Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Design and development of compact printed ultra wide band antennas

In recent years, there is a visible trend for products/services which demand seamless integration of cellular networks, WLANs and WPANs. This is a strong indication for the inclusion of high speed short range wireless technology in future applications. In this context UWB radio has a significant role to play as an extension/complement to existing cellular/access technology. In the present work, three major types of ultra wide band planar antennas are investigated: Monopole and Slot. Three novel compact UWB antennas, suitable for poratble applications, are designed and characterized, namely 1) Ground modified monopole 2) Serrated monopole 3) Triangular slot The performance of these designs have been studied using standard simulation tools used in industry/academia and they have been experimentally verified. Antenna design guidelines are also deduced by accounting the resonances in each structure. In addition to having compact sized, high efficiency and broad bandwidth antennas, one of the major criterion in the design of impulse-UWB systems have been the transmission of narrow band pulses with minimum distortion. The key challenge is not only to design a broad band antenna with constant and stable gain but to maintain a flat group delay or linear phase response in the frequency domain or excellent transient response in time domain. One of the major contributions of the thesis lies in the analysis of the frequency and timedomain response of the designed UWB antennas to confirm their suitability for portable pulsed-UWB systems. Techniques to avoid narrowband interference by engraving narrow slot resonators on the antenna is also proposed and their effect on a nano-second pulse have been investigated

Investigations On A Microstrip Excited Rectangular Dielectric Resonator Antenna

The thesis relates to the investigations carried out on Rectangular Dielectric Resonator Antenna configurations suitable for Mobile Communication applications. The main objectives of the research are to: - numerically compute the radiation characteristics of a Rectangular DRA - identify the resonant modes - validate the numerically predicted data through simulation and experiment 0 ascertain the influence of the geometrical and material parameters upon the radiation behaviour of the antenna ° develop compact Rectangular DRA configurations suitable for Mobile Communication applications Although approximate methods exist to compute the resonant frequency of Rectangular DRA’s, no rigorous analysis techniques have been developed so far to evaluate the resonant modes. In this thesis a 3D-FDTD (Finite Difference Time Domain) Modeller is developed using MATLAB® for the numerical computation of the radiation characteristics of the Rectangular DRA. The F DTD method is a powerful yet simple algorithm that involves the discretimtion and solution of the derivative form of Maxwell’s curl equations in the time domain.

Compositional Data Analysis with R

R from http://www.r-project.org/ is ‘GNU S’ – a language and environment for statistical computing and graphics. The environment in which many classical and modern statistical techniques have been implemented, but many are supplied as packages. There are 8 standard packages and many more are available through the cran family of Internet sites http://cran.r-project.org . We started to develop a library of functions in R to support the analysis of mixtures and our goal is a MixeR package for compositional data analysis that provides support for operations on compositions: perturbation and power multiplication, subcomposition with or without residuals, centering of the data, computing Aitchison’s, Euclidean, Bhattacharyya distances, compositional Kullback-Leibler divergence etc. graphical presentation of compositions in ternary diagrams and tetrahedrons with additional features: barycenter, geometric mean of the data set, the percentiles lines, marking and coloring of subsets of the data set, theirs geometric means, notation of individual data in the set . . . dealing with zeros and missing values in compositional data sets with R procedures for simple and multiplicative replacement strategy, the time series analysis of compositional data. We’ll present the current status of MixeR development and illustrate its use on selected data sets

Macrodisco y glaucoma en individuos estudiados con tomografía óptica coherente

Introducción: El glaucoma representa la tercera causa de ceguera a nivel mundial y un diagnóstico oportuno requiere evaluar la excavación del nervio óptico que está relacionada con el área del mismo. Existen reportes de áreas grandes (macrodiscos) que pueden ser protectoras, mientras otros las asocian a susceptibilidad para glaucoma. Objetivo: Establecer si existe asociación entre macrodisco y glaucoma en individuos estudiados con Tomografía Optica Coherente (OCT ) en la Fundación Oftalmológica Nacional. Métodos: Estudio transversal de asociación que incluyó 25 ojos con glaucoma primario de ángulo abierto y 74 ojos sanos. A cada individuo se realizó examen oftalmológico, campo visual computarizado y OCT de nervio óptico. Se compararon por grupos áreas de disco óptico y número de macrodiscos, definidos según Jonas como un área de la media más dos desviaciones estándar y según Adabache como área ≥3.03 mm2 quien evaluó población Mexicana. Resultados: El área promedio de disco óptico fue 2,78 y 2,80 mm2 glaucoma Vs. sanos. De acuerdo al criterio de Jonas, se observó un macrodisco en el grupo sanos y según criterio de Adabache se encontraron ocho y veinticinco macrodiscos glaucoma Vs. sanos. (OR=0,92 IC95%=0.35 – 2.43). Discusión: No hubo diferencia significativa (P=0.870) en el área de disco entre los dos grupos y el porcentaje de macrodiscos para los dos grupos fue similar, aunque el bajo número de éstos no permitió concluir en términos estadísticos sobre la presencia de macrodisco y glaucoma.

Spatio-temporal analysis of an invasive plant pathogen (Phytophthora ramorum) in England and Wales

Phytophthora ramorum is a damaging invasive plant pathogen and was first discovered in the UK in 2002. Spatial point analyses were applied to the occurrence of this disease in England and Wales during the period of 2003-2006 in order to assess its spatio-temporal spread. Out of the 4301 garden centres and nurseries (GCN) surveyed, there were 164, 105, 123 and 41 sites with P. ramorum in 2003, 2004, 2005 and 2006, respectively. Spatial analysis of the observed point patterns of GCN outbreaks suggested that these sites were significantly clumped within a radius of ca 60 km in 2003, but not in later years. Further analyses were conducted to determine the relationship of GCN outbreak sites over two consecutive years and thus to infer possible disease spread over time. This analysis suggested that disease spread among GCN sites was most likely to have occurred within a distance of 60 km for 2003-2004, but not for the later years. There were 35, 63, 81 and 58 sites with P. ramorum in the semi-natural environment (SNE). Analyses were carried out to assess whether infected GCN sites could act as an inoculum source of infected SNE plants or vice versa. In all years, there was a significant spatial closeness among GCN and SNE outbreak sites within a distance of 1 km. But a significant relationship over a longer distance (within 60 km) was only observed between cases in 2003 and 2004. These analyses suggest that statutory actions taken so far appear to have reduced the extent of long-distance spread of P. ramorum among garden centres and nurseries, but not the disease spread at a shorter distance between GCN and SNE sites.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

Wiener-System subspace identification for mobile wireless mm-wave networks

We discuss the feasibility of wireless terahertz communications links deployed in a metropolitan area and model the large-scale fading of such channels. The model takes into account reception through direct line of sight, ground and wall reflection, as well as diffraction around a corner. The movement of the receiver is modeled by an autonomous dynamic linear system in state space, whereas the geometric relations involved in the attenuation and multipath propagation of the electric field are described by a static nonlinear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a single-output Wiener model from time-domain measurements of the field intensity when the receiver motion is simulated using a constant angular speed and an exponentially decaying radius. The identification procedure is validated by using the model to perform q-step ahead predictions. The sensitivity of the algorithm to small-scale fading, detector noise, and atmospheric changes are discussed. The performance of the algorithm is tested in the diffraction zone assuming a range of emitter frequencies (2, 38, 60, 100, 140, and 400 GHz). Extensions of the simulation results to situations where a more complicated trajectory describes the motion of the receiver are also implemented, providing information on the performance of the algorithm under a worst case scenario. Finally, a sensitivity analysis to model parameters for the identified Wiener system is proposed.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales

This paper examines two hydrochemical time-series derived from stream samples taken in the Upper Hafren catchment, Plynlimon, Wales. One time-series comprises data collected at 7-hour intervals over 22 months (Neal et al., submitted, this issue), while the other is based on weekly sampling over 20 years. A subset of determinands: aluminium, calcium, chloride, conductivity, dissolved organic carbon, iron, nitrate, pH, silicon and sulphate are examined within a framework of non-stationary time-series analysis to identify determinand trends, seasonality and short-term dynamics. The results demonstrate that both long-term and high-frequency monitoring provide valuable and unique insights into the hydrochemistry of a catchment. The long-term data allowed analysis of long-termtrends, demonstrating continued increases in DOC concentrations accompanied by declining SO4 concentrations within the stream, and provided new insights into the changing amplitude and phase of the seasonality of the determinands such as DOC and Al. Additionally, these data proved invaluable for placing the short-term variability demonstrated within the high-frequency data within context. The 7-hour data highlighted complex diurnal cycles for NO3, Ca and Fe with cycles displaying changes in phase and amplitude on a seasonal basis. The high-frequency data also demonstrated the need to consider the impact that the time of sample collection can have on the summary statistics of the data and also that sampling during the hours of darkness provides additional hydrochemical information for determinands which exhibit pronounced diurnal variability. Moving forward, this research demonstrates the need for both long-term and high-frequency monitoring to facilitate a full and accurate understanding of catchment hydrochemical dynamics.

The sample autocorrelation function and the detection of long-memory processes

The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to be identified using the sum of the sample autocorrelations, as usually defined. The reason for this is that the sample sum is a predetermined constant for any stationary time series; a result that is independent of the sample size. Diagnostic or estimation procedures, such as those in the frequency domain, that embed this sum are equally open to this criticism. We develop this result in the context of long memory, extending it to the implications for the spectral density function and the variance of partial sums of a stationary stochastic process. The results are further extended to higher order sample autocorrelations and the bispectral density. The corresponding result is that the sum of the third order sample (auto) bicorrelations at lags h,k≥1, is also a predetermined constant, different from that in the second order case, for any stationary time series of arbitrary length.

The time-course of morphological processing in a second language

We report findings from psycholinguistic experiments investigating the detailed timing of processing morphologically complex words by proficient adult second (L2) language learners of English in comparison to adult native (L1) speakers of English. The first study employed the masked priming technique to investigate -ed forms with a group of advanced Arabic-speaking learners of English. The results replicate previously found L1/L2 differences in morphological priming, even though in the present experiment an extra temporal delay was offered after the presentation of the prime words. The second study examined the timing of constraints against inflected forms inside derived words in English using the eye-movement monitoring technique and an additional acceptability judgment task with highly advanced Dutch L2 learners of English in comparison to adult L1 English controls. Whilst offline the L2 learners performed native-like, the eye-movement data showed that their online processing was not affected by the morphological constraint against regular plurals inside derived words in the same way as in native speakers. Taken together, these findings indicate that L2 learners are not just slower than native speakers in processing morphologically complex words, but that the L2 comprehension system employs real-time grammatical analysis (in this case, morphological information) less than the L1 system.

Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks

Single-carrier (SC) block transmission with frequency-domain equalisation (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high bandwidthefficiency and high power-efficiency systems, the channel can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, We model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

Coda wave attenuation in the Parecis Basin, Amazon Craton, Brazil: sensitivity to basement depth

Small local earthquakes from two aftershock sequences in Porto dos GaA(0)chos, Amazon craton-Brazil, were used to estimate the coda wave attenuation in the frequency band of 1 to 24 Hz. The time-domain coda-decay method of a single backscattering model is employed to estimate frequency dependence of the quality factor (Q (c)) of coda waves modeled usingwhere Q (0) is the coda quality factor at frequency of 1 Hz and eta is the frequency parameter. We also used the independent frequency model approach (Morozov, Geophys J Int, 175:239-252, 2008), based in the temporal attenuation coefficient, chi(f) instead of Q(f), given by the equation for the calculation of the geometrical attenuation (gamma) and effective attenuation Q (c) values have been computed at central frequencies (and band) of 1.5 (1-2), 3.0 (2-4), 6.0 (4-8), 9.0 (6-12), 12 (8-16), and 18 (12-24) Hz for five different datasets selected according to the geotectonic environment as well as the ability to sample shallow or deeper structures, particularly the sediments of the Parecis basin and the crystalline basement of the Amazon craton. For the Parecis basin for the surrounding shield and for the whole region of Porto dos GaA(0)chos Using the independent frequency model, we found: for the cratonic zone, gamma = 0.014 s (-aEuro parts per thousand 1), nu a parts per thousand 1.12; for the basin zone with sediments of similar to 500 m, gamma = 0.031 s (-aEuro parts per thousand 1), nu a parts per thousand 1.27; and for the Parecis basin with sediments of similar to 1,000 m, gamma = 0.047 s (-aEuro parts per thousand 1), nu a parts per thousand 1.42. Analysis of the attenuation factor (Q (c)) for different values of the geometrical spreading parameter (nu) indicated that an increase of nu generally causes an increase in Q (c), both in the basin as well as in the craton. But the differences in the attenuation between different geological environments are maintained for different models of geometrical spreading. It was shown that the energy of coda waves is attenuated more strongly in the sediments, (in the deepest part of the basin), than in the basement, (in the craton). Thus, the coda wave analysis can contribute to studies of geological structures in the upper crust, as the average coda quality factor is dependent on the thickness of sedimentary layer.

Experimental characterization of nonlinear systems: a real-time evaluation of the analogous Chua`s circuit behavior

This paper presents an experimental characterization of the behavior of an analogous version of the Chua`s circuit. The electronic circuit signals are captured using a data acquisition board (DAQ) and processed using LabVIEW environment. The following aspects of the time series analysis are analyzed: time waveforms, phase portraits, frequency spectra, Poincar, sections, and bifurcation diagram. The circuit behavior is experimentally mapped with the parameter variations, where are identified equilibrium points, periodic and chaotic attractors, and bifurcations. These analysis techniques are performed in real-time and can be applied to characterize, with precision, several nonlinear systems.