Apparatus and associated methods relate to an ADC-based digital receiver including a feedforward equalizer (FFE) that has m precursor taps and n postcursor taps to equalize the precursor portion, and to adapt postcursor intersymbol interference (ISI) through a predetermined equalization coefficient selected to counteract the noise boosting effect associated with the precursor equalization. In an illustrative example, the receiver may dynamically balance noise and ISI through adaptively determining a coefficient hp.sub.1 of a first postcursor tap of a first FFE and a coefficient h.sub.1 of a first postcursor tap of a second equalizer adapted to substantially reduce or eliminate additional ISI introduced by the first FFE. The first FFE may optimize ISI removal and noise reduction, for example. One of the coefficients h.sub.1 and hp.sub.1 may be predetermined, and then the other coefficient may be iteratively adapted to trade off precursor ISI and postcursor ISI to minimize BER.

An embodiment provides a closed loop active gate driver configured to drive a switch for an inductive load and including a feedback loop, the feedback loop configured to sample a repetitive output waveform of the inductive load, the output waveform having a plurality of repetitive cycles and the feedback loop configured to sample the output waveform using a sampling rate that is lower than a sampling rate required for characterizing the output waveform, sample points acquired in cycles of the plurality of cycles are acquired at different time points during the cycles and wherein a representation of the output waveform is reconstructed using the sample points.

The adaptive filter for sparse system identification is an adaptive filter that uses an algorithm in the feedback loop that is designed to provide better performance when the unknown system model is sparse, i.e., when the filter has only a few non-zero coefficients, such as digital TV transmission channels and echo paths. The algorithm is a least mean square algorithm with filter coefficients updated at each iteration, as well as a step size that is also updated at each iteration. The adaptive filter may be implemented on a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or by field-programmable gate arrays (FPGAs).

Disclosed are implementations, including a method that includes obtaining measurement samples relating to electrical operation of an electric motor drive providing power to an electric motor, deriving, based on the samples, instantaneous estimates for parameters characterizing speed and/or position of the motor according to an optimization process based on a cost function defined for the samples, and applying a filtering operation to the instantaneous estimates to generate filtered values of the motor's speed and/or position. The filtering operation includes computing the filtered values using the derived instantaneous estimates in response to a determination that a computed convexity of the cost function is greater than or equal to a convexity threshold value, and/or applying a least-squares filtering operation to the derived instantaneous estimates and using at least one set of previous estimates derived according to the optimization process applied to previous measurement samples.

An electric motor control system comprising an electronic switch configured to control a current flow in a motor winding based on a pre-distorted signal and a digital pre-distorter configured to generate the pre-distorted signal based on an input signal and a plurality of coefficients, wherein the plurality of coefficients are based on a feedback signal that represents the current flow in the motor winding.

A device capable of autonomous motion includes a residual echo suppressor for suppressing echoes caused by an output reference signal. When the device outputs audio while moving with a velocity, it may receive echoes that are Doppler-shifted due to the motion. The residual echo suppressor generates estimated residual error data based on phase-shifted reference data to account for and suppress the Doppler-shifted echoes.

A method is explained for any adaptive processor processing digital signals by adjusting signal weights on digital signal(s) it handles, to optimize adaptation criteria responsive to a functional purpose or externalities (transient, temporary, situational, and even permanent) of that processor. Adaptation criteria for the adaptive algorithm may be any combination of a signal or parameter estimation, and measured quality(ies).

This method performs a linear transformation adapting parameters from M to (M.sub.1+L) dimensions in each adaptation event, such that M.sub.1 weights are updated without constraints and M.sub.0=M−M.sub.1 weights are forced by soft constraints into an L-dimensional subspace they spanned at the beginning of the adaptation period. The same dimensionality reduction, using the same linear transformation, is applied to the input data. The reduced-dimensionality weights are then adapted using the identical optimization strategy employed by the processor, except with input data that has also been reduced in dimensionality.

Example embodiments disclosed herein relate to filter coefficient updating in time domain filtering. A method of processing an audio signal is disclosed. The method includes obtaining a predetermined number of target gains for a first portion of the audio signal by analyzing the first portion of the audio signal. Each of the target gains is corresponding to a subband of the audio signal. The method also includes determining filter coefficients for time domain filtering the first portion of the audio signal so as to approximate a frequency response given by the target gains. The filter coefficients are determined by iteratively selecting at least one target gain from the target gains and updating the filter coefficient based on the selected at least one target gain. Corresponding system and computer program product for processing an audio signal are also disclosed.

An adaptive processor implements partial updates when it adjusts weights to optimize adaptation criteria in signal estimation, parameter estimation, or data dimensionality reduction algorithms. The adaptive processor designates some of the weights to be update weights and the other weights to be held weights. Unconstrained updates are performed on the update weights, whereas updates to the set of held weights are performed within a reduced-dimensionality subspace. Updates to the held weights and the update weights employ adapt-path operations for tuning the adaptive processor to process signal data during or after tuning.

A method is proposed for processing a measurement signal, in particular for a steering system. The method comprises the following steps: A measured variable is acquired based on the measurement signal, wherein the measured variable comprises items of information about a physical variable, and wherein the measured variable is a superposition of the actual value of the physical variable and the measurement noise. Filter parameters of a filter are ascertained based on the measured variable and a mathematical model of the measurement noise. The measurement signal is filtered by means of the filter, whereby an estimated value of the physical variable is obtained, wherein the filter has the ascertained filter parameters. The filter parameters are ascertained in such a way that a deviation between the estimated value of the physical variable and the actual value of the physical variable is approximated and minimized. Furthermore, a control unit for a steering system, a steering system, a computer program, and a computer-readable data carrier are disclosed.