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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jisong Kim Minjae Seong Geonho Bang Dongsuk Kum Jun Won Choi Department of Electrical Engineering Hanyang University Seoul South Korea Department of Artificial Intelligence Hanyang University Seoul South Korea Graduate School of Mobility Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Electrical and Computer Engineering College of Liberal Studies Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection. However, previous radar-camera fusion models could not fully utilize the potential of radar information. In this paper, we propose Radar-Camera Multi-level fusion (RCM-Fusion), which attempts to fuse both modalities at feature and instance levels. For feature-level fusion, we propose a Radar Guided BEV Encoder which transforms camera features into precise BEV representations using the guidance of radar Bird’s-Eye-View (BEV) features and combines the radar and camera BEV features. For instance-level fusion, we propose a Radar Grid Point Refinement module that reduces localization error by accounting for the characteristics of the radar point clouds. The experiments on the public nuScenes dataset demonstrate that our proposed RCM-Fusion achieves state-of-the-art performances among single frame-based radar-camera fusion methods in the nuScenes 3D object detection benchmark. The code will be made publicly available.

关键词： Three-dimensional displays Robot vision systems Radar detection Radar Object detection Transforms Cameras

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Abhay Chaturvedi Praveena Rao Venkata Maruti Prasad S Malini T S. Prabagar K. Bharathi Department of Electronics and Communication Engineering GLA University Mathurai India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad India Department of BS & H Aditya Institute of Technology & Management Tekkali India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Advanced Computing Alliance University Bangalore India Department of Agricultural Engineering Erode Sengunthar Engineering College Perundurai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture.

关键词： Support vector machines Precision agriculture Irrigation Accuracy Predictive models Feature extraction Internet of Things Water resources Long short term memory Mutual information

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Maximizing the Thermal Performance of Microheaters for Non-Volatile Phase Change Photonics: A Comparative study of Pulse Width Parameter Effects 23

Maximizing the Thermal Performance of Microheaters for Non-V...

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23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： Vasquez-Aza, Francis Sun, Hongyi Lian, Chuanyu Huang, Yi-Siou Vitale, Steven A. Takeuchi, Ichiro Hu, Juejun Youngblood, Nathan Ocampo, Carlos A. Rios Pavlidis, Georges University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering StorrsCT06269 United States University of Maryland Department of Materials Science and Engineering College ParkMD20742 United States University of Maryland Institute for Research in Electronics and Applied Physics College ParkMD20742 United States Mit Lincoln Laboratory Advanced Materials and Microsystems Group LexingtonMA02139 United States University of Maryland Quantum Materials Center Department of Physics College ParkMD20742 United States Massachusetts Institute of Technology Department of Materials & Science Engineering CambridgeMA02139 United States The University of Pittsburgh Electrical & Computer Engineering Department PittsburghPA15213 United States

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364330

Guiding, manipulating, and detecting light is integral to modern photonic systems that can be designed for displays, memory, data transfer, and sensors. The prospect of realizing programmable yet zero-static power photonics has recently gained attention thanks to the rise of optical phase change materials (PCMs). PCMs have a unique set of properties that make them desirable for this task: nonvolatile retention and reversible switching between their amorphous and crystalline states, which, in addition, display distinct optical and electronic properties. Nonvolatile photonic devices have primarily relied on either electrical or optical switching of PCMs. electrical switching offers effortless CMOS integration;however, it is predominantly limited to binary output. In contrast, optical switching enables accurate multilevel control but is limited in scalability. This paper studies an alternative electro-thermal approach to nonvolatile photonics by controlling the PCM phase via doped silicon microheaters. The results highlight the significance of precise spatial and temporal temperature control to ensure optimal modulation of the PCM. Specifically, this paper investigates the impact of the electric pulse width (0.2 μs to 10 μs) on the maximum temperature for two different microheater designs. High resolution (≈410 nm) transient thermoreflectance imaging (TTI) is used to measure and analyze the surface temperature distribution across each microheater under pulsed biasing. To assess the impact of varying pulse widths, this study systematically examines their effects on maximum temperature, response time, and maximum power. Shorter pulse widths ( 1 μs) provide greater thermal stability but exhibit reduced maximum temperatures due to limited current levels. The experimental results demonstrate a nonlinear relationship between the peak temperature and the power supplied at sub-steady state thermalization time scales. For the specific microheater geometry, the optimal pulse wid

关键词： Photonic integration technology

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Wide-Angle Single-lobe Beam-Steering using Optical Phased Arrays on Implantable Neural Probes

Wide-Angle Single-lobe Beam-Steering using Optical Phased Ar...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ankita Sharma Alperen Govdeli Tianyuan Xue Fu-Der Chen Xianshu Luo Hongyao Chua Guo-Qiang Lo Wesley D. Sacher Joyce K.S. Poon Max Planck Institute of Microstructure Physics Halle (Saale) Germany Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Advanced Micro Foundry Pte Ltd Singapore

We demonstrate single-lobe beam-steering over ~50° at λ=440-460 nm using an end-fire optical phased array coupled to a 1D grating for out-of-plane emission. The devices were integrated on implantable neural prob... 详细信息

ISBN: (纸本)9781957171258

关键词： Array waveguide devices Beam steering Emission Gradient index lenses Optogenetics Phased arrays

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Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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AI-Aided Height Optimization for NOMA-UAV Networks

AI-Aided Height Optimization for NOMA-UAV Networks

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Amira O. Hashesh Adly S. Tag Eldien Mostafa M. Fouda Rokaia M. Zaki Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt High Institute for Engineering and Technology - Obour Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Higher Institute of Engineering and Technology Kafrelsheikh Egypt

Currently, Unmanned Aerial Vehicles (UAVs) are gaining significant attention due to their potential to effectively carry out a variety of tasks with superior performance through the use of fifth-generation (5G) and sixth-generation (6G) networks. Non-orthogonal multiple access (NOMA) techniques can further improve the performance and efficiency while reducing the interference. In this paper, we propose the application of machine learning (ML) techniques to evaluate the outage performance of a NOMA-enabled UAV network. Specifically, this study investigates the optimal UAV height that allows two users on the ground to receive the best service when they are simultaneously served by one UAV. We generated our own dataset which included several network parameters. We then trained various machine learning techniques on this dataset, including artificial neural networks (ANN), support vector regression (SVR), and linear regression (LR). Our results indicate that ANN provides the best accuracy compared with SVR and LR, with an average root mean squared error (RMSE) of 0.0931.

关键词： Support vector machines NOMA Linear regression Artificial neural networks Machine learning Interference Autonomous aerial vehicles

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LARGE LANGUAGE MODELS FOR FORECASTING AND ANOMALY DETECTION: A SYSTEMATIC LITERATURE REVIEW

arXiv

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arXiv 2024年

作者： Su, Jing Jiang, Chufeng Jin, Xin Qiao, Yuxin Xiao, Tingsong Ma, Hongda Wei, Rong Jing, Zhi Xu, Jiajun Lin, Junhong AT&T Center for Virtualization Southern Methodist University DallasTX United States Department of Computer Science and Engineering Ohio State University ColumbusOH United States Department of Computer Science The University of Texas at Austin AustinTX United States Department of Information System Universidad Internacional Isabel I de Castilla Burgos Spain Department of Computer & Information Science & Engineering University of Florida GainesvilleFL United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States

This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models’ knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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First-principle Molecular Dynamics Studies of the Thermophysical Properties of LiF-NaF-ThF4

First-principle Molecular Dynamics Studies of the Thermophys...

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2022 Transactions of the American Nuclear Society Winter Meeting and Technology Expo, ANS 2022

作者： Grizzi, Vitor Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

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Controllable multi-level switching of optical phase change materials via microheater temperature profile engineering

Controllable multi-level switching of optical phase change m...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sun, Hongyi Lian, Chuanyu Vásquez-Aza, Francis Huang, Yi-Siou Vitale, Steven A. Takeuchi, Ichiro Hu, Juejun Youngblood, Nathan Pavlidis, Georges Ríos Ocampo, Carlos A. Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Mechanical Engineering University of Connecticut StorrsCT06269 United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA02139 United States Quantum Materials Center Department of Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering MIT CambridgeMA02139 United States Electrical & Computer Engineering Department The University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781957171395

We demonstrate doped-silicon-on-insulator microheaters with triangle-like temperature profiles. Such devices control the hotspot size and temperature and, thus, the area that undergoes amorphization or crystallization in optical phase-change materials, leading to controllable multilevel, nonvolatile response. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Optical materials

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

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IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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