An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests.

Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. in this paper, we first show that this least squares bias induces a significant increase in the bias and mean-squared error of kernel-based estimators.

ARMA modeling of time series based on rational approximation of spectral density function

This study is concerned with Autoregressive Moving Average (ARMA) models of time series. ARMA models form a subclass of the class of general linear models which represents stationary time series, a phenomenon encountered most often in practice by engineers, scientists and economists. It is always desirable to employ models which use parameters parsimoniously. Parsimony will be achieved by ARMA models because it has only finite number of parameters. Even though the discussion is primarily concerned with stationary time series, later we will take up the case of homogeneous non stationary time series which can be transformed to stationary time series. Time series models, obtained with the help of the present and past data is used for forecasting future values. Physical science as well as social science take benefits of forecasting models. The role of forecasting cuts across all fields of management-—finance, marketing, production, business economics, as also in signal process, communication engineering, chemical processes, electronics etc. This high applicability of time series is the motivation to this study.

Decomposition of spectral density in individual eigenvalue contributions

The eigenvalue densities of two random matrix ensembles, the Wigner Gaussian matrices and the Wishart covariant matrices, are decomposed in the contributions of each individual eigenvalue distribution. It is shown that the fluctuations of all eigenvalues, for medium matrix sizes, are described with a good precision by nearly normal distributions.

ADSORBATE SPECTRAL DENSITY FOR A GENERALIZED ANDERSON-NEWNS HAMILTONIAN

We discuss adsorbate-metal electrostatic interaction in the Anderson-Newns model.

Spectral density enhancement using coherent WDM

It is shown by numerical simulations that a significant increase in the spectral density of a 40-Gb/s wavelength-division-multiplexing (WDM) system can be obtained by controlling the phase of adjacent WDM channels. These simulations are confirmed experimentally at 40 Gb/s using a coherent,comb source. This technique allows the spectral density of a nonreturn-to-zero WDM system to be increased from 0.4 to 1 b/s/Hz in a single polarization. Optical filter optimization is required to minimize power crosstalk, and appropriate strategies are discussed in this letter. Index Terms-Filtering, optical communication terminals, phase control, wavelength-division multiplexing (WDM).

Relationship Between Surface Topography and Energy Density Distribution of Er,Cr:YSGG Beam on Irradiated Dentin: An Atomic Force Microscopy Study

Objective: The aim of this study was to assess by atomic force microscopy (AFM) the effect of Er,Cr:YSGG laser application on the surface microtopography of radicular dentin. Background: Lasers have been used for various purposes in dentistry, where they are clinically effective when used in an appropriate manner. The Er, Cr: YSGG laser can be used for caries prevention when settings are below the ablation threshold. Materials and Methods: Four specimens of bovine dentin were irradiated using an Er, Cr:YSGG laser (lambda = 2.78 mu m), at a repetition rate of 20 Hz, with a 750-mu m-diameter sapphire tip and energy density of 2.8 J/cm(2) (12.5 mJ/pulse). After irradiation, surface topography was analyzed by AFM using a Si probe in tapping mode. Quantitative and qualitative information concerning the arithmetic average roughness (Ra) and power spectral density analyses were obtained from central, intermediate, and peripheral areas of laser pulses and compared with data from nonirradiated samples. Results: Dentin Ra for different areas were as follows: central, 261.26 (+/- 21.65) nm; intermediate, 83.48 (+/- 6.34) nm; peripheral, 45.8 (+/- 13.47) nm; and nonirradiated, 35.18 (+/- 2.9) nm. The central region of laser pulses presented higher ablation of intertubular dentin, with about 340-760 nm height, while intermediate, peripheral, and nonirradiated regions presented no difference in height of peritubular and interperitubular dentin. Conclusion: According to these results, we can assume that even when used at a low-energy density parameter, Er, Cr: YSGG laser can significantly alter the microtopography of radicular dentin, which is an important characteristic to be considered when laser is used for clinical applications.

Spectral properties of chaotic signals generated by the skew tent map

Chaotic signals have been considered potentially attractive in many signal processing applications ranging from wideband communication systems to cryptography and watermarking. Besides, some devices as nonlinear adaptive filters and phase-locked loops can present chaotic behavior. In this paper, we derive analytical expressions for the autocorrelation sequence, power spectral density and essential bandwidth of chaotic signals generated by the skew tent map. From these results, we suggest possible applications in communication systems. (C) 2009 Elsevier B.V. All rights reserved.

Spectral properties of chaotic signals with applications in communications

This paper investigates the characteristics of the Power Spectral Density (PSD) of chaotic signals generated by skew tent maps. The influence of the Lyapunov exponent on the autocorrelation sequence and on the PSD is evaluated via computational simulations. We conclude that the essential bandwidth of these signals is strongly related to this exponent and they can be low-pass or high-pass depending on the family`s parameter. This way, the PSD of a chaotic signal is a function of the generating map although this is not a one-to-one relationship. (C) 2009 Elsevier Ltd. All rights reserved.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Surface characterization techniques for determining the root-mean-square roughness and power spectral densities of optical components

Surface topography and light scattering were measured on 15 samples ranging from those having smooth surfaces to others with ground surfaces. The measurement techniques included an atomic force microscope, mechanical and optical profilers, confocal laser scanning microscope, angle-resolved scattering, and total scattering. The samples included polished and ground fused silica, silicon carbide, sapphire, electroplated gold, and diamond-turned brass. The measurement instruments and techniques had different surface spatial wavelength band limits, so the measured roughnesses were not directly comparable. Two-dimensional power spectral density (PSD) functions were calculated from the digitized measurement data, and we obtained rms roughnesses by integrating areas under the PSD curves between fixed upper and lower band limits. In this way, roughnesses measured with different instruments and techniques could be directly compared. Although smaller differences between measurement techniques remained in the calculated roughnesses, these could be explained mostly by surface topographical features such as isolated particles that affected the instruments in different ways.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.