Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
Systems and methods are disclosed for performing segmentation and labeling of signals generated by single molecule sequencing. In certain embodiments, a method may comprise receiving a training signal generated by molecular detection, segmenting the training signal into a set of events, determining signal characteristics for the set of events, generating a Hidden Markov Model (HMM) based on the set of events and the signal characteristics. The HMM may also be applied to a second signal and may responsively segment the second signal into a second set of events and label the second set of events based on the signal characteristics. A labeled sequence signal output may be provided that includes the second set of events and corresponding labels generated by the HMM.
Abstract:
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.
Abstract:
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.