Cirrus Logic Inc.

Rahul Gawde

Siddharth Maru

Tejasvi Das

Junsong Li

Michael Allen Kost

Michael Allan Kost

Jon David Hendrix

Eric Lindemann

Matthew Beardsworth

Eric B Smith

Gregory Charles Yancey

Pablo Peso Parada

Marc John Kobayashi

Alvin Chuck Storvik

Josh Sanz-Robinson

A classification system for classifying sensor samples in a sensor system, the sensor system comprising N force sensors each configured to output a sensor signal, where N≥1, each sensor sample comprising N sample values from the N sensor signals, respectively, the classification system comprising: a classifier configured, for each of a series of candidate sensor samples, to perform a classification operation based on the N sample values concerned and generate a classification result which labels the candidate sensor sample as indicative of a defined target event, thereby generating a series of classification results corresponding to the series of candidate sensor samples, respectively; a determiner configured to output at least one event determination based on the series of classification results; and a controller configured to control the classifier and/or the determiner based on one or more controller input signals.

Force sensor sample classification

A method may include receiving an input signal, generating a baseline signal based on the input signal, generating a corrected input signal by subtracting the baseline signal from the input signal, determining a threshold level change of the input signal when the corrected input signal exceeds a level change threshold, and responsive to the threshold level change, updating the baseline signal to the level change threshold.

Baseline calculation for sensor system

A method for determining sensor parameters of an actively-driven sensor system may include performing an initialization operation to establish a baseline estimate of the sensor parameters, obtaining as few as three samples of a measured physical quantity versus frequency for the actively-driven sensor system, performing a refinement operation to provide a refined version of the sensor parameters based on the as few as three samples, iteratively repeating the refinement operation until the difference between successive refined versions of the sensor parameters is below a defined threshold, and outputting the refined sensor parameters as updated sensor parameters for the actively-driven sensor system.

Determination of resonant frequency and quality factor for a sensor system

A control method for a user interface system may include receiving an input signal, receiving a temperature signal indicative of a temperature, generating a baseline signal based on at least one among the input signal and the temperature signal, calculating an error signal based on a difference of the input signal and the baseline signal, and modifying the baseline signal based on the error signal.

Baseline estimation for sensor system

An error correction code for an array of N words of M bits each may be generated by: (i) for each word of the N words, computing a respective set of checkbits for single-error correction of such word; (ii) computing a set of bit-position-related checkbits comprising a bitwise logical exclusive OR of all of the sets of checkbits for single-error correction of the N words; (iii) for each word of the N words, computing a respective parity for the respective set of checkbits and the word itself in order to form a vector of N parity bits; (iv) computing a set of word-related checkbits for single-error correction of the vector of N parity bits; and (v) computing a cumulative parity bit comprising a parity calculation of the set of bit-position-related checkbits, the set of word-related checkbits, and the vector of N parity bits.

Detection of double bit errors and correction of single bit errors in a multiword array

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Lakeway, TX, United States of America

Rahul Gawde