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arXiv 2023年

作者： Shen, Xuemin Gao, Jie Wu, Wen Li, Mushu Zhou, Conghao Zhuang, Weihua The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Marquette University MilwaukeeWI53233 United States The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G. © 2023, CC BY-NC-SA.

关键词： Network architecture

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Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery 46th

Exploring Seasonal Variability in the Context of Neural R...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Kåreborn, Liv Ingerstad, Erica Berg, Amanda Karlsson, Justus Haglund, Leif Maxar International Sweden AB Ebbegatan 13 Linköping582 13 Sweden AI Sweden Folkungagatan 44 Stockholm118 26 Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden

ISBN: (纸本)9783031851865

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model’s ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Indoor Navigation System for Visually Impaired People using computer vision

Indoor Navigation System for Visually Impaired People using ...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Fraga, Adrian Leyte Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Universidad Politécnica de Madrid E.T.S.I. Industriales Madrid Spain

ISBN: (纸本)9781665480093

This paper studies an indoor navigation guidance system for visually impaired people using Artificial Intelligence (AI) and computer vision techniques to guide users via optimal path based on quick response (QR) code markers and collision avoidance system supported by the monocular depth estimation algorithm. The proposed system utilizes a set of QR code markers, as location beacons, to generate an optimal path for the user to reach the destination. The identified QR code markers are used to correlate and compare information from the online database containing valuable information such as current location information, and the next possible locations from the current location. In addition, our system embraces a safety feature, a collision avoidance system, by utilizing the monocular depth estimation algorithm to identify obstacles in the path to the desired destination. All obtained information is provided to the user through a text-to-speech engine, where the system can direct the user to the optimal path via audio output. © 2022 IEEE.

关键词： Location

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Design Flow and Implementation of an AI-Enabled Smart Guidance System for Art Galleries

Design Flow and Implementation of an AI-Enabled Smart Guidan...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Esteban, Alvaro Belloso Delgado, Mario Silvestre Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Universidad Politécnica de Madrid XE.T.S.I. Industriales Madrid Spain

ISBN: (纸本)9781665480093

Audio guides are commonly utilized to enrich the experience of art gallery visitors and to fully engage them with the artwork by providing background, contexts, and other information related to the corresponding artists. However, this method may be monotonous to the public and cannot automatically change to the appropriate recording when users move to the next artwork. The traditional operation of audio guides is only enabled by the user's visual recognition of the artwork to activate appropriate commentaries manually. In this paper, we explore the potential enhancement of the art gallery visitors' experience by applying Artificial Intelligence (AI), machine learning, and computer vision techniques to recognize the artwork's main features automatically and provide the corresponding artist and other information to the visitors. We train a model using a Deep Convolutional Neural Network (CNN) and Kaggle's Painter by Numbers dataset, and host the trained model on a low-power AI accelerator equipped with a camera to recognize the artwork in real-time. The results are then transmitted to a database, which is utilized by a custom Android application on the smartphone to provide updated artworkrelated information on the display or by audio. Our system targets not only the general public for an interactive experience in the art gallery but also visually impaired people for proper guidance with the smartphone's camera and its voice feedback capabilities. © 2022 IEEE.

关键词： Deep neural networks

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Applying Machine Learning in Designing Distributed Auction for Multi-agent Task Allocation with Budget Constraints 20

Applying Machine Learning in Designing Distributed Auction f...

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20th International Conference on Advanced robotics, ICAR 2021

作者： Luo, Chen Huang, Qinwei Kong, Fanxin Khan, Simon Qiu, Qinru Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY United States Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9781665436847

The multi-agent task allocation can be solved in a distributed manner using Consensus-Based Bundle Algorithm (CBBA). Under this distributed auction process, each agent greedily maximizes the global score, which is the difference of the reward and the cost, through an iterative bundle construction and conflict resolution procedure. The distributed algorithm has provable convergence and guarantees 50% optimality if the score function satisfies the condition of diminishing marginal gain (DMG). While the previous work focuses on unconstrained optimization of rewards, this paper aims at applying CBBA to task allocation with budget constraints. Several heuristics were proposed to build the bundle and calculate the bidding scores as improvements to the original CBBA algorithm. We then prove that some of the new score functions are DMG, and therefore guarantees the convergence of the distributed process. We also show that these heuristic extended CBBAs are Pareto efficient;using different heuristic extensions under different scenarios is more efficient than consistently using the same one. To decide which heuristic extension should be used for a given task allocation problem, a graph convolutional neural network (GCN) model is trained to extract and analyze the features of the constrained optimization problem as a graph, and predict the potential performance (i.e., global reward) of different heuristic extensions. Based on the prediction, the best heuristic extension will be selected. Experimental results show that the predicted reward has more than 0.98 correlation with the actual reward and for 70% of time the prediction guided selection picks the best heuristic extension for the budget constrained task allocation problem. © 2021 IEEE.

关键词： Iterative methods

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Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

Learning Perceptual Hallucination for Multi-Robot Navigation...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jin-Soo Park Xuesu Xiao Garrett Warnell Harel Yedidsion Peter Stone Department of Electrical and Computer Engineering The University of Texas at Austin United States Department of Computer Science George Mason University United States Everyday Robots United States Department of Computer Science The University of Texas at Austin United States Army Research Laboratory United States Sony AI Japan

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other in a narrow hallway, they may either turn around to find an alternative route or collide with each other. This paper presents a new approach to navigation that allows two robots to pass each other in a narrow hallway without colliding, stopping, or waiting. Our approach, Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically generate virtual obstacles (i.e., perceptual hallucination) to facilitate passing in narrow hallways by multiple robots that utilize otherwise standard autonomous navigation systems. Our experiments on physical robots in a variety of hallways show improved performance compared to multiple baselines.

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Video Instance Segmentation with Recurrent Graph Neural Networks 43rd

Video Instance Segmentation with Recurrent Graph Neural Net...

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43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Johnander, Joakim Brissman, Emil Danelljan, Martin Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden Zenseact Gothenburg Sweden Saab Bröderna Ugglas gata Linköping582 54 Sweden Computer Vision Lab ETH Zürich Zürich Switzerland School of Engineering University of KwaZulu-Natal Durban South Africa

ISBN: (纸本)9783030926588

Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach. © 2021, Springer Nature Switzerland AG.

关键词： computer vision

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Automatic search for photoacoustic marker using automated transrectal ultrasound

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Biomedical Optics Express 2023年第11期14卷 6016-6030页

作者： Wu, Zijian Moradi, Hamid Yang, Shuojue Song, Hyunwoo Boctor, Emad M. Salcudean, Septimiu E. Johns Hopkins University Laboratory for Computational Sensing and Robotics BaltimoreMD United States University of British Columbia Dept. of Electrical and Computer Engineering VancouverBC Canada University of Texas at Austin Walker Department of Mechanical Engineering AustinTX United States Johns Hopkins University Dept. of Computer Science BaltimoreMD United States

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [IEEE Robot. Autom. Lett 8, 1287 (2023).]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53°±0.30° average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29◦±0.31◦ and 0.48°±0.32° average errors after 28 data acquisitions. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Urology

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arXiv

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Collaborative Trolley Transportation System with Autonomous ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bingyi Xia Hao Luan Ziqi Zhao Xuheng Gao Peijia Xie Anxing Xiao Jiankun Wang Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China School of Computing National University of Singapore Singapore Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering Department of Electrical and Computer Engineering Chinese University of Hong Kong in Hong Kong University of Alberta Canada

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video 1 1 Video demonstration: https://***/efnPERm0Rco.)

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