Generalized eigenvector algorithm for blind equalization

Jelonnek, B.; Boss, D.; Kammeyer, Karl‐Dirk

doi:10.1016/s0165-1684(97)00108-4

Cited by 71 publications

(57 citation statements)

References 14 publications

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“…Therefore, by the similar way to as in [2], the maximization of | | Castella et al [5] have shown that from (9), a BD can be iteratively achieved by using x i (t) = ) ( t i y w (i = 1,n) as reference signals (see …”

Section: T T F Z H Z S T a Z S T mentioning

confidence: 99%

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A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Kawamoto

Kono

Inouye

et al. 2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

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Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.

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Section: T T F Z H Z S T a Z S T mentioning

confidence: 99%

“…Differently from the EVM in [5], (13) means that i w  is modified iteratively by the value of the right-hand side of (13) …”

Section: The Proposed Algorithmmentioning

confidence: 99%

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Kawamoto

Kono

Inouye

et al. 2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

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“…Since the 4 th -order cumulant involves computation of higher order statistics of equalized signals, a large amount of data is required for an accurate equalization [154]. If least mean squares-based stochastic gradient approach is adopted, the equalization might be slow and convergence rate may not be always satisfied.…”

Section: Eigenvector Algorithm (Eva) For Bementioning

confidence: 99%

“…Therefore, 4 th -order cumulant can be used for measuring non-Gaussianity of the source signal. In this chapter, the 4 th -order cumulant is adopted as a criterion for separating noise from PD signals.Since the 4 th -order cumulant involves computation of higher order statistics of equalized signals, a large amount of data is required for an accurate equalization [154]. If least mean squares-based stochastic gradient approach is adopted, the equalization might be slow and convergence rate may not be always satisfied.…”

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confidence: 99%

Advanced signal processing techniques for online power transformer insulation diagnosis

Chan¹

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Power transformer is one of the most important assets in an electric utility. However, a large number of existing power transformers worldwide have already approached or even exceeded their designed lifetimes. Any failure of a transformer can be disastrous. Therefore, the conditions of transformers need to be continuously monitored and evaluated. Since a transformer's condition is largely dependent on its insulation system, a number of diagnostic methods have been developed for assessing transformer insulation conditions over the past decades. Among these methods, partial discharge (PD) measurement is widely adopted due to its capability of providing continuously online monitoring and diagnosis of a transformer without disturbing its normal operation.PD is a rather complicated phenomenon and stochastic in nature. Properly performing online PD measurements of a transformer, effectively analysing the measured PD signals, and subsequently making an informed condition assessment on a transformer's insulation system are still challenging.This thesis is aimed at developing advanced signal processing techniques for online PD monitoring and diagnosis of power transformer insulation systems.PD signals acquired at substation environments are always coupled with extensive noise, which exhibits different distribution properties. Therefore, PD signal de-noising is an essential process for accurately extracting PD signals from the acquired noise-corrupted signals before further analysis. In this thesis, advanced signal processing techniques, such as wavelet transform (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), blind equalization (BE) and pre-whitening, have been investigated for removing discrete spectral interference (DSI) that exhibits sinusoidal behaviour at various frequencies. Mathematical morphology (MM) has also been investigated for suppressing white noise by adaptively selecting threshold values. To remove stochastic noise, fractal dimension and entropy analyses have been investigated. Based on these techniques, several adaptive PD signal de-noising methods have been proposed in this thesis for removing different types of noise. A number of case studies using PD signals acquired from both laboratory experiments and online PD measurements of field transformers are presented. These case studies demonstrate advantages of the proposed PD signal de-noising methods over conventional methods in PD signal de-noising.In this thesis, phase-resolved pulse sequence (PRPS) diagrams and kurtograms have also been proposed for consistent representations of PD patterns after the noise has been removed. Case studies have been provided to prove a PRPS diagram's capability for accurately and consistently ii representing PD patterns. This representation can minimize influences of different types of PD sensors and measurement systems on PD pattern construction. Results are presented to demonstrate that kurtograms can be used to represent PD patterns even in the presence of extensive w...

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“…To solve this problem, we use eigenvector algorithms (EVAs) [5,6,13]. The first proposal of the EVA was done by Jelonnek et al [5].…”

Section: Introductionmentioning

confidence: 99%

Blind Deconvolution of MIMO-IIR Systems: A Two-Stage EVA

Kawamoto

Inouye

Kohno

Neural Information Processing

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Abstract. This paper deals with a blind deconvolution (DB) problem for multiple-input multiple-output infinite impulse response (MIMO-IIR) systems. To solve this problem, we propose an eigenvector algorithm (EVA). In the proposed EVA, two kinds of EVAs are merged so as to give a good performance: One is an EVA and the other is a Robust EVA (REVA) which works with as little sensitive to Gaussian noise as possible. Owing to this combination, two drawbacks of the conventional EVAs can be overcome. Simulation results show the validity of the proposed EVA.

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Generalized eigenvector algorithm for blind equalization

Cited by 71 publications

References 14 publications

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

A modified eigenvector method for blind deconvolution of MIMO systems using the matrix pseudo-inversion lemma

Advanced signal processing techniques for online power transformer insulation diagnosis

Blind Deconvolution of MIMO-IIR Systems: A Two-Stage EVA

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