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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Seyed Hossein Hosseini Jack Greer Fariborz Lohrabi Pour Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Virginia Tech Blacksburg Virginia USA

This paper presents a powerline energy harvesting circuit to power wireless sensor nodes for powerline safety monitoring. The magnetic harvester is a ring-shaped nano-crystalline magnetic core, which transforms the ac powerline current to ac voltage. The major building blocks of the circuit are a buck-boost converter operating in discontinuous conduction mode (DCM) and a microcontroller unit (MCU) for maximum power point tracking (MPPT). The MPPT algorithm based on the perturb and observe senses the current flowing into the load and adjusts the duty cycle of the buck-boost converter to match the source impedance. The magnetic core delivers 6.98 W to an optimal $200\ \Omega$ resistor directly attached to the core under the powerline current of 30 A. The output power of the proposed circuit is 4.86 W with the optimal load resistance of $R_{L}=250\ \Omega$ , resulting in the conversion efficiency of 70%.

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Wireless Sensor Node System to Monitor Pig Activities for Behavior Classification

Wireless Sensor Node System to Monitor Pig Activities for Be...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： Brandon Cheung Yuezhong Xu Dong Sam Ha Bradley Department of Electrical and Computer Engineering Virginia Tech Multifunctional Integrated Circuits and Systems (MICS) Group Blacksburg Virginia USA

Wireless sensor nodes (WSNs) are useful to monitor animals remotely and continuously. The proposed WSN aims to monitor pig activities, and it consists of a 3-axis accelerometer, a 3-axis gyroscope, and a microcontroller with embedded BLE (Bluetooth Low Energy) radio. The WSN was designed and prototyped with a custom PCB and used to collect data from pigs in field for about 131 hours, and the collected data was processed to classify pig behaviors with machine learning models. The sampling rate of the sensors is 10 samples per second. The proposed WSN dissipates 6.29 mW, on average, and the peak power dissipation is 41.01 mW during transmission of the sensed data. The WSN is estimated to operate for about three weeks with a coin cell battery CR2477.

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2.4 GHz Ultra-Low Power Direct Digital-to-RF CMOS Transmitter for Biosensing Applications

2.4 GHz Ultra-Low Power Direct Digital-to-RF CMOS Transmitte...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Fariborz Lohrabi Pour Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Virginia Tech Blacksburg Virginia USA

This paper presents a low-power and low-complexity direct digital-to-RF transmitter architecture, suitable for biosensing applications. The RF front end of the transmitter is based on a ring oscillator, whose output phase is modulated through the charge-to-phase mechanism using a charge injection block. Hence, the phase shift keying (PSK) modulation can be performed directly in the RF domain. Post-layout simulation results show that the transmitter is able to collect, process, and transmit sensed data with the maximum data rate of 20 Mbps and an error vector magnitude (EVM) of smaller than 3.5%, while dissipating the DC power smaller than 0.5 mW. The results demonstrate that the proposed transmitter architecture is effective for wireless biosensing applications.

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Direct Learning for Parameter-Varying Feedforward control: A Neural-Network Approach

Direct Learning for Parameter-Varying Feedforward Control: A...

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IEEE Conference on Decision and control

作者： Johan Kon Jeroen van de Wijdeven Dennis Bruijnen Roland Tóth Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft The Netherlands

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.

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Scaling Distributed Collaboration in Mixed Reality

Scaling Distributed Collaboration in Mixed Reality

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Genay, Adélaïde Syiem, Brandon Victor Wong, Emily Feuchtner, Tiare Knibbe, Jarrod Grønbæk, Jens Emil Sloth Velloso, Eduardo School of Computer Science University of Sydney Sydney NSW Australia School of Computing and Information Systems The University of Melbourne Melbourne VIC Australia HCI Group University of Konstanz Konstanz Germany Department of Computer Science Aarhus University Aarhus Denmark School of Electrical Engineering and Computer Science The University of Queensland St. Lucia QLD Australia

ISBN: (纸本)9798400713958

Distributed collaboration in Mixed Reality (MR) promises to revolutionise how people connect across different physical environments, offering experiences akin to face-to-face interactions. However, previous work has mostly focused on enabling this vision in overly simplified settings such as with only two users interacting in identical distributed environments. Scaling current systems to work with large groups and for common real-life scenarios is a persistent challenge that requires addressing multiple tensions. We identified six challenges: 1) supporting locally congruent actions from heterogeneous remote spaces, 2) communicating accurate user behaviours through virtual representation instead of physical bodies, 3) facilitating organic group interactions within limited physical space, 4) maintaining conversational dynamics even in asynchronous exchanges, 5) providing equal access to physical objects for all participants, and 6) enabling efficient task switching within a complex ecology of applications, devices, and accessibility needs. This workshop aims to gather researchers and practitioners to explore actionable strategies for resolving these challenges. Through a mix of presentations, hands-on activities, and group discussions, participants will generate new ideas and develop a research agenda to articulate the future of MR collaboration systems. The workshop outcomes will include a list of concrete next steps for the community to bring distributed MR collaboration at scale. © 2025 Copyright held by the owner/author(s).

关键词： Collaboration Distributed systems Human-computer Interaction Mixed Reality

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Direct Learning for Parameter-Varying Feedforward control: A Neural-Network Approach

arXiv

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arXiv 2023年

作者： Kon, Johan van de Wijdeven, Jeroen Bruijnen, Dennis Tóth, Roland Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands Philips Engineering Solutions Eindhoven Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft Netherlands

关键词： Learning systems

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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