Catheterization instruments are increasingly being improved to accurately diagnose and treat cardiovascular conditions. However, current catheter systems provide limited information about the shape of the catheter and...
Catheterization instruments are increasingly being improved to accurately diagnose and treat cardiovascular conditions. However, current catheter systems provide limited information about the shape of the catheter and tissue-instrument interaction forces during an intervention. Furthermore, relying on inconsistent feedback of such interaction forces during an intervention may result in tissue injury. This paper presents the first steps to estimate the interaction forces between a catheter and a mock-up arterial environment. We base the proposed method on a Pseudo-Rigid Body approximation of the catheter and integrate three-dimensional shape information provided by Fiber Bragg Grating sensors inside the catheter. The reconstructed forces along the catheter body can be fed back to the surgeon in visual and/or haptic form. In this work, the estimated forces are displayed in real-time in a graphical user interface with the reconstructed catheter shape. Experimental validation demonstrates a root mean square error of 0.03 N and a mean reconstruction error of 0.02 N.
Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry...
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This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently...
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作者：
Mohamed I. El-HawwaryDepartment of Electrical Power Engineering
Faculty of Engineering Cairo University Giza 12613 Egypt Division of Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden
In this paper the flying convex-path-following formations problem (FCxPFF) is solved for two cases of underactuated rigid bodies. In the first case the the rigid bodies have a single degree of underactuation with two ...
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In this paper the flying convex-path-following formations problem (FCxPFF) is solved for two cases of underactuated rigid bodies. In the first case the the rigid bodies have a single degree of underactuation with two thrusts and three torques. In the second, they have two degrees of underactuation with a single thrust. The solution builds on the one developed for fully-actuated agents in Part I of the paper. In addition, the way the solution is tailored for underactuation relies on further utilization of hierarchic set stabilization, and reduction. Additional remarks on the benefits of the approach, and simulation results of the proposed solutions are presented.
The characteristics of human emergency behaviour under the emergency are a crucial scientific issue in basic emergency management research. The analysis of time dynamic aspects of human behaviour based on electronic f...
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This paper discusses the output feedback control problem of the active magnetic bearing(AMB) suspension system in absence of full-state feedback. Firstly, an exponential convergence state observer is designed to estim...
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This paper discusses the output feedback control problem of the active magnetic bearing(AMB) suspension system in absence of full-state feedback. Firstly, an exponential convergence state observer is designed to estimate the state variables only depending on output feedback. Secondly, the adaptive command filtered backstepping approach is adopted to facilitate the controller design which can avoid the complexity for calculating the analytic derivatives of the virtual control inherent in standard backstepping. Then, the compensation dynamic for filtered errors of virtual control signals is designed according to Lyapunov theory and the stability analysis shows that the compensated tracking errors can be proven asymptotic convergence. Finally, the simulation results are provided to illustrate the effectiveness of the proposed method.
Two-line graphs of a given partial Latin rectangle are introduced as vertex-and-edge-coloured bipartite graphs that give rise to new autotopism invariants. They reduce the complexity of any currently known method for ...
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In this paper, we present an output feedback based design of event-Triggered sliding mode control for delta operator systems. For discretetime systems, multi-rate output sampling based state estimation technique is ve...
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Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this pape...
Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning model is proposed, which can identify drivers from their driving behaviors based on vehicle telematics data. The proposed Long-Short-Term-Memory (LSTM) model predicts the identity of the driver based on the individual's unique driving patterns learned from the vehicle telematics data. Given the telematics is time-series data, the problem is formulated as a time series prediction task to exploit the embedded sequential information. The performance of the proposed approach is evaluated on three naturalistic driving datasets, which gives high accuracy prediction results. The robustness of the model on noisy and anomalous data that is usually caused by sensor defects or environmental factors is also investigated. Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noise-induced in the data.
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