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.

Abstract: Various parallel processing devices, methods for designing such and using such are disclosed herein. For example, a parallel linear processing device is disclosed that includes two multipliers. One of the multipliers is operable to multiply a feedback signal by a first value and to provide a first multiplier output. The other multiplier is operable to multiply a data input by a second value and to provide a second multiplier output. The processing device further includes an adder and a register. The adder is operable to sum at least the first multiplier output and the second multiplier output and to provide an adder output. The register is operable to register the adder output as a register output, and the feedback signal provided to the first multiplier is derived from the register output.

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 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 304A-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 [t2[k], t1[k], t0[k]] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C[k] defined by Cij[k]=E(ni-3nj-3|NRZ condition k).

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 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 304A–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 [t2[k],t1[k],t0[k]] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C[k] defined by Cij[k]=E(ni?3nj?3|NRZ condition k).

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 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 304A-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 [t2[k], t1[k], t0[k]] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C[k] defined by Cij[k]=E(ni?3nj?3|NRZ condition k).

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.

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 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 304A-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.

Abstract: A system and method for acquisition signal error estimation is provided which uses one or more past values of the sequence to determine the nearest ideal sample value. According to one embodiment, four consecutive samples are used. According to another embodiment, two samples are used. The acquisition signal error estimator maybe used in conjunction with gain, DC offset, or magneto-resistive asymmetry control loops in a sampled amplitude read channel.

Abstract: A first trellis code (12A, 20A) according to the present invention is a rate 24/26 trellis code with three (3) bytes error propagation due to EPRML minimum instance channel errors, a minimum of six (6) transitions per code word and a maximum of twelve (12) consecutive zeroes. A second trellis code (12B, 20B) according to the present invention is a rate 48/51 trellis code, derived from the first trellis code. The second trellis code has four (4) bytes error propagation due to EPRML minimum distance channel errors, a minimum of twelve (12) transitions per code word and a maximum of twelve (12) consecutive zeroes. A third trellis code (12C, 20C) according to the present invention is a rate 48/51 trellis code with four (4) bytes error propagation due to EPRML minimum distance channel errors, a minimum of fourteen (14) transitions per code word and a maximum of eleven (11) consecutive zeroes.

Abstract: Encoding method for data to be recorded on optical disks using the pit-per-transition technique or return-to-zero techniques whereby the characteristics of the encoded bits produce a stable, low frequency spectral notch and minimum interference with associated focus and tracking servomechanisms. RLL (d,k) code words are selected so that the ratio of the lengths of pits and lands approximate some nonzero fraction less than one-half which depends on the (d,k) code employed. For example, a fraction of 1/3 is appropriate for RLL (2,7).

Abstract: The eleven-plane cubical puzzle, shown in FIG. 1, is a puzzle in the shape of a cube whose twenty-four exposed pieces may be permuted by rotations of groups of said pieces about any of seven axes passing through the vertices and the centers of the faces of said cube.

Abstract: The tabular puzzle, shown in FIG. 5, is a puzzle comprising nine square tablets arranged in a three-by-three array and inclosed in a mechanism in such a way that any group of four tablets meeting at a common corner point may be rotated about said point by any multiple of 90.degree.. The exposed face of each of the tablets is marked with an index which uniquely establishes said tablet's identity and orientation. These indices may be scrambled by a series of said rotations. The object of the puzzle is to return the tablets, and hence the indices, to their original positions and orientations in the said three-by-three array by means of a series of said rotations.