Florin-Marian Birleanu

A vector approach to transient signal processing

The detection and characterization of burst signals are challenging tasks for time-frequency analysis, due to their very short duration. This paper investigates in this context the recurrence plot analysis (RPA) method, from which it derives the vector samples processing (VeSP) concept. The paper shows that VeSP is a generic framework that unifies signal processing concepts like histogram and autocorrelation, which it also generalizes and extends. Results of VeSP based tools are provided, concerning detection of transient signals, noise reduction, and frequency estimation.

On the recurrence plot analysis method behaviour under scaling transform

In the last decade, the applications of the recurrence plot analysis method make it a valuable alternative to the time-frequency and time-scale tools. As it was initially developed for the study of dynamical systems, and was later used in nonlinear time series analysis, the question of using it as a signal processing tool has not been put into discussion yet. In this field the projective techniques are largely used, with good results. Nevertheless, they also have some limitations — especially regarding transient signal processing. But this kind of signals are ubiquitous in real world. In addition, propagation through various media as well as on multiple paths lead to delayed, attenuated and dilated versions of the original transients. In this paper we study the behaviour of the recurrence plot analysis method in the context of analyzing some finite duration signals being subject to rescalings of the amplitude and time axes. This study is a starting point in employing the analysis of recurrences in investigations of a large class of real world signals.

Recent Advances in Non-stationary Signal Processing Based on the Concept of Recurrence Plot Analysis

This work concerns the analysis of non-stationary signals using Recurrence Plot Analysis concept. Non-stationary signals are present in real-life phenomena such as underwater mammal’s vocalizations, human speech, ultrasonic monitoring, detection of electrical discharges, transients, wireless communications, etc. This is why a large number of approaches for non-stationary signal analysis are developed such as wavelet analysis, higher order statistics, or quadratic time-frequency analysis. Following the context, the methods defined around the concept of Recurrence Plot Analysis (RPA) constitute an interesting way of analyzing non-stationary signals and, particularly, the transient ones. Starting from the phase space and the recurrence matrix, new approaches [the angular distance, recurrence-based autocorrelation function (ACF), average-magnitude difference function (AMDF) and time-distributed recurrence (TDR)] are introduced in order to extract information about the non-stationary signals, specific to different applications. Comparisons with existing analysis methods are presented, proving the interest and the potential of the RPA-based approaches.

A time-distributed phase space histogram for detecting transient signals

Burst-type signals constitute an important class of transient signals, being used especially in the investigation of various physical environments by electric or acoustic means. An important issue in the analysis of this type of signals is their detection in time. In this paper, we propose a detection method that is based on the histogram of the phase space distributed over time. The method consists in representing the analyzed signal in phase space and, then, quantifying the recurrences of the trajectory obtained in this space. In this way, we derive a time - recurrence radius representation for the signal, that allows identification of positions and durations of the transients. Afterwards, we propose a method to obtain a detection curve starting from this representation of the signal. We also present here some results concerning the performance of our method in the presence of noise on both synthetic and real signals.

Modeling a PV panel using the manufacturer data and a hybrid adaptive method

This paper presents a new method for establishing the series and parallel resistors for the one-diode model of a PV panel. The proposed method uses only the PV panel data that are provided by the manufacturer to train a feed-forward neural network that computes the values of the two resistors. The developed model is validated by comparing it to a classic one diode model whose resistors are determined through Newton-Raphson method.

Feature extraction for distance-based classification of signal sources

In this paper we present a solution for the classification of different patterns from seismic signals generated by different human activities for which an automatic recognition is required. Some a priori known signals were available, which gave us the possibility to represent them in feature space in order to capture their global characteristics. The classification of signals is based on computing the Mahalanobis distance in feature space and finding the smallest distance. At the end of the paper we present the results obtained for 108 signals acquired from 8 different vibration sources, the source of each signal being already known. The approach to feature engineering and feature selection presented in this paper is generic and can be applied to a large class of classification problems.

Overview of our recent results in signal analysis using recurrence plots

This paper highlights the main results of our research in the field of recurrence plot analysis in the last five years. It briefly discusses results in the study of the method itself, as well as in some applications of it, i.e. characterization of speech signals, detection and localization of partial discharges in electric cables, representation of transient signals, estimation of signal parameters, reduction of additive noise, and representation of digitally modulated signals (ASK, FSK, PSK, and OFDM).

Estimation of thermo-hydrodynamic parameters in energy production systems using non-stationary signal processing

In order to control water flow in power generation systems, obtaining precise information about the parameters of the flow is important. The construction of such systems, as well as the operating conditions, impose the use of non-intrusive techniques. The use of ultrasounds allows solving this problem in a simple and elegant manner. The technique consists in sending a signal to be affected by the flow without sensibly interacting with it, and then extracting the desired parameters of the flow from the received signal. The paper first presents how the acoustic wave is altered by the propagation in the liquid environment, and then it studies the means to analyze the received signal in order to obtain from it information about two important parameters of the flow - the temperature of the water and the flow rate. Polynomial phase modeling is used, for obtaining a good estimation of the propagation times.