The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jingxin Zhang - One of the best experts on this subject based on the ideXlab platform.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
Signal Processing, 2020Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Abstract This paper presents theoretical results on the uncertainty principle and the sparse reconstruction of Rational transfer Functions in a dictionary of two orthonormal Rational Function (ORF) bases. The uncertainty principle concerning pairs of representations of Rational transfer Functions in different ORF bases is established. It is shown that a Rational transfer Function cannot have a sparse representation simultaneously in two different mutually incoherent ORF bases. The uniqueness for the sparse representation is derived as a direct consequence of this uncertainty principle. A reconstruction method for a Rational transfer Function in a pair of ORF bases is proposed. The sparse reconstruction result shows that, given a Rational transfer Function with a sufficiently sparse representation in a given dictionary of two ORF bases, the sparse representation can be recovered by solving a linear programming problem. A lower bound is provided on the number of frequency response measurements required to recover the sparse representation with high probability.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
arXiv: Signal Processing, 2017Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Most Rational systems can be described in terms of orthonormal basis Functions. This paper considers the reconstruction of a sparse coefficient vector for a Rational transfer Function under a pair of orthonormal Rational Function bases and from a limited number of linear frequency-domain measurements. We prove the uncertainty principle concerning pairs of compressible representation of orthonormal Rational Functions in the infinite dimensional Function space. The uniqueness of compressible representation using such pairs is provided as a direct consequence of uncertainty principle. The bound of the number of measurements which guarantees the replacement of 1_0 optimization searching for the unique sparse reconstruction by 1_1 optimization using random sampling on the unit circle with high probability is provided as well.
Dan Xiong - One of the best experts on this subject based on the ideXlab platform.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
Signal Processing, 2020Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Abstract This paper presents theoretical results on the uncertainty principle and the sparse reconstruction of Rational transfer Functions in a dictionary of two orthonormal Rational Function (ORF) bases. The uncertainty principle concerning pairs of representations of Rational transfer Functions in different ORF bases is established. It is shown that a Rational transfer Function cannot have a sparse representation simultaneously in two different mutually incoherent ORF bases. The uniqueness for the sparse representation is derived as a direct consequence of this uncertainty principle. A reconstruction method for a Rational transfer Function in a pair of ORF bases is proposed. The sparse reconstruction result shows that, given a Rational transfer Function with a sufficiently sparse representation in a given dictionary of two ORF bases, the sparse representation can be recovered by solving a linear programming problem. A lower bound is provided on the number of frequency response measurements required to recover the sparse representation with high probability.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
arXiv: Signal Processing, 2017Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Most Rational systems can be described in terms of orthonormal basis Functions. This paper considers the reconstruction of a sparse coefficient vector for a Rational transfer Function under a pair of orthonormal Rational Function bases and from a limited number of linear frequency-domain measurements. We prove the uncertainty principle concerning pairs of compressible representation of orthonormal Rational Functions in the infinite dimensional Function space. The uniqueness of compressible representation using such pairs is provided as a direct consequence of uncertainty principle. The bound of the number of measurements which guarantees the replacement of 1_0 optimization searching for the unique sparse reconstruction by 1_1 optimization using random sampling on the unit circle with high probability is provided as well.
Li Chai - One of the best experts on this subject based on the ideXlab platform.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
Signal Processing, 2020Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Abstract This paper presents theoretical results on the uncertainty principle and the sparse reconstruction of Rational transfer Functions in a dictionary of two orthonormal Rational Function (ORF) bases. The uncertainty principle concerning pairs of representations of Rational transfer Functions in different ORF bases is established. It is shown that a Rational transfer Function cannot have a sparse representation simultaneously in two different mutually incoherent ORF bases. The uniqueness for the sparse representation is derived as a direct consequence of this uncertainty principle. A reconstruction method for a Rational transfer Function in a pair of ORF bases is proposed. The sparse reconstruction result shows that, given a Rational transfer Function with a sufficiently sparse representation in a given dictionary of two ORF bases, the sparse representation can be recovered by solving a linear programming problem. A lower bound is provided on the number of frequency response measurements required to recover the sparse representation with high probability.
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uncertainty principle and sparse reconstruction in pairs of orthonormal Rational Function bases
arXiv: Signal Processing, 2017Co-Authors: Dan Xiong, Li Chai, Jingxin ZhangAbstract:Most Rational systems can be described in terms of orthonormal basis Functions. This paper considers the reconstruction of a sparse coefficient vector for a Rational transfer Function under a pair of orthonormal Rational Function bases and from a limited number of linear frequency-domain measurements. We prove the uncertainty principle concerning pairs of compressible representation of orthonormal Rational Functions in the infinite dimensional Function space. The uniqueness of compressible representation using such pairs is provided as a direct consequence of uncertainty principle. The bound of the number of measurements which guarantees the replacement of 1_0 optimization searching for the unique sparse reconstruction by 1_1 optimization using random sampling on the unit circle with high probability is provided as well.
L.m. Silveira - One of the best experts on this subject based on the ideXlab platform.
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Robust Rational Function approximation algorithm for model generation
Proceedings 1999 Design Automation Conference (Cat. No. 99CH36361), 1999Co-Authors: C.p. Coelho, J.r. Phillips, L.m. SilveiraAbstract:The problem of computing Rational Function approximations to tabulated frequency data is of paramount importance in the modeling arena. In this paper, we present a method for generating a state space model from tabular data in the frequency domain that solves some of the numerical difficulties associated with the traditional fitting techniques used in linear least squares approximations. An extension to the MIMO case is also derived.
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an efficient approach to transmission line simulation using measured or tabulated s parameter data
Design Automation Conference, 1994Co-Authors: L.m. Silveira, Jacob K White, Ibrahim M Elfadel, M Chilukuri, Ken KundertAbstract:In this paper we describe an algorithm for efficient circuit-level simulation of transmission lines which can be specified by tables of frequency-dependent scattering parameters. The approach uses a forced stable section-by-section l2 minimization approach to construct a high order Rational Function approximation to the frequency domain data, and then applies guaranteed stable balanced realization techniques to reduce the order of the Rational Function. The Rational Function is then incorporated in a circuit simulator using fast recursive convolution. An example of a transmission line with skin-effect is examined to both demonstrate the effectiveness of the approach and to show its generality.
Xu Huang - One of the best experts on this subject based on the ideXlab platform.
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a new approach on optimization of the Rational Function model of high resolution satellite imagery
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Yongjun Zhang, Lei Wang, Xu HuangAbstract:Overparameterization is one of the major problems that the Rational Function model (RFM) faces. A new approach of RFM parameter optimization is proposed in this paper. The proposed RFM parameter optimization method can resolve the ill-posed problem by removing all of the unnecessary parameters based on scatter matrix and elimination transformation strategies. The performances of conventional ridge estimation and the proposed method are evaluated with control and check grids generated from Satellites d'observation de la Terre (SPOT-5) high-resolution satellite data. Experimental results show that the precision of the proposed method, with about 35 essential parameters, is 10% to 20% higher than that of the conventional model with all 78 parameters. Moreover, the ill-posed problem is effectively alleviated by the proposed method, and thus, the stability of the estimated parameters is significantly improved.
