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.

The invention relates to a method for filtering an input signal (3b, 4b, 5b) relative to a physical variable of a turbine engine (9), the input signal being digitised, the method implementing frequency filtering of said signal in a computer (6) of a control system (7) of said turbine engine (9), said signal being provided at the input of the computer, a digital derivative of said signal being intended for being used by the control system (7), characterised in that it involves: —detecting an amplitude variation of said variable on said input signal, by a step of generating a second derivative signal (S) of the input signal and a step of comparing a value of the second derivative value of the input signal with at least one predetermined threshold (S.sub.1 . . . S.sub.n); and —adapting the frequency filtering of said input signal as a function of the detected amplitude variation of said variable, by a step of controlling a controlled filter (PB.sub.11) capable of applying frequency filtering to the input signal, so that the controlled filter applies or does not apply the frequency filtering as a function of a result of the comparison step.

The present disclosure relates to a concept of nonlinear signal processing which may be used for predistortion for RF power amplifiers. The concept includes generating time variant filter coefficients for a linear filter circuit based on a nonlinear mapping of an input signal, and filtering the input signal with the linear filter circuit using the time variant filter coefficients in order to generate a filtered output signal. Thus, it is proposed to implement a non-linear filter by a time-varying linear filter where the time-varying coefficients are derived from the input signal.

The exemplary embodiments provide a parallel implementation of filters with recursive transfer functions. This can enable a filter to act as a frame filter that may process a frame of multiple samples of data in parallel rather than being limited to processing a single sample of data at a time. Each frame contains plural input samples of data values. The input samples are from a common source and have a time dependency. The exemplary embodiments are suitable for implementing various types of filters in parallel, such as cascaded integrator comb filters, biquad filters and other types of infinite impulse response (IIR) filters. The exemplary embodiments may use polyphase decomposition to decompose a filter with a recursive transfer function into multiple polyphase component filters. The polyphase component filters may be applied to respective samples of data in a parallel pipelined configuration to produce filtered output for the samples of data in parallel.

Systems and methods for electronic devices including two or more semiconductor devices coupled via an interconnect. The interconnect includes multiple lanes each having a link between the first and second semiconductor devices. One or more lanes of the multiple lanes each include clock and data recovery circuitry to perform full clock and data recovery. One or more other lanes of the multiple lanes each do not include clock and data recovery circuitry and instead includes a phase adjustment and clock multiplier circuit that is slave to clock and data recovery circuitry of the one or more lanes.

A medical device system, such as an extra-cardiovascular implantable cardioverter defibrillator ICD, senses R-waves from a first cardiac electrical signal by a first sensing channel and stores a time segment of a second cardiac electrical signal in response to each sensed R-wave. The medical device system determines a morphology parameter correlated to signal noise from time segments of the second cardiac electrical signal, detects a noisy signal segment based on the signal morphology parameter; and withholds detection of a tachyarrhythmia episode in response to detecting a threshold number of noisy signal segments.

A noise reduction system includes sensors configured to generate an input signal, an adaptive filter configured to represent a transfer function of a path traversed by the input signal, one or more processing devices, and one or more transducers. The processing devices receive the input signal and generate an updated set of filter coefficients of the adaptive filter by separating the input signal into frequency subbands; determining for each subband, coefficients of a corresponding subband adaptive module; and combining the coefficients of multiple subband adaptive modules. Determining the coefficients of the corresponding subband adaptive module includes selecting a subset of a precomputed set of filter coefficients of the adaptive filter. The processing devices process a portion of the input signal using the updated set of filter coefficients of the adaptive filter to generate an output that destructively interferes with another signal traversing the path represented by the transfer function.

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.

In one illustrative embodiment, an equalizer includes: a shift register, an array of multipliers, an array of multiplexers, and a summer. The shift register provides receive signal samples at each tap. Each multiplier in the array multiplies one of said receive signal samples by a respective coefficient to produce a product, with at least one of said multipliers coupled to a fixed tap. Each multiplexer in the array supplies an associated one of said multipliers with a receive signal sample from a selectable tap. The summer sums the products to produce a filtered output signal. To reduce hardware requirements, coefficient multipliers may be multiplexed to a reduced set of taps, and the dynamic range of the coefficients may be increased by overlapping the sets for different multipliers. Methods of tap selection and coefficient adaptation are disclosed.