IEEE Access最新文献

{"title":"An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning","authors":"Jinqiang Li;Miao Ye;Linqiang Huang;Xiaofang Deng;Hongbing Qiu;Yong Wang;Qiuxiang Jiang","doi":"10.1109/ACCESS.2023.3302178","DOIUrl":"10.1109/ACCESS.2023.3302178","url":null,"abstract":"To address the challenges of obtaining network state information, flexibly forwarding data, and improving the communication quality of service (QoS) in wireless network transmission environments in response to dynamic changes in network topology, this paper introduces an intelligent routing algorithm based on deep reinforcement learning (DRL) with network situational awareness under a software-defined wireless networking (SDWN) architecture. First, comprehensive network traffic information is collected under the SDWN architecture, and a graph convolutional network-gated recurrent unit (GCN-GRU) prediction mechanism is used to perceive future traffic trends. Second, a proximal policy optimization (PPO) DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the DRL environment, while next-hop adjacent nodes are treated as executable actions, and action selection policies are designed for different network conditions. To guide the learning and improvement of the DRL agent’s routing strategy, reward functions of different forms are designed by utilizing network link information and different penalty mechanisms. Additionally, importance sampling steps and gradient clipping methods are employed during gradient updating to enhance the convergence speed and stability of the designed intelligent routing method. Experimental results show that this solution outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling Deep Q-Network (DQN) routing, the convergence of the proposed method is significantly faster and more stable. Simultaneously, hardware storage consumption is reduced, and real-time routing decisions can be made using the current network state information. The source code can be accessed at \u0000<uri>https://github.com/GuetYe/DRL-PPONSA</uri>\u0000.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"83322-83342"},"PeriodicalIF":3.9,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10209181.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42807036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MeROS: SysML-Based Metamodel for ROS-Based Systems","authors":"Tomasz Winiarski","doi":"10.1109/ACCESS.2023.3301727","DOIUrl":"10.1109/ACCESS.2023.3301727","url":null,"abstract":"The complexity of today’s robot control systems implies difficulty in developing them efficiently and reliably. Systems engineering (SE) and frameworks come to help. The framework metamodels are needed to support the standardisation and correctness of the created application models. Although the use of frameworks is widespread nowadays, for the most popular of them, Robot Operating System (ROS), a contemporary metamodel has been missing so far. This article proposes a new metamodel for ROS called MeROS, which addresses the running system and developer workspace. The ROS comes in two versions: ROS 1 and ROS 2. The metamodel includes both versions. In particular, the latest ROS 1 concepts are considered, such as nodelet, action, and metapackage. An essential addition to the original ROS concepts is the grouping of these concepts, which provides an opportunity to illustrate the system’s decomposition and varying degrees of detail in its presentation. The metamodel is derived from the requirements and verified on the practical example of Rico assistive robot. The matter is described in a standardised way in SysML (Systems Modeling Language). Hence, common development tools that support SysML can help develop robot controllers in the spirit of SE.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"82802-82815"},"PeriodicalIF":3.9,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10207804.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42756765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RGB-D and Thermal Sensor Fusion: A Systematic Literature Review","authors":"Martin Brenner;Napoleon H. Reyes;Teo Susnjak;Andre L. C. Barczak","doi":"10.1109/ACCESS.2023.3301119","DOIUrl":"10.1109/ACCESS.2023.3301119","url":null,"abstract":"In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal modalities to date. While autonomous driving using LiDAR, radar, RGB, and other sensors has garnered substantial research interest, along with the fusion of RGB and depth modalities, the integration of thermal cameras and, specifically, the fusion of RGB-D and thermal data, has received comparatively less attention. This might be partly due to the limited number of publicly available datasets for such applications. This paper provides a comprehensive review of both, state-of-the-art and traditional methods used in fusing RGB-D and thermal camera data for various applications, such as site inspection, human tracking, fault detection, and others. The reviewed literature has been categorised into technical areas, such as 3D reconstruction, segmentation, object detection, available datasets, and other related topics. Following a brief introduction and an overview of the methodology, the study delves into calibration and registration techniques, then examines thermal visualisation and 3D reconstruction, before discussing the application of classic feature-based techniques and modern deep learning approaches. The paper concludes with a discourse on current limitations and potential future research directions. It is hoped that this survey will serve as a valuable reference for researchers looking to familiarise themselves with the latest advancements and contribute to the RGB-DT research field.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"82410-82442"},"PeriodicalIF":3.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10201865.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47439451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Bipedal Walking for Humanoids With Current Feedback","authors":"Rohan P. singh;Zhaoming Xie;Pierre Gergondet;Fumio Kanehiro","doi":"10.1109/ACCESS.2023.3301175","DOIUrl":"10.1109/ACCESS.2023.3301175","url":null,"abstract":"Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with low gear-ratio transmission systems. Application to real, life-sized humanoid robots has been less common arguably due to a large sim2real gap. In this paper, we present an approach for effectively overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our key idea is to utilize the current feedback from the actuators on the real robot, after training the policy in a simulation environment artificially degraded with poor torque-tracking. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion. Through ablations, we also show that a feedforward policy architecture combined with targeted dynamics randomization is sufficient for zero-shot sim2real success, thus eliminating the need for computationally expensive, memory-based network architectures. Finally, we validate the robustness of the proposed RL policy by comparing its performance against a conventional model-based controller for walking on uneven terrain with the real robot.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"82013-82023"},"PeriodicalIF":3.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10201853.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42312194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iterative Interference Cancellation for Time Reversal Division Multiple Access","authors":"Ali Mokh;George C. Alexandropoulos;Mohamed Kamoun;Abdelwaheb Ourir;Arnaud Tourin;Mathias Fink;Julien de Rosny","doi":"10.1109/ACCESS.2023.3300993","DOIUrl":"10.1109/ACCESS.2023.3300993","url":null,"abstract":"Time Reversal (TR) has been proposed as a competitive precoding strategy for low-complexity devices, relying on ultra-wideband waveforms. This transmit processing paradigm can address the need for low power and low complexity receivers, which is particularly important for the Internet of Things, since it shifts most of the communications signal processing complexity to the transmitter side. Due to its spatio-temporal focusing property, TR has also been used to design multiple access schemes for multi-user communications scenarios. However, in wideband time-division multiple access schemes, the signals received by users suffer from significant levels of inter-symbol interference as well as interference from uncoordinated users, which often require additional processing at the receiver side. This paper proposes an iterative TR scheme that aims to reduce the level of interference in wideband multi-user settings, while keeping the processing complexity only at the transmitter side. The performance of the proposed TR-based protocol is evaluated using analytical derivations. In addition, its superiority over the conventional Time Reversal Division Multiple Access (TRDMA) scheme is demonstrated through simulations as well as experimental measurements at 2.5 GHz carrier frequency with variable bandwidth values.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"92788-92795"},"PeriodicalIF":3.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10201848.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47571446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Direction-of-Arrival Estimation Using Array Feedback Beamforming in Low SNR Scenarios","authors":"Parth Mehta;Kumar Appaiah;Rajbabu Velmurugan","doi":"10.1109/ACCESS.2023.3300709","DOIUrl":"10.1109/ACCESS.2023.3300709","url":null,"abstract":"A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitative comparison we show that our approach outperforms the previous feedback beamforming approach with a single transmit antenna, and the conventional beamformer. We then incorporate a retransmission based minimum variance distortionless response (MVDR) beamformer with the feedback beamforming setup. We propose two approaches, show that one approach is superior in terms of lower estimation error, and use that as the DoA estimation method. We then compare this approach with Multiple Signal Classification (MUSIC), Estimation of Parameters using Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods. The results show that at SNR levels of \u0000<inline-formula> <tex-math>$-60,,mathrm { text {d} text {B} }$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$-10,,mathrm { text {d} text {B} }$ </tex-math></inline-formula>\u0000, the angle estiation error of the proposed method is 20° less compared to that of prior methods.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"80647-80655"},"PeriodicalIF":3.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10198436.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41558926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks With Multi-Robot Aerial Systems","authors":"Alvaro Caballero;Giuseppe Silano","doi":"10.1109/ACCESS.2023.3300240","DOIUrl":"10.1109/ACCESS.2023.3300240","url":null,"abstract":"This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"81361-81377"},"PeriodicalIF":3.9,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10197369.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning","authors":"Heng Zhang;Danilo Vasconcellos Vargas","doi":"10.1109/ACCESS.2023.3299296","DOIUrl":"10.1109/ACCESS.2023.3299296","url":null,"abstract":"Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model’s rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model’s dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC’s recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain’s mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"81033-81070"},"PeriodicalIF":3.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10196105.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41577091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testbed as a RegUlatory Science Tool (TRUST): A Testbed Design for Evaluating 5G-Enabled Medical Devices","authors":"Yongkang Liu;Mohamad Omar Al Kalaa","doi":"10.1109/ACCESS.2023.3299242","DOIUrl":"10.1109/ACCESS.2023.3299242","url":null,"abstract":"The fifth-generation (5G) cellular communication technology introduces technical advances that can expand medical device access to connectivity services. However, assessing the safety and effectiveness of emerging 5G-enabled medical devices is challenging as relevant evaluation methods have not yet been established. In this paper, we propose a design model for 5G testbed as a regulatory science tool (TRUST) for assessing 5G connectivity enablers of medical device functions. Specifically, we first identify application specific testing needs and general testing protocols. Next, we outline the selection and customization of key system components to create a 5G testbed. A TRUST demonstration is documented through a realistic 5G testbed implementation along with the deployment of a custom-built example use-case for 5G-enabled medical extended reality (MXR). Detailed configurations, example collected data, and implementation challenges are presented. The openness of the TRUST design model allows a TRUST testbed to be easily extended and customized to incorporate available resources and address the evaluation needs of different stakeholders.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"81563-81576"},"PeriodicalIF":3.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10196310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10241376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Attention Graph Pooling for Efficient Pairwise Graph Interaction Learning","authors":"Junhyun Lee;Bumsoo Kim;Minji Jeon;Jaewoo Kang","doi":"10.1109/ACCESS.2023.3299267","DOIUrl":"10.1109/ACCESS.2023.3299267","url":null,"abstract":"Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"78549-78560"},"PeriodicalIF":3.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10196307.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43386952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}