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arXiv 2024年

作者： Ceglia, Amedeo Facon, Kael Begon, Mickaël Seoud, Lama Institute of Biomedical Engineering University of Montreal MontrealQC Canada Ecole d'ingénieurs informatique Paris Le Kremlin-Bicêtre France School of Kinesiology and Human Kinetics University of Montreal MontrealQC Canada Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Biomechanical biofeedback may enhance rehabilitation and provide clinicians with more objective task evaluation. These feedbacks often rely on expensive motion capture systems, which restricts their widespread use, leading to the development of computer vision-based methods. These methods are subject to large joint angle errors, considering the upper limb, and exclude the scapula and clavicle motion in the analysis. Our open-source approach offers a user-friendly solution for high-fidelity upper-limb kinematics using a single low-cost RGBD camera and includes semi-automatic skin marker labeling. Real-time biomechanical analysis, ranging from kinematics to muscle force estimation, was conducted on eight participants performing a hand-cycling motion to demonstrate the applicability of our approach on the upper limb. Markers were recorded by the RGBD camera and an optoelectronic camera system, considered as a reference. Muscle activity and external load were recorded using eight EMG and instrumented hand pedals, respectively. Bland-Altman analysis revealed significant agreements in the 3D markers' positions between the two motion capture methods, with errors averaging 3.3±3.9 mm. For the biomechanical analysis, the level of agreement was sensitive to whether the same marker set was used. For example, joint angle differences averaging 2.3±2.8° when using the same marker set, compared to 4.5 pm 2.9° otherwise. Biofeedback from the RGBD camera was provided at 63 Hz. Our study introduces a novel method for using an RGBD camera as a low-cost motion capture solution, emphasizing its potential for accurate kinematic reconstruction and comprehensive upper-limb biomechanical studies. © 2024, CC BY.

关键词： Cameras

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Extending Cislunar Communication Network Reach Using Reconfigurable Intelligent Surfaces

arXiv

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arXiv 2024年

作者： Huroon, Aamer Mohamed Donmez, Baris Huang, Yu-Chih Kurt, Gunes Karabulut Wang, Li-Chun EECS International Graduate Program National Yang Ming Chiao Tung University Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Dept. of Electrical Engineering Polytechnique Montréal Montréal Canada

This study introduces a novel approach to enhance communication networks in the cislunar space by leveraging Reconfigurable Intelligent Surfaces (RIS). Using the ability of RIS to dynamically control electromagnetic waves, this paper tackles the challenges of signal attenuation, directivity, and divergence in cislunar missions, primarily caused by immense distances and that Earth-based station transmitters do not always face the Moon. A new optimization problem is formulated, whose objective is to maximize the received signal-to-noise ratio (SNR) for Earth-to-Moon communications. We derive a closed-form solution to the problem of determining the optimal RIS phase shift configuration based on the effective area of the RIS. Through extensive simulations, this paper demonstrates how optimal adjustments in RIS phase shifts can significantly enhance signal integrity, hinting at the substantial potential of RIS technology to revolutionize long-distance cislunar communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Moon

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Digital Twin-empowered Network Slicing in B5G Networks: Experience-driven approach

Digital Twin-empowered Network Slicing in B5G Networks: Expe...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Naeem, Faisal Kaddoum, Georges Tariq, Muhammad Ecole de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada National University of Computer and Emerging Sciences Electrical Engineering Department 25000 Pakistan

ISBN: (纸本)9781665423908

Network slicing is considered a promising networking pillar of efficient resource management in beyond 5G (B5G) networks. However, the dynamic and complex characteristics of future networks pose challenges in designing novel resource allocation techniques due to the stringent quality of service (QoS) requirements and virtualized network infrastructures. To solve this issue, we propose a digital twin (DT)-enabled deep distributional Q-network (DDQN) framework that constructs a digital mirror of the physical slicing-enabled network to simulate its complex environment and predict the dynamic characteristics of the network. The DT of network slicing is expressed as a graph, and a graph neural network (GNN) is developed to learn the complicated relationships of the network slice. The graph-based network states are forwarded to the DDQN agent to learn the optimal network slicing policy. Through simulations, it is demonstrated that the proposed technique can satisfy the stringent QoS requirements and achieve near-optimal performance in a dynamic B5G network. © 2021 IEEE.

关键词： Software defined networking

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INTELLIGENT PROACTIVE FAULT TOLERANCE AT THE EDGE THROUGH RESOURCE USAGE PREDICTION

arXiv

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arXiv 2023年

作者： Theodoropoulos, Theodoros Tsanakas, Stylianos Tserpes, Konstantinos Violos, John Leivadeas, Aris Varvarigou, Theodora Department of Informatics & Telematics Harokopio University of Athens Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Software IT Engineering Ecole de technologie superieure Montreal Canada

The proliferation of demanding applications and edge computing establishes the need for an efficient management of the underlying computing infrastructures, urging the providers to rethink their operational methods. In this paper, we propose an Intelligent Proactive Fault Tolerance (IPFT) method that leverages the edge resource usage predictions through Recurrent Neural Networks (RNN). More specifically, we focus on the process-faults, which are related with the inability of the infrastructure to provide Quality of Service (QoS) in acceptable ranges due to the lack of processing power. In order to tackle this challenge we propose a composite deep learning architecture that predicts the resource usage metrics of the edge nodes and triggers proactive node replications and task migration. Taking also into consideration that the edge computing infrastructure is also highly dynamic and heterogeneous, we propose an innovative Hybrid Bayesian Evolution Strategy (HBES) algorithm for automated adaptation of the resource usage models. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Additionally, the IPFT mechanism that leverages the resource usage predictions has been evaluated in an extensive simulation in CloudSim Plus and the results show significant improvement compared to the reactive fault tolerance method in terms of reliability and maintainability. © 2023, CC BY.

关键词： Fault tolerance

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An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments

IEEE Transactions on Field Robotics

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IEEE Transactions on Field Robotics 2024年 1卷 476-526页

作者： Benjamin Morrell Kyohei Otsu Ali Agha David D. Fan Sung-Kyun Kim Muhammad Fadhil Ginting Xianmei Lei Jeffrey Edlund Seyed Fakoorian Amanda Bouman Fernando Chavez Taeyeon Kim Gustavo J. Correa Maira Saboia Angel Santamaria-Navarro Brett Lopez Boseong Kim Chanyoung Jung Mamoru Sobue Oriana Claudia Peltzer Joshua Ott Robert Trybula Thomas Touma Marcel Kaufmann Tiago Stegun Vaquero Torkom Pailevanian Matteo Palieri Yun Chang Andrzej Reinke Matthew Anderson Frederik E. T. Schöller Patrick Spieler Lillian M. Clark Avak Archanian Kenny Chen Hovhannes Melikyan Anushri Dixit Harrison Delecki Daniel Pastor Barry Ridge Nicolas Marchal Jose Uribe Sharmita Dey Kamak Ebadi Kyle Coble Alexander Nikitas Dimopoulos Vivek Thangavelu Vivek S. Varadharajan Nicholas Palomo Antoni Rosinol Arghya Chatterjee Christoforos Kanellakis Bjorn Lindqvist Micah Corah Kyle Strickland Ryan Stonebraker Michael Milano Christopher E. Denniston Sami Sahnoune Thomas Claudet Seungwook Lee Gautam Salhotra Edward Terry Rithvik Musuku Robin Schmid Tony Tran Ara Kourchians Justin Schachter Hector Azpurua Levi Resende Arash Kalantari Jeremy Nash Josh Lee Christopher Patterson Jennifer G. Blank Kartik Patath Yuki Kubo Ryan Alimo Yasin Almalioglu Aaron Curtis Jacqueline Sly Tesla Wells Nhut T. Ho Mykel Kochenderfer Giovanni Beltrame George Nikolakopoulos David Shim Luca Carlone And Joel Burdick NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Aeronautics and Astronautics Stanford University Stanford CA USA Department of Electrical And Information Engineering Polytechnic University of Bari Bari Italy Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Computer Engineering and Software Engineering École Polytechnique de Montréal Montreal QC Canada Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Mechanical Engineering California State University at Northridge Northridge CA USA Department of Earth and Planetary Sciences McGill University Montreal QC Canada Blue Marble Space Institute of Science Seattle WA USA

This article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robots (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal ground–aerial exploration *** demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.

关键词： Planning State estimation Semantics NASA Hardware Three-dimensional displays Systems architecture

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Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning

arXiv

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arXiv 2023年

作者： Sclocchi, Antonio Geiger, Mario Wyart, Matthieu Institute of Physics École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can operate in distinct training regimes. Here we study how the magnitude of this noise T affects performance as the size of the training set P and the scale of initialization α are varied. For gradient descent, α is a key parameter that controls if the network is ‘lazy’ (α 1) or instead learns features (α 1). For classification of MNIST and CIFAR10 images, our central results are: (i) obtaining phase diagrams for performance in the (α, T) plane. They show that SGD noise can be detrimental or instead useful depending on the training regime. Moreover, although increasing T or decreasing α both allow the net to escape the lazy regime, these changes can have opposite effects on performance. (ii) Most importantly, we find that the characteristic temperature Tc where the noise of SGD starts affecting the trained model (and eventually performance) is a power law of P. We relate this finding with the observation that key dynamical quantities, such as the total variation of weights during training, depend on both T and P as power laws. These results indicate that a key effect of SGD noise occurs late in training, by affecting the stopping process whereby all data are fitted. Indeed, we argue that due to SGD noise, nets must develop a stronger ‘signal’, i.e. larger informative weights, to fit the data, leading to a longer training time. A stronger signal and a longer training time are also required when the size of the training set P increases. We confirm these views in the perceptron model, where signal and noise can be precisely measured. Interestingly, exponents characterizing the effect of SGD depend on the density of data near the decision boundary, as we explain. © 2023, CC BY.

关键词： Deep neural networks

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On Data Association with Possibly Unresolved Measurements

On Data Association with Possibly Unresolved Measurements

引用

International Conference on Information Fusion

作者： Augustin A. Saucan Florian Meyer Télécom SudParis Institut Polytechnique de Paris Paris France Department of Electrical and Computer Engineering Scripps Institution of Oceanography University of California San Diego San Diego CA

Tracking targets based on measurements provided by radar, sonar, or lidar sensors is essential to obtain situational awareness in important applications, including autonomous navigation and applied ocean sciences. A key challenge in multitarget tracking is the unknown association between the available measurements and the targets to be tracked. In particular, robust data association for closely spaced targets requires advanced methods that explicitly model unresolved measurements. Due to limited sensor resolution, the sensor produces a single measurement for two or more actual targets. If not explicitly modeled in the multitarget tracking method, unresolved measurements lead to track losses and thus, to significant tracking errors. In this paper, we propose a scalable data association method for the tracking of multiple potentially unresolved targets. A loopy belief propagation method is presented that efficiently approximates the marginal association probabilities given a set of potentially unresolved measurements. This method scales quadratically in the number of targets and linearly in the number of measurements. Our numerical results demonstrate that the computed approximate marginal association probabilities are close in $L_{1}$ distance to the true marginal association probabilities, which can only be calculated for very small tracking scenarios.

关键词：

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Conformal Wide-Angle Scanning Leaky-Wave Antenna for V-Band On-Body Applications

arXiv

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arXiv 2024年

作者： Vadher, Pratik Skrivervik, Anja K. Zeng, Qihang Sauleau, Ronan Ho, John S. Sacco, Giulia Nikolayev, Denys IETR UMR 6164 CNRS Univ Rennes RennesFR-35000 France Microwave and Antenna Group Ecole Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland Department of Electrical and Computer Engineering The Institute for Health Innovation and Technology The N.1 Institute National University of Singapore Singapore117583 Singapore

Wearable on-body millimeter-wave (mmWave) radars can provide obstacle detection and guidance for visually impaired people. However, their everyday performance is hindered by the rigid form factor and limited scanning range. In this article, we propose a low-profile, fast-scanning leaky-wave antenna (LWA) operating in the unlicensed V-band (57–64 GHz) to be integrated for on-body applications such as lightweight portable frequency-modulated continuous wave (FMCW) radars. The proposed LWA consists of meandering microstrips that can conform to the human body curvatures while maintaining beamforming and beam-scanning properties. Experimental results demonstrate that the planar LWA achieves a realized gain above 10 dB with a fan-beam steering range in the H-plane from −40° to 43° over the operating frequency band while the half power beam-width (HPBW) is within 20°. Since for the foreseen application the antenna is supposed to conform to the user’s body, the performance is also analyzed for a bent condition. The beam steering range changes to −32° to 50° when placed on the knee (corresponding to 80 mm radius). Under bending conditions, the LWA exhibits a maximum degradation of 1.75 dB, while the HPBW increases to 25°. This shows that due to the small size of the antenna, the impact of bending is low and the beam-forming and beam-scanning property of the designed LWA remain intact. Furthermore, we enable 2-D spatial scanning by employing an array of twelve LWAs with phased excitation, extending the scanning range in the E-plane from −40° to 40°, while the HPBW remains below 20° across the operational frequency range. © 2024, CC BY.

关键词： Scanning antennas

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Task load dependent decision referrals for joint binary classification in human-automation teams

arXiv

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arXiv 2025年

作者： Kaza, Kesav Ny, Jerome Le Mahajan, Aditya Polytechnique Montreal Canada Department of Electrical Engineering Polytechnique Montreal Canada Department of Electrical and Computer Engineering McGill University Canada GERAD Montreal Canada

We consider the problem of optimal decision referrals in human-automation teams performing binary classification tasks. The automation, which includes a pre-trained classifier, observes data for a batch of independent tasks, analyzes them, and may refer a subset of tasks to a human operator for fresh and final analysis. Our key modeling assumption is that human performance degrades with task load. We model the problem of choosing which tasks to refer as a stochastic optimization problem and show that, for a given task load, it is optimal to myopically refer tasks that yield the largest reduction in expected cost, conditional on the observed data. This provides a ranking scheme and a policy to determine the optimal set of tasks for referral. We evaluate this policy against a baseline through an experimental study with human participants. Using a radar screen simulator, participants made binary target classification decisions under time constraint. They were guided by a decision rule provided to them, but were still prone to errors under time pressure. An initial experiment estimated human performance model parameters, while a second experiment compared two referral policies. Results show statistically significant gains for the proposed optimal referral policy over a blind policy that determines referrals using the automation and human-performance models but not based on the observed data. Copyright © 2025, The Authors. All rights reserved.

关键词： Human engineering

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Efficient Path Planning and Tracking for Multi-Modal Legged-Aerial Locomotion Using Integrated Probabilistic Road Maps (PRM) and Reference Governors (RG)

Efficient Path Planning and Tracking for Multi-Modal Legged-...

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IEEE Conference on Decision and Control

作者： Eric Sihite Benjamin Mottis Paul Ghanem Alireza Ramezani Morteza Gharib Department of Aerospace California Institute of Technology Pasadena CA USA Department of Microtechnology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Department of Electrical and Computer Engineering SiliconSynapse Laboratory Northeastern University Boston MA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

There have been several successful implementations of bio-inspired legged robots that can trot, walk, and hop robustly even in the presence of significant unplanned disturbances. Despite all of these accomplishments, practical control and high-level decision-making algorithms in multimodal legged systems are overlooked. In nature, animals such as birds impressively showcase multiple modes of mobility including legged and aerial locomotion. They are capable of performing robust locomotion over large walls, tight spaces, and can recover from unpredictable situations such as sudden gusts or slippery surfaces. Inspired by these animals’ versatility and ability to combine legged and aerial mobility to negotiate their environment, our main goal is to design and control legged robots that integrate two completely different forms of locomotion, ground and aerial mobility, in a single platform. Our robot, the Husky Carbon, is being developed to integrate aerial and legged locomotion and to transform between legged and aerial mobility. This work utilizes a Reference Governor (RG) based on low-level control of Husky’s dynamical model to maintain the efficiency of legged locomotion, uses Probabilistic Road Maps (PRM) and 3D A ⋆ algorithms to generate an optimal path based on the energetic cost of transport for legged and aerial mobility.

关键词： Legged locomotion Solid modeling Three-dimensional displays Roads Heuristic algorithms Decision making Transforms

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