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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dingqi Zhang Antonio Loquercio Xiangyu Wu Ashish Kumar Jitendra Malik Mark W. Mueller Dept. of Mechanical Engineering High Performance Robotics Lab Dept. of Electrical Engineering and Computer Science University of California Berkeley

This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters in the real world with differences in mass, size, motors, and propellers with mass differing by 4.5 times. In addition, we show rapid adaptation to sudden and large disturbances up to one-third of the mass of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://***/U-c-LbTfvoA.

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Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers

arXiv

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arXiv 2023年

作者： Liu, Huafeng Dowdell, Benjamin Engelder, Todd Pulmano, Zarah Osa, Nicolas Barman, Arko Department of Computer Science Rice University United States Data to Knowledge Lab Department of Electrical and Computer Engineering Rice University United States

Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for the diagnosis and treatment of brain cancers such as glioblastoma. Accurate tumor segmentation in MRI images is often required for treatment planning and risk assessment of treatment methods. Here, we propose a novel pipeline, Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages MaskFormer, a vision transformer model, and generates robust tumor segmentation maks. We use an ensemble of nine predictions from three models separately trained on each of the three orthogonal 2D slice directions (axial, sagittal, and coronal) of a 3D brain MRI volume. We train and test our models on the publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI (mpMRI) scans from 611 subjects. Using Dice coefficient (DC) and 95% Hausdorff distance (HD) for evaluation, our models achieved state-of-the-art results in segmenting all three different tumor regions – tumor core (DC = 0.894, HD = 2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812, HD = 1.608). © 2023, CC BY-SA.

关键词： Tumors

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A Comprehensive Survey on Detection of Non-linear Analysis Techniques for EEG Signal

A Comprehensive Survey on Detection of Non-linear Analysis T...

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Digital Health (ICDH), IEEE International Conference on

作者： Sheikh Iqbal Ahamed Masud Rabbani Richard J. Povinelli Department of Computer Science Ubicomp Lab Marquette University Mlwakke WI USA Electrical and Computer Engineering Marquette University Milwaukee WI USA

This survey provides a comprehensive overview of the different methods for detecting the nonlinearity of the EEG signal. Electroencephalography (EEG) is a widely used signal for studying different brain functions. The nonlinearity of the EEG signal is often overlooked in many studies, but these nonlinear dynamics are important to understand and for characterizing the EEG. Different methods have been applied to determine the nonlinearity of the complex dynamics of EEG signals. In this paper, we review the current state-of-art chaos theories that can be applied to detect the nonlinearity of an EEG signal. The paper summarizes, in total, 28 research articles on six different methods: Correlation Dimension, Lyapunov Exponents, Recurrence Analysis, Surrogate Data Analysis, Nonlinear Regression, and Entropy-Based Measures. The review highlights the potential of nonlinear techniques for improving traditional EEG data analysis, the challenges of current nonlinearity detection techniques and the comparison between them, and the potential opportunities of these techniques in EEG signal analysis. With the findings from this survey, we propose a chaos-based nonlinear method that can overcome the challenges with current techniques and quantify the nonlinearity of all types of EEG signals. Overall, this survey serves as a resource on the latest nonlinearity detection techniques and their potential applications in EEG signal analysis.

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Expediting Distributed GNN Training with Feature-only Partition and Optimized Communication Planning

Expediting Distributed GNN Training with Feature-only Partit...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Bingqian Du Jun Liu Ziyue Luo Chuan Wu Qiankun Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The Ohio State University USA Department of Computer Science The University of Hong Kong Hong Kong School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Feature-only partition of large graph data in distributed Graph Neural Network (GNN) training offers advantages over commonly adopted graph structure partition, such as minimal graph preprocessing cost and elimination of cross-worker subgraph sampling burdens. Nonetheless, performance bottleneck of GNN training with feature-only partitions still largely lies in the substantial communication overhead due to cross-worker feature fetching. To reduce the communication overhead and expedite distributed training, we first investigate and answer two key questions on convergence behaviors of GNN model in feature-partition based distribute GNN training: 1) As no worker holds a complete copy of each feature, can gradient exchange among workers compensate for the information loss due to incomplete local features? 2) If the answer to the first question is negative, is feature fetching in every training iteration of the GNN model necessary to ensure model convergence? Based on our theoretical findings on these questions, we derive an optimal communication plan that decides the frequency for feature fetching during the training process, taking into account bandwidth levels among workers and striking a balance between model loss and training time. Extensive evaluation demonstrates consistent results with our theoretical analysis, and the effectiveness of our proposed design.

关键词： Training Time-frequency analysis Costs Computational modeling Distributed databases Bandwidth Graph neural networks

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Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of New South Wales School of Electrical Engineering and Telecommunications NSW2052 Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2002-2012 IEEE.

关键词： Budget control

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Spatial Feature Topology-Based Heterogeneous Knowledge Transfer Framework for Long-Term Fingerprint Positioning

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IEEE Transactions on Vehicular Technology 2025年

作者： Si, Haonan Guo, Xiansheng Boateng, Gordon Owusu Yang, Yin Ansari, Nirwan University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China New Jersey Institute of Technology Advanced Networking Lab. Department of Electrical and Computer Engineering NewarkNJ07102 United States

Transfer learning (TL) is effective for addressing distribution discrepancies in fingerprint positioning. However, existing TL frameworks cannot react well to the heterogeneous feature dimensions of fingerprints caused by the topology variations of base stations (BSs) in evolving long-term environments. To address this issue, we propose a spatial feature topology-based heterogeneous knowledge transfer framework tailored for long-term fingerprint positioning (SFTP). Firstly, we partition the heterogeneous features into common and domain-specific (including source and target-specific) features from static and dynamic environmental components perspectives, respectively. Notably, we observe that the features captured from BSs with significant spatial distances differ across all samples, while those from BSs with close spatial distances show higher similarity. Based on these observations, we approximate the cross-domain mapping for each domain-specific feature by integrating the mappings of similar common features, which are easy to achieve using deep neural networks (DNNs). Subsequently, the heterogeneous feature spaces are effectively transformed into homogeneous counterparts, and a deep adaptation network (DAN) is utilized to further predict the positions for testing samples. Hence, SFTP is capable of capturing evolutionary environmental information for long-term positioning. Finally, real-world experimental results demonstrate the superiority and robustness of the proposed framework. © 1967-2012 IEEE.

关键词： Deep neural networks

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Optimizing Transistor Selection Using AH-TOR for Low Noise Amplifier Design in Sensing Applications 17

Optimizing Transistor Selection Using AH-TOR for Low Noise A...

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17th International Conference on Sensing Technology, ICST 2024

作者： Baidya, Dayarnab Thiyagarajan, Karthick Bhattacharjee, Mitradip An, Yang Indian Institute of Science Education and Research Bhopal I-Lab Department of Electrical Engineering and Computer Science Madhya Pradesh Bhopal462066 India PenrithNSW2751 Australia

ISBN: (纸本)9798350374827

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications. © 2024 IEEE.

关键词： High electron mobility transistors

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LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing

arXiv

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arXiv 2024年

作者： Ma, Zeyang Chen, An Ran Kim, Dong Jae Chen, Tse-Hsun Wang, Shaowei Lab Concordia University MontrealQC Canada Electrical and Computer Engineering Department University of Alberta EdmontonAB Canada Department of Computer Science University of Manitoba WinnipegMB Canada

Logs are important in modern software development with runtime information. Log parsing is the first step in many log-based analyses, that involve extracting structured information from unstructured log data. Traditional log parsers face challenges in accurately parsing logs due to the diversity of log formats, which directly impacts the performance of downstream log-analysis tasks. In this paper, we explore the potential of using Large Language Models (LLMs) for log parsing and propose LLMParser, an LLM-based log parser based on generative LLMs and few-shot tuning. We leverage four LLMs, Flan-T5-small, Flan-T5-base, LLaMA-7B, and ChatGLM-6B in LLMParsers. Our evaluation of 16 open-source systems shows that LLMParser achieves statistically significantly higher parsing accuracy than state-of-the-art parsers (a 96% average parsing accuracy). We further conduct a comprehensive empirical analysis on the effect of training size, model size, and pre-training LLM on log parsing accuracy. We find that smaller LLMs may be more effective than more complex LLMs;for instance where Flan-T5-base achieves comparable results as LLaMA-7B with a shorter inference time. We also find that using LLMs pre-trained using logs from other systems does not always improve parsing accuracy. While using pre-trained Flan-T5-base shows an improvement in accuracy, pre-trained LLaMA results in a decrease (decrease by almost 55% in group accuracy). In short, our study provides empirical evidence for using LLMs for log parsing and highlights the limitations and future research direction of LLM-based log parsers. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Design and Implementation of a QoL Improvement System for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Internet of Things Technology for Photovoltaic Smart Sprinkler Systems and Its Analysis

Internet of Things Technology for Photovoltaic Smart Sprinkl...

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2023 SPE Nigeria Annual International Conference and Exhibition, NAIC 2023

作者： Chinonyelum, Ejimuda Kingsley, Okoli Lilian, Uzodiagu Juliet, Olusola Department of Mechanical Engineering Texas A & M University United States Controls Lab Texas A & M University United States Department of Computer Science and Knowledge Discovery Saint Petersburg Electrotechnical University LETI Saint Petersburg Russia Artificial Intelligence Lab University of Nigeria Nsukka Nigeria Department of Electrical and Electronics Engineering Bells University of Technology Ogun State Ota Nigeria Department of Electrical and Electronics Engineering Afe Babalola University Ekiti State Ado-Ekiti Nigeria

ISBN: (纸本)9781959025177

The advancement of technology has brought about ease of life with the help of technology automation which can be applied virtually in all aspects of living. With the emergence of internet of things, prototyping real state systems has become less expensive and easy to embark on thus creating platforms for development in the oil & gas sector. This research aims to analyze, design, and develop an effortless system powered via solar energy for the control of a sprinkler system and liquid flow void of human intervention. The system was programmed using an Arduino board and other hardware at an adjustable set time with Arduino software. The research is geared at reducing human efforts in irrigation systems, to save cost and time in sprinkling liquid on any system. The use of some weather parameters like humidity, temperature, and moisture sensors;Arduino kits and GSM module constitutes the smart irrigation system setup. The system is automated to feed the necessary quantity of liquid to any given surface based on design parameters with the moisture sensor programmed within 0 to 1023 (0-100%) range. The system adapted a method of pump-start at 40% moisture reading. Various analysis of the research findings has been presented in the work with applicability in remote areas and oil fields void of human intervention. Copyright © 2023 Society of Petroleum Engineers.

关键词： Automation

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