution, distribution and dissipation will aid an understanding of climate-change mechanisms at glacial bays. Given the challenges stemming from lack of data and labeled training samples, we employ a pre-trained vision model with zero-shot performance, the Segment Anything Model (SAM). SAM is fine-tuned to perform automatic estimation of floating-ice distribution on a dataset of images collected in Svalbard at various locations and through various modalities, by segmenting the images into floating ice and ocean surface not covered by ice. The Low-rank adaptation technique is employed on the image encoder structure to reduce the training data and hardware resources needed for model fine-tuning. This approach opens new avenues for real-time analysis of glacial dynamics and their implications on global climate patterns.

关键词： Training Adaptation models Sea surface Image segmentation Estimation Training data Glaciers Data models Real-time systems Meteorology

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Investigating Thermal Effects and Mitigation Strategies in Multi-Finger GaN HEMTs

Investigating Thermal Effects and Mitigation Strategies in M...

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Microwave and Antenna Symposium (IMAS), International

作者： Amirreza G. Avval Soheil Nouri Dan Denninghoff Ali Darwish Samir M. El-Ghazaly Department of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA HRL Laboratories Malibu CA USA Army Research Laboratory Adelphi MD USA

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

Gallium Nitride High Electron Mobility Transistors have garnered attention as promising solutions for high-power and high-frequency applications, attributed to their exceptional features such as high electron mobility, high breakdown voltage, and wide bandgap. However, the efficacy of GaN HEMTs is challenged by some restrictive phenomena, notably self-heating and thermal effects. This paper presents experimental results from DC analysis conducted on different GaN HEMT devices featuring various numbers of fingers, alongside an examination of the thermal impact on device performance. The correlation between finger count in multi-finger HEMTs and device performance is explored, followed by a discussion on mitigation strategies aimed at optimizing device design and structures to address these challenges.

关键词： Performance evaluation Microwave antennas Correlation Prevention and mitigation Fingers HEMTs Thermal conductivity Thermal analysis MODFETs Gallium nitride

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Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Unraveling the transformation pathway of the β to γ phase transition in Ga2O3 from atomistic simulations

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Physical Review Materials 2025年第1期9卷 014601-014601页

作者： Channyung Lee Michael A. Scarpulla Joel B. Varley Elif Ertekin Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Materials Science and Engineering University of Utah Salt Lake City Utah 84112 USA Department of Electrical and Computer Engineering University of Utah Salt Lake City Utah 84112 USA Lawrence Livermore National Laboratory Livermore California 94550 USA Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

Defect spinel γ−Ga2O3 is the least stable polymorph of Ga2O3, so its frequent appearance as a structural defect within or on the surface of monoclinic β−Ga2O3 remains a mystery. Through first-principles calculations, we explore potential pathways for the phase transition from β−Ga2O3 to γ−Ga2O3, and examine two key driving forces: tensile strain and Ga deficiency. When configurational entropy contributions to phase energies are included, the γ phase becomes energetically competitive with the β phase, with the free energy difference between these phases diminishing even further under Ga-deficient conditions. Notably, a stability crossover occurs at room temperature at high vacancy concentrations ([VGa3−]>3%). A simple model β→γ transformation pathway is identified, comprising two primary reactions, that enables the formation of the γ phase via simultaneous migration of Ga atoms from tetrahedral lattice sites to octahedral interstitial positions. The transformation barriers are prohibitively large in pristine Ga2O3, but can be substantially reduced by: (1) the presence of Ga vacancies, (2) elongational strains along the crystallographic a-axis, and (3) when volumetric relaxations are possible during transformation. These results elucidate prior experimental observations, where γ−Ga2O3 is seen on damaged surfaces or in highly n-type β−Ga2O3 environments, which support Ga deficiency and mechanical strain. The insights into the driving forces and mechanisms of γ−Ga2O3 formation enhance understanding of how localized strain and nonequilibrium defect concentrations may facilitate its formation from the β phase.

关键词： Free energy

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European Systems, Software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Advanced Deep Learning Models for Groundwater Contaminant Dynamics at the Savannah River Site

Advanced Deep Learning Models for Groundwater Contaminant Dy...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Soni, Jayesh Upadhyay, Himanshu Wainwright, Haruko Xu, Zexuan Lagos, Leonel Florida International University Applied Research Center Miami United States Electrical and Computer Engineering Florida International University Miami United States Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge United States Lawrence Berkeley National Laboratory Climate Dept Berkeley United States Florida International University Moss Department of Construction Management Miami United States

ISBN: (纸本)9798350391213

Advances in sensor technology have greatly enhanced the monitoring and assessment of environmental conditions. However, groundwater monitoring presents unique challenges, primarily in accurately detecting anomalies amidst sensor noise, fluctuations, and other inconsistencies in the data. The introduction of low-cost in-situ sensors has dramatically increased data volumes, requiring more advanced analytical techniques. This paper investigates the potential of Artificial Intelligence and Machine Learning (AI/ML), specifically Long Short-Term Memory (LSTM) Autoencoder models and Transformers, to tackle these challenges. LSTM Autoencoders, a specialized form of recurrent neural networks, are well-suited for sequential data analysis and anomaly detection, particularly in time-based sensor readings. The LSTM Autoencoder can compress the data and reconstruct normal patterns by training on historical sensor data representing normal groundwater conditions, thus identifying deviations that signal potential contamination. Meanwhile, transformer models, known for their attention mechanism and ability to capture long-range dependencies, are incorporated to enhance further their ability to distinguish between natural data variations and significant anomalies in complex, dynamic groundwater environments. We apply these LSTM Autoencoder and Transformer approaches to US-DoE Savannah River Site F-Area datasets, demonstrating their robustness and adaptability in identifying subtle and progressive groundwater contamination. This approach improves the accuracy of groundwater monitoring and supports a proactive stance in environmental preservation and public health protection. © 2024 IEEE.

关键词： Groundwater pollution

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Operando FTIR study on water additive in lithium-sulfur batteries to mitigate shuttle effect

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Journal of Energy Chemistry 2024年第11期98卷 702-713页

作者： Érick A.Santos Martim C.Policano Manuel J.Pinzón Isabela Galantini Vanessa A.Goncalves Francisco C.B.Maia Lucyano J.A.Macedo Gustavo Doubek Renato G.Freitas Hudson Zanin Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Electrical and Computer EngineeringUniversity of CampinasAv Albert Einstein 400CampinasSP 13083-852Brazil Catalytic Processes and Materials Group Department of Chemical EngineeringFaculty of Science and TechnologyMESA+Institute for NanotechnologyUniversity of TwentePO Box 2177500 AE EnschedeThe Netherlands Centre for Cooperative Research on Alternative Energies(CIC EnergiGUNE) Basque Research and Technology Alliance(BRTA)Vitoria-Gasteiz 01510Spain Institute of Physics&Institute of Chemistry Laboratory of Computational MaterialsFederal University of Mato GrossoCuiabá78060-900MTBrazil Brazilian Synchrotron Light Laboratory Brazilian Center for Research in Energy and MaterialsCampinas 13083-970SPBrazil Advanced Energy Storage Division LABCenter for Innovation on New EnergiesSchool of Chemical EngineeringUniversity of CampinasAv Albert Einstein 500Campinas 13083-852SPBrazil

Additives in the electrolytes of Li-S batteries aim to increase overall capacity,improve Li ion conductivity,enhance cyclability,and mitigate the shuttle effect,which is one of the major issues of this ***,the use of water as an additive in the commonly used electrolyte,1.0 M LiTFSI/1.0%(w/w) LiNO_(3) and a 1:1 mixture of 1,3-dioxolane(DOL) and 1,2-dimethoxyethane(DME) was *** used Co_(2)Mn_(0.5)Al_(0.5)O_(4)(CMA) as an electrocatalyst anchored on an activated carbon(AC) electrode with added sulfur via a melt-diffusion *** structural analysis of CMA via Rietveld refinement showed interatomic spaces that can promote ionic conductivity,facilitating Li^(+) ion *** tests determined 1600 ppm as the optimal water concentration,significantly reducing the shuttle ***-mortem XPS analysis focused on the lithium metal anode revealed the formation of Li_(2)O layers in dry samples and LiOH in wet *** capacity was observed in wet samples,which can be attributed to the superior ionic conductivity of LiOH at the electrode/electrolyte interface,surpassing that of Li_(2)O by 12 ***,Operando FTIR experiments provided real-time insights into electrolyte degradation and SEI formation,elucidating the activity mechanisms of water and Li_(2)CO_(3) over the *** work presents results that could aid future advancements in Li-S battery technology,offering possibilities to mitigate its challenges with inexpensive and scalable additives.

关键词： Diffusion Li-S Ionic conductivity Operando FTIR

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