Chii-Horng Chen

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Cumulant-based inverse filter criteria (IFC) using secondand higher order statistics (HOS) proposed by Tugnait et al. have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals(More)
Higher order statistics-based inverse filter criteria (IFC) have been effectively used for blind equalization of single-input multiple-output (SIMO) systems. Recently, Chi and Chen reported a relationship between the unknown SIMO system and the optimum equalizer designed by the IFC for finite signal-to-noise ratio (SNR). In this paper, based on this(More)
Tugnait and Chi and Chen proposed multi-input multi-output inverse filter criteria (MIMO-IFC) using higher order statistics for blind deconvolution of MIMO linear time-invariant systems. This paper proposes three properties on the performance of the MIMO linear equalizer associated with MIMO-IFC for any signal-to-noise ratio, including P1) perfect phase(More)
In this paper, Shalvi and Weinstein’s super-exponential (SE) algorithm using higher order statistics for blind deconvolution of one-dimensional (1-D) linear time-invariant systems is extended to a two-dimensional (2-D) SE algorithm. Then, a 2-D frequency-domain blind system identification (BSI) algorithm for 2-D linear shift-invariant (LSI) systems using(More)
In this paper, Chi’s real one-dimensional (1-D) parametric nonminimum-phase Fourier series-based model (FSBM) is extended to two-dimensional (2-D) FSBM for a 2-D nonminimumphase linear shift-invariant system by using finite 2-D Fourier series approximations to its amplitude response and phase response, respectively. The proposed 2-D FSBM is guaranteed(More)
In this paper, Shalvi and Weinstein's 1-dimensional (1-D) computationally efficient super-exponential (SE) algorithm for blind deconvolution is extended to 2-dimensional (2-D) SE algorithm. Then a noiseinsensitive 2-D blind system identification (BSI) algorithm using the computationally efficient 2-D SE algorithm is proposed for the estimation of 2-D linear(More)
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