ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing.

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction.

Fast Kalman-like optimal FIR filter for time-variant systems with improved robustness.

In this paper, a fast Kalman-like iterative OFIR algorithm is proposed for discrete-time filtering of linear time-varying dynamic systems. The batch OFIR filter is re-derived in an alternative way to show that this filter is unique for such systems. A computationally efficient fast iterative form is found for the OFIR filter using recursions.

Error probability for RFID SAW tags with pulse position coding and peak-pulse detection.

This paper addresses the code reading error probability (EP) in radio-frequency identification (RFID) SAW tags with pulse position coding (PPC) and peak-pulse detection. EP is found in a most general form, assuming M groups of codes with N slots each and allowing individual SNRs in each slot. The basic case of zero signal in all off-pulses and equal signals in all on-pulses is investigated in detail.

Linear unbiased prediction of clock errors.

In this paper, we propose a new formula for linear unbiased prediction of the local clock timescales. To predict future errors over all the measurement data, a new gain is derived for the p-step ramp unbiased finite impulse response (FIR) predictor. We then show that this gain gives the best linear unbiased fit suitable for forming the prediction vector.

Efficient predictive estimator for holdover in GPS-based clock synchronization.

This paper addresses an unbiased p-step predictive finite impulse response (FIR) filter of the local clock K-degree time interval error (TIE) polynomial model with applications to the global positioning system (GPS)-based clock synchronization. Generic coefficients are derived for a 2-parameter family of the polynomial filter gains. A generalization is provided for the p-step linear (ramp) gain allowing for close to optimal predictive filtering of the TIE.

On real-time optimal FIR estimation of linear TIE models of local clocks.

In this paper, we present an optimal finite-impulse response (FIR) filter for the linear time-interval error (TIE) model of a local clock and compare its estimates to those obtained by the unbiased FIR filter via the global positioning system (GPS)-based sawtooth measurements. We show theoretically and verify experimentally that there is no reasonable necessity in using optimal filters for GPS-based estimation of the TIE of precision crystal, rubidium, and cesium clocks. For these clocks, the optimal FIR filter requiring four coefficients degenerates to a simple unbiased FIR filter requiring only one coefficient.

An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping.

An unbiased finite impulse response (FIR) filter is proposed to estimate the time-interval error (TIE) K-degree polynomial model of a local clock in global positioning system (GPS)-based timekeeping in the presence of noise that is not obligatory Gaussian. Generic coefficients for the unbiased FIRs are derived. The low-degree FIRs and noise power gains are given.

Approximate estimates of limiting errors of passive wireless SAW sensing with DPM.

This paper discusses approximate statistical estimates of limiting errors associated with single differential phase measurement of a time delay (phase difference) between two reflectors of the passive surface acoustic wave (SAW) sensor. The remote wireless measurement is provided at the ideal coherent receiver using the maximum likelihood function approach. Approximate estimates of the mean error, mean square error, estimate variance, and Cramér-Rao bound are derived along with the error probability to exceed a threshold in a wide range of signal-to-noise ratio (SNR) values.

The noise conversion method for oscillatory systems.

The paper addresses a method for calculating the amplitude and phase power spectral density (PSD) functions of an oscillatory system (resonator, oscillator, bandpass filter, selective circuit, etc.) via the PSDs of its intrinsic noise sources and relevant transformation coefficients. A systematic description of the method is given for the scalar and vector noises.

One-port noise model of a crystal oscillator.

This paper presents a one-port noise model of a crystal oscillator combined with equivalent impedances of a resonator and linearized feedback amplifier. Based on the noise conversion technique, we translate the thermal additive and flicker noises of both the resonator and amplifier into the oscillator signal amplitude and phase. The generic transformation coefficients for the noise are derived, and the power spectral density (psd) function of the oscillator signal phase is analyzed in detail.

A simple optimally unbiased MA filter for timekeeping.

In this paper, we address the design of an optimally unbiased moving average (MA) filter for time error online estimating and synchronization in Global Positioning System (GPS)-based timekeeping. We proceed from the slowly changing nature of a time error generated by a local clock, assuming its function to be linear for N points in the average. Employing the inherent property of a simple MA to produce the lowest noise for the given averaging interval and using linear regression as a proper stochastic approximation to evaluate the bias, we find the optimal weighting coefficients.