Heinrich J Stockmanns

US Patent:

6889154, May 3, 2005

Filed:

Mar 28, 2003

Appl. No.:

10/402033

Inventors:

Jonathan J. Ashley - Los Gatos CA, US
Heinrich J. Stockmanns - Santa Cruz CA, US

Assignee:

Infineon Technologies AG

International Classification:

G06F019/00

US Classification:

702107, 702 87, 702190, 702196, 714795, 714799, 704242, 375262, 375341

Abstract:

Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. An offline algorithm for calculating the parameters of data-dependent noise predictive filters A-D is presented which has two phases: a noise statistics estimation or training phase, and a filter calculation phase. During the training phase, products of pairs of noise samples are accumulated in order to estimate the noise correlations. Further, the results of the training phase are used to estimate how wide (in bits) the noise correlation accumulation registers need to be. The taps [, , ] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C defined by(condition ).

US Patent:

7165000, Jan 16, 2007

Filed:

Apr 18, 2005

Appl. No.:

11/109207

Inventors:

Jonathan J. Ashley - Los Gatos CA, US
Heinrich J. Stockmanns - Santa Cruz CA, US

Assignee:

Infineon Technologies AG

International Classification:

G06F 19/00

US Classification:

702107, 702 85, 702190, 704242, 714795, 714799, 375262, 375341

Abstract:

Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. An offline algorithm for calculating the parameters of data-dependent noise predictive filters A–D is presented which has two phases: a noise statistics estimation or training phase, and a filter calculation phase. During the training phase, products of pairs of noise samples are accumulated in order to estimate the noise correlations. Further, the results of the training phase are used to estimate how wide (in bits) the noise correlation accumulation registers need to be. The taps [t,t,t] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C defined by C=E(nn|NRZ condition k).

US Patent:

7191083, Mar 13, 2007

Filed:

Jul 20, 2006

Appl. No.:

11/490981

Inventors:

Jonathan J. Ashley - Los Gatos CA, US
Heinrich J. Stockmanns - Santa Cruz CA, US

Assignee:

Infineon Technologies, AG - Munich

International Classification:

G01R 35/00

US Classification:

702107, 702 85, 702190, 714795, 714799, 704242, 375262, 375341, 360 46, 360 66

Abstract:

Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. An offline algorithm for calculating the parameters of data-dependent noise predictive filters A-D is presented which has two phases: a noise statistics estimation or training phase, and a filter calculation phase. During the training phase, products of pairs of noise samples are accumulated in order to estimate the noise correlations. Further, the results of the training phase are used to estimate how wide (in bits) the noise correlation accumulation registers need to be. The taps [t, t, t] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C defined by C=E(nn|NRZ condition k).

US Patent:

7522678, Apr 21, 2009

Filed:

Mar 28, 2003

Appl. No.:

10/402654

Inventors:

Jonathan J. Ashley - Los Gatos CA, US
Heinrich J. Stockmanns - Santa Cruz CA, US
Kai Chi Zhang - San Jose CA, US

Assignee:

Infineon Technologies AG

International Classification:

H03D 1/00

US Classification:

375341, 375340, 375262, 375263, 375265, 375286

Abstract:

An improved Viterbi detector is disclosed in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. Also disclosed is a method of reducing the complexity of the branch metric calculations by clustering branches corresponding to signals with similar signal-dependent noise statistics. A feature of this architecture is that the branch metrics are clustered into multiple groups, where all the members of each group draw input from a single, shared noise predictive filter corresponding to the group. In recording technologies as practiced today, physical imperfections in the representation of recorded user data in the recording medium itself are becoming the dominate source of noise in the read back data. This noise is highly dependent on what was (intended to be) written in the medium. The disclosed Viterbi detector exploits this statistical dependence of the noise on the signal.

US Patent:

7743314, Jun 22, 2010

Filed:

Dec 1, 2006

Appl. No.:

11/607492

Inventors:

Heinrich J. Stockmanns - Santa Cruz CA, US
William G. Bliss - Thornton CO, US
Razmik Karabed - San Jose CA, US
James W. Rae - Rochester MN, US

Assignee:

Marvell International Ltd. - Hamilton

International Classification:

H03M 13/03

US Classification:

714796, 714795

Abstract:

An improved Viterbi detector is disclosed in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. Also disclosed is a method of reducing the complexity of the branch metric calculations by clustering branches corresponding to signals with similar signal-dependent noise statistics. A feature of this architecture is that the branch metrics (and their corresponding square difference operators) are clustered into multiple groups, where all the members of each group draw input from a single, shared noise predictive filter corresponding to the group. In recording technologies as practiced today, physical imperfections in the representation of recorded user data in the recording medium itself are becoming the dominate source of noise in the read back data. This noise is highly dependent on what was (intended to be) written in the medium. The disclosed Viterbi detector exploits this statistical dependence of the noise on the signal.

US Patent:

8015477, Sep 6, 2011

Filed:

Jun 21, 2010

Appl. No.:

12/819993

Inventors:

Heinrich J. Stockmanns - Santa Cruz CA, US
William G. Bliss - Thornton CO, US
Razmik Karabed - San Jose CA, US
James W. Rae - Rochester MN, US

Assignee:

Marvell International Ltd. - Hamilton

International Classification:

H03M 13/03

US Classification:

714795, 714796, 375341

Abstract:

An improved Viterbi detector is disclosed in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. Also disclosed is a method of reducing the complexity of the branch metric calculations by clustering branches corresponding to signals with similar signal-dependent noise statistics. A feature of this architecture is that the branch metrics (and their corresponding square difference operators) are clustered into multiple groups, where all the members of each group draw input from a single, shared noise predictive filter corresponding to the group. In recording technologies as practiced today, physical imperfections in the representation of recorded user data in the recording medium itself are becoming the dominate source of noise in the read back data. This noise is highly dependent on what was (intended to be) written in the medium. The disclosed Viterbi detector exploits this statistical dependence of the noise on the signal.

US Patent:

20020154430, Oct 24, 2002

Filed:

Dec 18, 2001

Appl. No.:

10/025001

Inventors:

James Rae - Rochester MN, US
William Bliss - Thornton CO, US
Jonathan Ashley - Los Gatos CA, US
Razmik Karabed - San Jose CA, US
Stephen Franck - Felton CA, US
Fritz Mistlberger - Bavaria, DE
Matthias Driller - Santa Cruz CA, US
Heinrich Stockmanns - Santa Cruz CA, US
Dominik Margraf - Santa Cruz CA, US

International Classification:

G11B005/02

US Classification:

360/025000

Abstract:

An improved sampled amplitude read/write channel is provided. The system is an integrated Generalized Partial Response Maximum Likelihood (GPRML) read channel incorporating Read, Write, and Servo modes of operation. One implementation includes a 32/34 rate parity code and matched Viterbi detector, a 32 state Viterbi detector optimal parity processor, robust frame synchronization, self-adapive equalization, thermal asperity detection and compensation, adaptive magneto-resistive asymmetry compensation, low latency interpolated timing recovery and programmable write precompensation.