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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Mahmoud M. Mostafa Wael M. Abdel-Wahab Department of Electrical and Computer Engineering Centre for Intelligent Antenna and Radio Systems (CIARS) University of Waterloo Waterloo Canada C-COM Satellite Systems Inc. Ottawa Canada

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

In this paper, an accurate analytical model for predicting the active impedance variation with the scan angle of slot-phased array antennas (S-PAAs). is presented. The model is based on Floquet modes expansion of magnetic currents and its application to phased array slot antennas. The mutual coupling effect is taken into account by considering the variation of magnetic current with the scan angle. The simulation results calculated by proposed analytical model are presented and compared to those obtained by the numerical EM solver (HFSS). A very good agreement is observed.

关键词： Phased arrays Mutual coupling Analytical models Slot antennas Accuracy Simulation Conferences

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intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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Framework for Ship Trajectory Forecasting Based on Linear Stationary Models Using Automatic Identification System

Framework for Ship Trajectory Forecasting Based on Linear St...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Srivastava, Smita Kumar, Lovneet Jeyanthi, R. Deepa, K. Aggrawal, Vinay Department of Computer Science and Engineering India Department of Electronics and Communication Engineering India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Centre for Artificial Intelligence and Robotics Defense Research and Development Organization Karnataka Bangalore India

Maritime surveillance is of utmost priority for a nation's security, and hence it's economy. For maritime awareness, coastal surveillance, and maritime activities in the Region of Interest (ROI) should be monitored. One of the ways to keep this in check is to restrain unwanted infiltration. Monitoring unwanted infiltration is feasible through vessel trajectory forecasting and anomaly detection in real time. Most solutions for trajectory predictions are available but require a huge amount of historical data, and high-power computing resources. Here, the requirement is developing a decision support framework consisting of both lite weight approaches for short-term predictions and deep learning-based techniques for long-term forecasting. This paper aims to find the suitability of Linear Stationary Models (LSM) like the Auto-Regressive Integrated Moving Average Model (ARIMA) for predicting and forecasting the Vessel Trajectory as means of lite weight short-term predictions. For this purpose, the Automatic Identification System (AIS) dataset of the U.S. West Coast is used. The significant effort was for data pre-processing to create a robust dataset for model training. An appropriate model after the model-selection process is used for trajectory forecasting. The model's accuracy is validated using Root Mean Square Error (RMSE) performance indices for residual and forecast errors. A window generator model is integrated with the best-fitted ARIMA model for recursive real-time predictions, with varied sizes and visualization. The proposed time-series model provided a very high accuracy as the RMSE value for prediction and 48 hours forecast are 0.023 and 0.017, respectively. © 2023 The Authors. Published by Elsevier B.V.

关键词： Time series analysis

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Distance Protection Application for 33kV Traction Network

Distance Protection Application for 33kV Traction Network

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Luthando Nkalitshana Senthil Krishnamurthy Haltor Mataifa Department of Electrical Electronics and Computer Engineering Centre for Substation Automation and Energy Management Systems (CSAEMS) Center for Intelligent Systems and Emerging Technologies (CSIET) Cape Peninsula University of Technology Bellville South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The research investigates the laboratory-scale setup and functional testing of distance protection techniques for the Transnet traction system. The Transnet network, which powers South Africa's railway system, requires a strong protection mechanism to assure the safety and dependability of the electrical systems that power locomotives. Distance protection, essential in securing high-voltage transmission networks, is critical to detecting faults, isolating affected portions, and minimizing system interruptions. In this study, a simulation environment was created to simulate the features of the Transnet traction system, including traction substations, overhead power lines, and associated equipment. A lab-scale model was developed utilizing protection functional simulation to simulate real-time fault situations and evaluate the distance protection relay's response under different fault conditions. The performance and efficacy of the protection mechanism were assessed by varying key factors such as fault impedance, fault location, and fault types.

关键词： Rails Substations Simulation Reliability engineering Stability analysis Software Real-time systems Protection Load flow Testing

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Error-correcting Codes in Communication systems

Error-correcting Codes in Communication Systems

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Khawla Almazrouei Khawla A Alnajjar Department of Computer Engineering University of Sharjah Sharjah UAE Autonomous Robotics Research Centre Technology Innovation Institute Abu Dhabi UAE Department of Electrical Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In most communication systems, when transmitting or receiving a signal, some unwanted signals may get introduced into the communications, making the quality of the communication poor. This disturbance, referred to as noise, can affect the reliability and quality of a digital communication system by corrupting or getting a message scrambled whenever it is transmitted. To prevent these errors, error-correction codes help detect and correct these errors arising during data transmission within digital communication systems. In communication systems, such error-correcting codes are essential for ensuring the efficient and reliable exchange of information over noisy channels like satellite links, optical fibers, and wireless networks. As such, the work presented in this paper delves into the issue of noisy data in communication systems and how error-correcting codes can be used to solve the problem. To achieve this, a review of different relevant literature by various scholars is done to present authentic and verifiable data on error-correcting codes for communication systems. On top of that, this paper introduces a novel approach by integrating advanced machine learning techniques, particularly artificial neural networks, to enhance the effectiveness and adaptability of error-correcting codes in diverse and dynamically changing communication environments, significantly surpassing traditional error correction methods.

关键词： Satellites Reviews Wireless networks Noise Machine learning Digital communication Error correction codes

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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Asilomar Conference on Signals, systems & computers

作者： Muhammad Moinuddin Azzedine Zerguine Ubaid M. Al-Saggaf Muhammad Arif Electrical and Computer Engineering Department and the Centre of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah Saudi Arabia Electrical Engineering Department and the Interdisciplinary Research Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt and the Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm.

关键词： Filtering Two-dimensional displays Simulation Cost function Calculus Steady-state Performance analysis Transient analysis Standards Convergence

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiang, Senwei Ye, Minxiang Zhu, Shiqiang Gu, Jason Xie, Anhuan Men, Zehua Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou310000 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers;an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude © 2022 IEEE.

关键词： Aircraft landing

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Real-time Architecture for Audio-Visual Active Speaker Detection

Real-time Architecture for Audio-Visual Active Speaker Detec...

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IEEE International Conference on robotics and Biomimetics

作者： Min Huang Wen Wang Zheyuan Lin Fiseha B. Tesema Shanshan Ji Jason Gu Minhong Wan Wei Song Te Li Shiqiang Zhu Intelligent Robotics Research Center Zhejiang Lab and Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical And Computer Engineering Dalhousie University Canada

ISBN: (纸本)9781665481106

Continuously measuring the speaking state of users with robot in a human-robot Interaction(HRI) system improves metrics of interaction quality. Meanwhile, mainstream active speaker detection (ASD) algorithms emphasize achieving high AUCs at frame level in the AVA-Active Speaker dataset and pay less attention to get real-time performance in robotic systems. In this paper, we propose a model named FSDNet to keep a high AUC score in the AVA-Active Speaker dataset while reducing time cost, our model increase AUC score by 0.1% compared with the State-Of-The-Art and need only 75% running time. Furthermore, we put forward an architecture with a time-related prediction function to make our algorithm more effective and generative in interactive robotic systems. The code is released at https://***/huangmin9966/FSDNet_RealTimeArch.

关键词： Measurement Visualization Costs Codes Biological system modeling Transformers Feature extraction

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