Order Blind Identification Algorithm Research Articles

Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity

The power distribution network is susceptible to several Power Quality (PQ) disturbances. Among those, the harmonic and interharmonic distortions should be highlighted due to their high proliferation. This work proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI). This algorithm is normally applied to a multivariate data set, what implies in a necessity of multiple measurements in different points of the system that will be analyzed. However, Single-Channel Blind Source Separation (SCBSS) method will be proposed in this work to estimate the components via SOBI using only one measured signal point. The method works as a set of adaptive filters whose coefficients are blindly obtained via SOBI and is responsible for the components separation. An Exact Model Order (EMO) algorithm will be used to improve the performance of the SOBI algorithm in order to estimate the correct number of components to be separated. Also, the EMO will be helpful to reduce the computational complexity of the SOBI. The performance of the proposed SCBSS method will be compared to that of the SCICA (Single-Channel Independent Component Analysis) based on the well-known FastICA algorithm, which employs Higher Order Statistics (HOS). It will be shown that the proposed SCBSS overtakes the SCICA for harmonic and interharmonic decomposition in performance and in reduced computational complexity. Also, the proposed SCBSS method will be compared to EMO-ESPRIT algorithm, where will be shown that the SCBSS achieved better results in noisy and time-varying scenarios. Finally, the proposed SCBSS will be applied for the analysis of a voltage signal acquired from the simulation of a power system containing wind generation.

Modal parameter estimation of a reduced-scale rocket nozzle using blind source separation

In this paper, two blind source separation (BSS) algorithms are used to estimate the modal parameters of a reduced-scale rocket nozzle using only measurements of deformation. The data is gathered using a single high-speed video camera, in combination with fluorescent markers placed on the nozzle lip illuminated by a Nd:YLF pulsed laser strobe. A marker tracking algorithm is used to measure the displacement of each fluorescent marker. The nozzle is excited using a conventional shaker with band-limited white noise input as well as an impact hammer. The results of the two BSS algorithms, namely the second order blind identification algorithm and the complexity pursuit algorithm, are compared to a well-established output-only modal analysis technique, the stochastic subspace identification algorithm, as well as finite element analysis predictions. It was found that both BSS algorithms correctly identified all modes compared to the stochastic subspace identification algorithm and the finite element analysis, and the calculated eigenfrequencies are within 0.1% of measured values. The BSS algorithms facilitate the accurate extraction of modal parameters from output-only measurements with a minimum of user interaction compared to conventional modal analysis algorithms.

Influence of ocular filtering in EEG data on the assessment of drug‐induced effects on the brain

Ocular artifacts in EEG signals affect the interpretation of clinical study results. The aim of this study was to assess the influence of automatic ocular filtering procedures in the conclusions drawn from a pharmaco-EEG trial. Regression analysis, gold standard, and blind source separation (BSS), Second Order Blind Identification algorithm, ocular filtering procedures were compared using time, frequency, topographic and tomographic brain mapping approaches and pharmacokinetic-pharmacodynamic (PK-PD) relationships. Data consisted of EEGs obtained from 20 volunteers who received single oral doses of haloperidol 3 mg, risperidone 1 mg, olanzapine 5 mg and placebo in a randomized cross-over double-blind design. Although the BSS-based technique preserved brain activity more than regression analysis in anterior leads, in general, topographic significance probability maps globally showed similar results with both methods for most spectral variables. However, different results were obtained when using whole multi-channel information for studying drug effects in the brain: (i) higher correlations between PK and PD time courses showing that BSS allowed estimation of spectral variables more accurately related to drug effects and (ii) larger and more symmetric drug related tomographic LORETA maps showing that BSS led to results that were more neurophysiopharmacologically sound. Definitely, the BSS-based procedure is an effective and efficient preprocessing method to remove ocular artifacts from EEG data. The selection of the ocular filtering procedure could determine different results whose impact depends on the evaluating tool applied to analyze the pharmaco-EEG data.