Kurt Barbé

Nonparametric preprocessing in system identification: A powerful tool

In this paper we study the properties of existing non-parametric methods for estimating the plant and noise transfer functions of a linear dynamic system. The analysis is based on the recent insight that leakage errors in the frequency domain have a smooth nature that is completely similar to the initial transients in the time domain. This not only allows us to understand better the existing classic methods, but also opens the road to new better performing algorithms. The paper includes the output error setup, the errors-in-variables setup, and measurements under feedback conditions. Eventually, some of the methods are illustrated in the analysis of a vibrating metal beam.

Welch Method Revisited: Nonparametric Power Spectrum Estimation Via Circular Overlap

The objective of this paper is twofold. The first part provides further insight in the statistical properties of the Welch power spectrum estimator. A major drawback of the Welch method reported in the literature is that the variance is not a monotonic decreasing function of the fraction of overlap. Selecting the optimal fraction of overlap, which minimizes the variance, is in general difficult since it depends on the window used. We show that the explanation for the nonmonotonic behavior of the variance, as reported in the literature, does not hold. In the second part, this extra insight allows one to eliminate the nonmonotonic behavior of the variance for the Welch power spectrum estimator (PSE) by introducing a small modification to the Welch method. The main contributions of this paper are providing extra insight in the statistical properties of the Welch PSE; modifying the Welch PSE to circular overlap-the variance is a monotonically decreasing function of the fraction of overlap, making the method more user friendly; and an extra reduction of variance with respect to the Welch PSE without introducing systematic errors-this reduction in variance is significant for a small number of data records only.

Automatic Detection, Estimation, and Validation of Harmonic Components in Measured Power Spectra: All-in-One Approach

The detection of periodic components buried in noise is a general problem in various engineering fields. The amplitudes in the frequency domain of a disturbed signal follow Rice distribution, which is fully described by two parameters. Most methods are restricted to automatically detecting the harmonic components. In this paper, we extend the methodology to detect significant harmonics in measured spectra such that, aside from detection, the magnitude of the harmonic component is also estimated, together with the probability that the harmonic component was incorrectly detected.

Linearizing Oscillometric Blood-Pressure Measurements: (Non)Sense?

This paper proposes a simplified method to compute the systolic and diastolic blood pressures from measured oscillometric blood-pressure waveforms. Therefore, the oscillometric waveform is analyzed in the frequency domain, which reveals that the measured blood-pressure signals are heavily disturbed by nonlinear contributions. The proposed approach will linearize the measured oscillometric waveform in order to obtain a more accurate and transparent estimation of the systolic and diastolic pressure based on a robust preprocessing technique. This new approach will be compared with the Korotkoff method and a commercially available noninvasive blood-pressure meter. This allows verification if the linearized approach contains as much information as the Korotkoff method in order to calculate a correct systolic and diastolic blood pressure.

Analyzing the Windkessel Model as a Potential Candidate for Correcting Oscillometric Blood-Pressure Measurements

Developing a good model for oscillometric blood-pressure measurements is a hard task. This is mainly due to the fact that the systolic and diastolic pressures cannot be directly measured by noninvasive automatic oscillometric blood-pressure meters (NIBP) but need to be computed based on some kind of algorithm. This is in strong contrast with the classical Korotkoff method, where the diastolic and systolic blood pressures can be directly measured by a sphygmomanometer. Although an NIBP returns results similar to the Korotkoff method for patients with normal blood pressures, a big discrepancy exist between both methods for severe hyper- and hypotension. For these severe cases, a statistical model is needed to compensate or calibrate the oscillometric blood-pressure meters. Although different statistical models have been already studied, no immediate calibration method has been proposed. The reason is that the step from a model, describing the measurements, to a calibration, correcting the blood-pressure meters, is a rather large leap. In this paper, we study a “databased” Fourier series approach to model the oscillometric waveform and use the Windkessel model for the blood flow to correct the oscillometric blood-pressure meters. The method is validated on a measurement campaign consisting of healthy patients and patients suffering from either hyper- or hypotension.

Estimating the parameters of a Rice distribution: A Bayesian approach

The problem of detecting a periodic signal buried in zero-mean Gaussian noise is present in various fields of engineering. It is well-known that the amplitude of the disturbed signal follows a Rice distribution which is characterized by two parameters. In this paper, an alternative Bayesian approach is proposed to tackle this two-parameter estimation problem. By incorporating prior knowledge into a mathematical framework, the drawbacks of the existing methods (i.e., the maximum likelihood approach and the method of moments) can be overcome. The performance of the proposed Bayesian estimatoris shown through simulations.

Using Alternating Kalman Filtering to Analyze Oscillometric Blood Pressure Waveforms

Measuring the blood pressure as accurately as possible can save a lot of human lives. Hence, it is very important to find an optimal method to determine the systolic and diastolic pressures out of the measured oscillometric blood pressure waveform (OBPW). Recently, studies have shown that by working in the frequency domain, outperforming results could be obtained for the separation of breathing from cardiac activity. In this paper, we present a new implementation of the Kalman filtering algorithm to estimate the envelope of the cardiac activity. Even though the alternating Kalman filter algorithm has subharmonic infiltrations, it offers an envelope that, when applied together with the Windkessel model for calibration, substantially reduces the error of the calibrated systolic and diastolic pressures. This important result validates the non-linear model for the OBPW, as well as the Windkessel model for calibration.

Blind Spectrum Sensing for Cognitive Radios Using Discriminant Analysis: A Novel Approach

In this paper, we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator. The presented technique uses the knowledge of the noise uncertainty and a probabilistic validation to overcome the limitations of the discriminant analysis. A comparative study between the proposed technique and the maximum-minimum eigenvalue detection has been performed based on two performance metrics: the probability of false alarm and the probability of detection. The spectrum discriminator has been further developed to a peel-off technique where all primary users can be detected. The performance of the spectrum discriminator and the peel-off technique has been tested on simulations and experimentally verified. The comparative study is based on simulations as well as measurements.

Functional Magnetic Resonance Imaging: An Improved Short Record Signal Model

The number of measurement problems for which it is either difficult or expensive to obtain long measurement records is rising. This short-record issue particularly pops up in biomedical measurements. In this paper, we study the problem of modeling fMRI signals used to map brain activity. In this paper, it is shown that, by using a postprocessing technique that is well equipped to handle the short-record problem, either the spatial resolution or the detection accuracy of the active regions can be improved by at least 50%.

Frequency domain Errors-In-Variables estimation of linear dynamic systems using data from overlapping sub-records

In this paper, we study the consistency of a frequency-domain, errors-in-variables estimator using data extracted from overlapping subrecords. While the classical approach without overlap needs six consecutive periods, we show in this paper that by using overlapping subrecords, consistent models can be found with only two periods of the steady-state response of a periodic excitation. Moreover, the system identification procedure used for data extracted from independent experiments is shown to be valid for data extracted from overlapping subrecords. This allows the user to considerably reduce the measurement time or the measurement uncertainty without changing the identification procedure.