Seagate Technology Incorporated

Seagate Technologies, Inc.

Raman Venkataramani

William Michael Radich

Ara Patapoutian

Jason Charles Jury

Belkacem Derras

Rishi Ahuja

Mehmet Fatih Erden

Sundararajan Sankaranarayanan

Zheng Wang

Jason Bellorado

Marcus Marrow

Tim Rausch

Deepak Sridhara

Drew Michael Mader

Arvind Sridharan

Alexander Kuznetsov

Gil Shamir

Ching He

A cyclo-stationary characteristic of a communications channel and/or storage media is determined. The cyclo-stationary characteristic has K-cycles, K>1. Markov transition probabilities are determined that depend on a discrete phase ϕ=t mod K, wherein t is a discrete time value. An encoder to optimize the Markov transition probabilities for encoding data sent through the communications channel and/or stored on the storage media. The optimized Markov transition probabilities are used to decode the data from the communication channel and/or read from the storage media.

Markov encoder-decoder optimized for cyclo-stationary communications channel or storage media

Systems and methods are disclosed for performing event timing detection for DNA sequencing. In certain embodiments, a method may comprise generating a signal based on a DNA strand passed through a nanopore sensor, sampling the signal to generate a plurality of sample values, and detecting one or more event boundaries based on the sample values, an event representing a movement of a single DNA base of the DNA strand through the nanopore sensor. Detecting the one or more event boundaries may include segmenting the plurality of sample values into multiple events to calculate an optimal total score, assigning an event value to a selected event from the multiple events based on sample values of the selected event, and providing the event value to a base caller to determine a sequence of DNA bases.

Event timing detection for DNA sequencing

In one embodiment, a system provides for optimizing an error rate of data through a communication channel. The system includes a data generator operable to generate a training sequence as a Markov code, and to propagate the training sequence through the communication channel. The system also includes a Soft Output Viterbi Algorithm (SOVA) detector operable to estimate data values of the training sequence after propagation through the communication channel. The system also includes an optimizer operable to compare the estimated data values to the generated training sequence, to determine an error rate based on the comparison, and to change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

Channel error rate optimization using Markov codes

A method includes receiving a data signal over a multi-input multi-output (MIMO) channel. The method further includes equalizing the data signal, by an adaptive equalizer circuit having an associated target, to provide an equalized output of the data signal. As part of the method, taps of the equalizer circuit and coefficients of the target are estimated. A constraint is imposed on the coefficients of the target as part of the estimation of the coefficients of the target. A similar minimization process is used with constraint imposed on whitening filter taps associated with a DDNP detector in the MIMO channel.

Adaptive MIMO channel equalization and detection

A circuit may be configured to adaptively combine two or more waveforms into a single waveform. The circuit can generate weighting factors based on received error signals, and can apply the weighting factors to the two or waveforms to be combined. In some examples, a circuit can be configured to receive input signals, receive error signals, generating a weighting coefficient based on at least some of the error signals, and determine an output signal based on the weighting coefficient and the input signals.

Adaptively combining waveforms

In certain embodiments, an apparatus may comprise a circuit configured to receive a plurality of samples of an input signal. The circuit may update one or more equalizer parameters using partial zero forcing equalization. Further, the circuit may generate an equalized signal based on the plurality of samples of the input signal and the one or more equalizer parameters.

Partial zero forcing equalization

A system may include an interpolator circuit configured to receive a first signal with a first rate and to generate an interpolated signal with a second rate. The system may include a cancellation circuit configured to determine an interference component signal based on the interpolated signal. The system may further comprise an adder configured to receive a second signal with the second rate and to cancel interference in the second signal using the interference component signal to generate a cleaned signal.

Asynchronous interference cancellation

First and second different write precompensation values are associated with different first and second non-return-to-zero, inverted (NRZI) data patterns. The first and second different write precompensation values cause a predetermined phase shift to be written into test data that comprises the first and second NRZI data patterns. The test data is mitten to a recording medium of a storage device using the first and second write precompensation value. The test data is used to determine a response of the storage device to the predetermined phase shift.

Obtaining a predetermined phase shift in data using different write precompensation associated with different NRZI patterns

A digitized signal is processed via an interpolator. The interpolator performs timing adjustment on the digitized signal. The error signal is determined based on a desired signal and the time-adjusted digitized signal. A corrective phase shift of the digitized signal is determined via a least-mean-squared processing block that uses the error and the derivative of a function used by the interpolator. The corrective phase shift is input to the interpolator to perform the timing adjustment.

Timing error processor that uses the derivative of an interpolator function

Technologies are described herein for adapting channel parameters of a read channel in a transformed space. A set of channel parameter values associated with one or more components of the read channel is received. A general transformation is performed on the channel parameter values, and insignificant terms in the transformed space are filtered out. Next, an inverse transformation is performed on the filtered terms in the transformed space to calculate a new set of channel parameter values for the component(s).

Noise prediction detector adaptation in transformed space

A circuit may be configured to adaptively combine two or more waveforms into a single waveform. The circuit can generate weighting factors based on received error signals, and can apply the weighting factors to the two or waveforms to be combined.

Apparatus and method for recovering data from a multi-channel input signal, such as but not limited to a readback signal from a bit patterned medium (BPM) having a plurality of subtracks. In accordance with some embodiments, a single input single output (SISO) equalizer is adapted to generate equalized outputs responsive to alternating subchannels of the multi-channel input signal. A detector is adapted to generate estimates of data symbols represented by the input signal responsive to the equalized outputs. A switching circuit is adapted to switch in different equalizer coefficients for use by the SISO equalizer for each of the alternating subchannels in the input signal.

Time-multiplexed single input single output (SISO) data recovery channel

Technologies are described herein for implementing a universal modulation coding mechanism for a data channel. A user data sequence comprising a first number of bits is received. The user data sequence is encoded into a codeword sequence comprising a second number of bits utilizing a modulation encoder. The modulation encoder may encode the user data sequence based on a set of Markov state transition probabilities. The modulation encoder may implement a data de-compressor algorithm from a lossless data compression scheme. The codeword sequence is output to the data channel. The codeword sequence is subsequently received from the data channel, and the user data sequence is decoded from the codeword sequence utilizing a modulation decoder. The modulation decoder may decode the user data sequence based on the same set of Markov states transition probabilities. The modulation de-coder may implement the data compressor algorithm from the lossless data compression scheme.

Universal modulation coding for a data channel

In a particular embodiment, a channel detector is disclosed that includes a programmable look-up table (LUT) to relate user bits to channel bits. The programmable LUT is adapted to be implemented on a state trellis of arbitrary radix. The channel detector further includes a sectional precoder coupled to a channel and having access to the programmable LUT. The sectional precoder is adapted to map channel bits to user bits and vice versa using a programmable LUT.

Programmable soft-output Viterbi algorithm system and method

An equalizer employed in conjunction with bit-patterned media (BPM) is designed to take advantage of a common topology associated with BPM in which each data track is comprised of a plurality of sub-tracks. The equalizer-target filter pair is designed as multiple-input/multiple-output (MIMO) system, in which the readback signal is divided into readback signals associated with each sub-track. The MIMO equalizer takes into account differences in the channel response associated with each sub-track. A detector is configured to receive a plurality of equalized inputs provided by the MIMO equalizer, wherein the detector generates in response estimates of the user data stored on each of the plurality of sub-tracks.

Growing community of inventors

Longmont, CO, United States of America

Raman Venkataramani