The Experts below are selected from a list of 3765 Experts worldwide ranked by ideXlab platform
Xinghuo Yu - One of the best experts on this subject based on the ideXlab platform.
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ACC - One Step Prediction-based packet dropout compensation for networked control systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Yu-long Wang, Xinghuo YuAbstract:This paper studies the problem of network-induced delay and packet dropout compensation for continuous-time networked control systems (NCSs). By proposing the one Step Prediction-based packet dropout compensation method, new model for NCSs with packet dropout and network-induced long delay is presented. Then, a packet dropout compensation threshold time based Lyapunov functional is proposed, and H ∞ controller design method is presented. Even for NCSs without packet dropout compensation, the obtained result is still less conservative than the existing ones. This paper proves also that some existing results can be improved by using the convex analysis method. Numerical examples are given to illustrate the merits and effectiveness of the proposed methods.
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One Step Prediction-based packet dropout compensation for networked control systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Yu-long Wang, Xinghuo YuAbstract:This paper studies the problem of network induced delay and packet dropout compensation for continuous time networked control systems (NCSs). By proposing the one Step Prediction-based packet dropout compensation method, new model for NCSs with packet dropout and network-induced long delay is presented. Then, a packet dropout compensation threshold time based Lyapunov functional is proposed, and H∞ controller design method is presented. Even for NCSs without packet dropout compensation, the obtained result is still less conservative than the existing ones. This paper proves also that some existing results can be improved by using the convex analysis method. Numerical examples are given to illustrate the merits and effectiveness of the proposed methods.
Yu-long Wang - One of the best experts on this subject based on the ideXlab platform.
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ACC - One Step Prediction-based packet dropout compensation for networked control systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Yu-long Wang, Xinghuo YuAbstract:This paper studies the problem of network-induced delay and packet dropout compensation for continuous-time networked control systems (NCSs). By proposing the one Step Prediction-based packet dropout compensation method, new model for NCSs with packet dropout and network-induced long delay is presented. Then, a packet dropout compensation threshold time based Lyapunov functional is proposed, and H ∞ controller design method is presented. Even for NCSs without packet dropout compensation, the obtained result is still less conservative than the existing ones. This paper proves also that some existing results can be improved by using the convex analysis method. Numerical examples are given to illustrate the merits and effectiveness of the proposed methods.
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One Step Prediction-based packet dropout compensation for networked control systems
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Yu-long Wang, Xinghuo YuAbstract:This paper studies the problem of network induced delay and packet dropout compensation for continuous time networked control systems (NCSs). By proposing the one Step Prediction-based packet dropout compensation method, new model for NCSs with packet dropout and network-induced long delay is presented. Then, a packet dropout compensation threshold time based Lyapunov functional is proposed, and H∞ controller design method is presented. Even for NCSs without packet dropout compensation, the obtained result is still less conservative than the existing ones. This paper proves also that some existing results can be improved by using the convex analysis method. Numerical examples are given to illustrate the merits and effectiveness of the proposed methods.
V. Koivunen - One of the best experts on this subject based on the ideXlab platform.
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ISCAS (3) - Error analysis of a multi-Step Prediction based blind equalizer
ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 1999Co-Authors: J. Mannerkoski, V. KoivunenAbstract:Blind equalization is used to compensate the intersymbol interference caused by a communication channel without using any known training sequences. For many practical channel types, a suitable delay at the output of the equalizer allows for achieving a small estimation error. Blind equalizers may be implemented with linear Prediction-error filters (PEF), but the delay cannot be controlled with one-Step predictors. Consequently, multi-Step Prediction has been suggested as a solution to the problem. We consider the effects of additive noise at the output of the multiStep PEF. An analytical error bound for a PEF-based blind zero-forcing (ZF) equalizer in the presence of noise is derived. The obtained results are verified with simulations.
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Error analysis of a multi-Step Prediction based blind equalizer
1999 IEEE International Symposium on Circuits and Systems (ISCAS), 1999Co-Authors: J. Mannerkoski, V. KoivunenAbstract:Blind equalization is used to compensate the intersymbol interference caused by a communication channel without using any known training sequences. For many practical channel types, a suitable delay at the output of the equalizer allows for achieving a small estimation error. Blind equalizers may be implemented with linear Prediction-error filters (PEF), but the delay cannot be controlled with one-Step predictors. Consequently, multi-Step Prediction has been suggested as a solution to the problem. We consider the effects of additive noise at the output of the multiStep PEF. An analytical error bound for a PEF-based blind zero-forcing (ZF) equalizer in the presence of noise is derived. The obtained results are verified with simulations.
K. C. Veluvolu - One of the best experts on this subject based on the ideXlab platform.
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Multi-Step Prediction of Physiological Tremor With Random Quaternion Neurons for Surgical Robotics Applications
IEEE Access, 2018Co-Authors: Yubo Wang, S. Tatinati, Kabita Adhikari, Liyu Huang, Kianoush Nazarpour, K. C. VeluvoluAbstract:Digital filters are employed in hand-held robotic instruments to separate the concomitant involuntary physiological tremor motion from the desired motion of micro-surgeons. Inherent phase-lag in digital filters induces phase distortion (time-lag/delay) into the separated tremor motion and it adversely affects the final tremor compensation. Owing to the necessity of digital filters in hand-held instruments, multi-Step Prediction of physiological tremor motion is proposed as a solution to counter the induced delay. In this paper, a quaternion variant for extreme learning machines (QELMs) is developed for multi-Step Prediction of the tremor motion. The learning paradigm of the QELM integrates the identified underlying relationship from 3-D tremor motion in the Hermitian space with the fast learning merits of ELMs theories to predict the tremor motion for a known horizon. Real tremor data acquired from micro-surgeons and novice subjects are employed to validate the QELM for various Prediction horizons in-line with the delay induced by the order of digital filters. Prediction inferences underpin that the QELM method elegantly learns the cross-dimensional coupling of the tremor motion with random quaternion neurons and hence obtained significant improvement in Prediction performance at all Prediction horizons compared with existing methods.
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Multi-Step Prediction of physiological tremor for robotics applications
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: K. C. Veluvolu, S. Tatinati, S. M. HongAbstract:The performance of surgical robotic devices in real-time mainly depends on phase-delay in sensors and filtering process. A phase delay of 16-20 ms is unavoidable in these robotics procedures due to the presence of hardware low pass filter in sensors and pre-filtering required in later stages of cancellation. To overcome this phase delay, we employ multi-Step Prediction with band limited multiple Fourier linear combiner (BMFLC) and Autoregressive (AR) methods. Results show that the overall accuracy is improved by 60% for tremor estimation compared to single-Step Prediction methods in the presence of phase delay. Experimental results with the proposed methods for 1-DOF tremor estimation highlight the improvement.
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Online LS-SVM based multi-Step Prediction of physiological tremor for surgical robotics
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: S. Tatinati, Yubo Wang, G. Shafiq, K. C. VeluvoluAbstract:Performance of robotics based hand-held surgical devices in real-time is mainly dependent on accurate filtering of physiological tremor. The presence of phase delay in sensors (hardware) and filtering (software) processes affects the cancellation accuracy. This paper focuses on developing an estimation algorithm to improve the estimation accuracy in the presence of phase delay for real-time implementations. Moving window based online training approach for least squares-support vector machines (LSSVM) is employed in this paper for tremor estimation. A study is conducted with tremor data recorded from the subjects to analyze the suitability of proposed approach for both single-Step and multi-Step Prediction.
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EMBC - Multi-Step Prediction of physiological tremor for robotics applications
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2013Co-Authors: K. C. Veluvolu, S. Tatinati, S. M. HongAbstract:The performance of surgical robotic devices in real-time mainly depends on phase-delay in sensors and filtering process. A phase delay of 16-20 ms is unavoidable in these robotics procedures due to the presence of hardware low pass filter in sensors and pre-filtering required in later stages of cancellation. To overcome this phase delay, we employ multi-Step Prediction with band limited multiple Fourier linear combiner (BMFLC) and Autoregressive (AR) methods. Results show that the overall accuracy is improved by 60% for tremor estimation compared to single-Step Prediction methods in the presence of phase delay. Experimental results with the proposed methods for 1-DOF tremor estimation highlight the improvement.
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EMBC - Online LS-SVM based multi-Step Prediction of physiological tremor for surgical robotics
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2013Co-Authors: S. Tatinati, Yubo Wang, G. Shafiq, K. C. VeluvoluAbstract:Performance of robotics based hand-held surgical devices in real-time is mainly dependent on accurate filtering of physiological tremor. The presence of phase delay in sensors (hardware) and filtering (software) processes affects the cancellation accuracy. This paper focuses on developing an estimation algorithm to improve the estimation accuracy in the presence of phase delay for real-time implementations. Moving window based online training approach for least squares-support vector machines (LSSVM) is employed in this paper for tremor estimation. A study is conducted with tremor data recorded from the subjects to analyze the suitability of proposed approach for both single-Step and multi-Step Prediction.
Luigi Piroddi - One of the best experts on this subject based on the ideXlab platform.
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CDC - Some convergence properties of multi-Step Prediction error identification criteria
2008 47th IEEE Conference on Decision and Control, 2008Co-Authors: Marcello Farina, Luigi PiroddiAbstract:Multi-Step Prediction error identification methods are preferred over plain one-Step ahead Prediction error ones in application contexts (e.g., predictive control) where model accuracy is required over a wide horizon. For sufficiently high Prediction horizons, their properties can be shown to be conveniently related to those of output error methods, for which several important issues (e.g., uniqueness of estimation, robustness with respect to the noise model) have been characterized in the literature. The convergence properties of such criteria with respect to the Prediction horizon are analyzed.
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Some convergence properties of multi-Step Prediction error identification criteria
2008 47th IEEE Conference on Decision and Control, 2008Co-Authors: Marcello Farina, Luigi PiroddiAbstract:Multi-Step Prediction error identification methods are preferred over plain one-Step ahead Prediction error ones in application contexts (e.g., predictive control) where model accuracy is required over a wide horizon. For sufficiently high Prediction horizons, their properties can be shown to be conveniently related to those of output error methods, for which several important issues (e.g., uniqueness of estimation, robustness with respect to the noise model) have been characterized in the literature. The convergence properties of such criteria with respect to the Prediction horizon are analyzed.
