The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Victor Sreeram - One of the best experts on this subject based on the ideXlab platform.
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relationship between Schur Decomposition based frequency weighted balanced truncation techniques
International Conference on Mechatronics and Control, 2014Co-Authors: Victor SreeramAbstract:In this paper, we present some remarks on frequency weighted balanced truncation technique based on Schur-Decomposition idea. In particular we show that two frequency weighted balanced truncation techniques [7], [9] based on Schur-Decomposition idea are special cases of a more general technique introduced recently in [10], [11].
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New results of frequency weighted balanced truncation technique on Schur Decomposition
2011 6th IEEE Conference on Industrial Electronics and Applications, 2011Co-Authors: Shafishuhaza Sahlan, Ruzairi Abdul Rahim, Victor SreeramAbstract:In this paper, we present some new results on frequency weighted balanced technique based on partial fraction expansion idea in discrete-time system. The reduced order models obtained are guaranteed to be stable even for double sided weightings by direct truncation. A simple and easily computable a priori error bound is derived. Numerical example shows the effectiveness of the proposed method.
Josef A. Nossek - One of the best experts on this subject based on the ideXlab platform.
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simultaneous Schur Decomposition of several nonsymmetric matrices to achieve automatic pairing in multidimensional harmonic retrieval problems
IEEE Transactions on Signal Processing, 1998Co-Authors: Martin Haardt, Josef A. NossekAbstract:This paper presents a new Jacobi-type method to calculate a simultaneous Schur Decomposition (SSD) of several real-valued, nonsymmetric matrices by minimizing an appropriate cost function. Thereby, the SSD reveals the "average eigenstructure" of these nonsymmetric matrices. This enables an R-dimensional extension of Unitary ESPRIT to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. Unitary ESPRIT is an ESPRIT-type high-resolution frequency estimation technique that is formulated in terms of real-valued computations throughout. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real-valued matrix. The SSD jointly estimates the eigenvalues of all R matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure that neither requires any search nor any other heuristic pairing strategy. Moreover, we describe how R-dimensional harmonic retrieval problems (with R/spl ges/3) occur in array signal processing and model-based object recognition applications.
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Simultaneous Schur Decomposition of several matrices to achieve automatic pairing in multidimensional harmonic retrieval problems
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996Co-Authors: Martin Haardt, Knut Hüper, John B. Moore, Josef A. NossekAbstract:This paper presents a new Jacobi-type method to calculate a simultaneous Schur Decomposition (SSD) of several real-valued, non-symmetric matrices by minimizing an appropriate cost function. Thereby, the SSD reveals the “average eigenstructure” of these non-symmetric matrices. This enables an R-dimensional extension of Unitary ESPRIT to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. Unitary ESPRIT is an ESPRIT-type high-resolution frequency estimation technique that is formulated in terms of real-valued computations throughout. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real-valued matrix. The SSD jointly estimates the eigenvalues of all R matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure, that neither requires any search nor any other heuristic pairing strategy. Finally, we show how R-dimensional harmonic retrieval problems (with R ≥ 3) occur in array signal processing and model-based object recognition applications.
Takayuki Matsuno - One of the best experts on this subject based on the ideXlab platform.
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vibration damping in manipulation of deformable linear objects using sliding mode control
Advanced Robotics, 2014Co-Authors: Feng Ding, Takayuki Matsuno, Yongji Wang, Jian Huang, Toshio FukudaAbstract:In this paper, we proposed a position-based control strategy for eliminating the vibration at the end of deformable linear objects (DLOs) during its manipulation. Using Schur Decomposition of matrices and linear transform of variables, actuated and underactuated parts of the DLO dynamic model are separated. Based on the decoupled dynamic model of a DLO system, a sliding mode control with exponential approach law is designed to force the state variables to converge to an equilibrium and to allow vibration at the end of the DLO to be damped quickly. The DLO system, subjected to control input saturation, is further studied to solve the input saturation problem. An adaptive sliding mode control law is designed to suppress the damping at the end of the DLO. Proposed control strategies are verified by numerical simulations. The simulation results show that proposed methods can effectively damp the vibration at the end of the DLO.
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adaptive sliding mode control for manipulating deformable linear object with input saturation
International Conference on Mechatronics and Automation, 2012Co-Authors: Feng Ding, Toshio Fukuda, Yongji Wang, Jian Huang, Takayuki MatsunoAbstract:Manipulating deformable linear objects (DLOs) is much challengeable as the uncertainty resulting from oscillation of DLOs may cause failure in the operation. In this paper, we proposed a position-based control strategy to eliminate the vibration at the end of DLOs. The simplified dynamic model of a DLO is obtained by local linearization, Schur Decomposition of matrices and linear transform of variable. Based on the dynamic model, an adaptive law is applied to estimate the uncertain parameters, and a sliding mode controller considering the position constraint condition is designed to force the state variables converging to the equilibrium. The effectiveness of proposed control strategies are verified by numerical simulations
Junxiang Wang - One of the best experts on this subject based on the ideXlab platform.
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Schur Decomposition based robust watermarking algorithm in contourlet domain
International Conference on Cloud Computing, 2016Co-Authors: Junxiang Wang, Ying LiuAbstract:Most of the existing watermarking schemes utilized SVD Decomposition to embed watermark, which lead to a high computational complexity and incidental the false positive detection problem. Therefore, this paper provides a robust copyright protection scheme based on a simple Decomposition scheme (Schur Decomposition) and Quantization Index Modulation (QIM) in Contourlet domain. In addition, some stable features are acquired by using Schur Decomposition in the Contourlet domain. Consequently, the watermark is embedded into those stable features with QIM method. Experimental results show that the proposed scheme has some superiorities in terms of robustness and imperceptibility, which could against most common attacks such as JPEG compression, filtering, cropping, noise adding and so on.
Martin Haardt - One of the best experts on this subject based on the ideXlab platform.
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simultaneous Schur Decomposition of several nonsymmetric matrices to achieve automatic pairing in multidimensional harmonic retrieval problems
IEEE Transactions on Signal Processing, 1998Co-Authors: Martin Haardt, Josef A. NossekAbstract:This paper presents a new Jacobi-type method to calculate a simultaneous Schur Decomposition (SSD) of several real-valued, nonsymmetric matrices by minimizing an appropriate cost function. Thereby, the SSD reveals the "average eigenstructure" of these nonsymmetric matrices. This enables an R-dimensional extension of Unitary ESPRIT to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. Unitary ESPRIT is an ESPRIT-type high-resolution frequency estimation technique that is formulated in terms of real-valued computations throughout. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real-valued matrix. The SSD jointly estimates the eigenvalues of all R matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure that neither requires any search nor any other heuristic pairing strategy. Moreover, we describe how R-dimensional harmonic retrieval problems (with R/spl ges/3) occur in array signal processing and model-based object recognition applications.
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Simultaneous Schur Decomposition of several matrices to achieve automatic pairing in multidimensional harmonic retrieval problems
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996Co-Authors: Martin Haardt, Knut Hüper, John B. Moore, Josef A. NossekAbstract:This paper presents a new Jacobi-type method to calculate a simultaneous Schur Decomposition (SSD) of several real-valued, non-symmetric matrices by minimizing an appropriate cost function. Thereby, the SSD reveals the “average eigenstructure” of these non-symmetric matrices. This enables an R-dimensional extension of Unitary ESPRIT to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. Unitary ESPRIT is an ESPRIT-type high-resolution frequency estimation technique that is formulated in terms of real-valued computations throughout. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real-valued matrix. The SSD jointly estimates the eigenvalues of all R matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure, that neither requires any search nor any other heuristic pairing strategy. Finally, we show how R-dimensional harmonic retrieval problems (with R ≥ 3) occur in array signal processing and model-based object recognition applications.
