Adaptive signal processor using Newton/LMS algorithm

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364725, 364726, 36472803, G06F 1710

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056823410

ABSTRACT:
An adaptive signal processor using a Newton/least mean square (LMS) algorithm, adapted to remove a signal distortion or noise. The adaptive signal processor is adapted to derive an autocorrelation matrix from an input signal, derive an inverse matrix of the autocorrelation matrix by executing DCT twice or executing DFT and IDFT, and apply the derived inverse matrix to an adaptive signal processing, thereby capable of greatly reducing the total amount of computations to derive the inverse matrix, achieving an easy hardware realization thereof and reducing the manufacture cost thereof.

REFERENCES:
patent: 4589133 (1986-05-01), Swinbanks
patent: 4791390 (1988-12-01), Harris et al.
patent: 4843583 (1989-06-01), White et al.
patent: 4868850 (1989-09-01), Kaku et al.
patent: 5058047 (1991-10-01), Chung
patent: 5168459 (1992-12-01), Hiller
patent: 5253192 (1993-10-01), Tufts
patent: 5418849 (1995-05-01), Cannalire et al.
patent: 5418857 (1995-05-01), Eatwell
patent: 5513531 (1996-05-01), Sapia et al.
patent: 5535150 (1996-07-01), Chaing
patent: 5553014 (1996-09-01), De Leon et al.

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