An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system

Linear Predictive Spectral Analysis via the Lp Norm

This study involves linear predictive spectral analysis under the general LP norm; both one dimensional and two dimensional spectral estimation algorithms are developed. The objective in this study is determination of frequency resolution capability for various LP normed solutions to linear predictive spectral estimation equations. A modified residual steepest descent algorithm is utilized to generate the required solution. The research presented in this thesis could not have been accomplished without the support of the Oklahoma State University Research Consortium For Well Log Data Enhancement Via Signal Processing. The member companies of this consortium include Amococ Production Company, Area Oil and Gas Company, Cities Service Oil and Gas Corporation, Conoco, Exxon, IBM, Mobil Research and Development, Phillips Petroleum Corporation, Sohio Petroleum Company, and Texaco.Electrical Engineerin

Advanced Condition Monitoring of Complex Mechatronics Systems Based on Model-of-Signals and Machine Learning Techniques

Prognostics and Health Management (PHM) of machinery has become one of the pillars of Industry 4.0. The introduction of emerging technologies into the industrial world enables new models, new forms, and new methodologies to transform traditional manufacturing into intelligent manufacturing. In this context, diagnostics and prognostics of faults and their precursors has gained remarkable attention, mainly when performed autonomously by systems. The field is flourishing in academia, and researchers have published numerous PHM methodologies for machinery components. The typical course of actions adopted to execute servicing strategies on machinery components requires significant sensor measurements, suitable data processing algorithms, and appropriate servicing choices. Even though the industrial world is integrating more and more Information Technology solutions to keep up with Industry 4.0 new trends most of the proposed solutions do not consider standard industrial hardware and software. Modern controllers are built based on PCs and workstations hardware architectures, introducing more computational power and resources in production lines that we can take advantage of. This thesis focuses on bridging the gap in PHM between the industry and the research field, starting from Condition Monitoring and its application using modern industrial hardware. The cornerstones of this "bridge" are Model-of-Signals (MoS) and Machine Learning techniques. MoS relies on sensor measurements to estimate machine working condition models. Those models are the result of black-box system identification theory, which provides essential rules and guidelines to calculate them properly. MoS allows the integration of PHM modules into machine controllers, exploiting their edge-computing capabilities, because of the availability of recursive estimation algorithms. Besides, Machine Learning offers the tools to perform a further refinement of the extracted information, refining data for diagnostics, prognostics, and maintenance decision-making, and we show how its integration is possible within the modern automation pyramid

Traitement paramétrique des signaux audio dans le contexte des prothèses auditives

Modèle à moyenne mobile > -- Modèle autorégressif > -- Modèle autorégressif à moyenne mobile > -- Remarque sur le lien entre AR, MA et ARMA -- Evaluation des paramètres d'un processus AR(p) -- Critères de sélection de l'ordre d'un modèle AR(p) -- Notion d'enveloppe spectrale -- Méthodes élaborées dans le domaine fréquentiel -- Méthodes élaborées dans le domaine de corrélation -- Réduction de bruit dans le domaine fréquentiel -- A two-microphone algorithm for speech enhancement -- State of the art -- Zelinski's approach in the case of two-microphone arrangement -- Two-microphone speech enhancement system -- Performance evaluation and results -- Réduction de bruit dans le domaine de corrélation -- Estimation de la puissance du bruit -- Compensation des effets du bruit -- Amélioration de la procédure de compensation -- Perspectives de développement -- Traitement paramétrique en présence de bruit -- Disposition du traitement combiné -- Amélioration de la précision de l'estimateur de variance du bruit

Quality control procedures for GNSS precise point positioning in the presence of time correlated residuals

PhD ThesisPrecise point positioning (PPP) is a technique for processing Global Navi- gation Satellite Systems (GNSS) data, often using recursive estimation methods e.g. a Kalman Filter, that can achieve centimetric accuracies using a single receiver. PPP is now the dominant real-time application in o shore marine positioning industry. For high precision real-time applications it is necessary to use high rate orbit and clock corrections in addition to high rate observations. As Kalman filters require input of process and measurement noise statistics, not precisely known in practice, the filter is non-optimal. Geodetic quality control procedures as developed by Baarda in the 1960s are well established and their extension to GNSS is mature. This methodology, largely unchanged since the 1990s, is now being applied to processing techniques that estimate more parameters and utilise many more observations at higher rates. \Detection, Identification and Adaption" (DIA), developed from an optimal filter perspective and utilising Baarda's methodology, is a widely adopted GNSS quality control procedure. DIA utilises various test statistics, which require observation residuals and their variances. Correct derivation of the local test statistic requires residuals at a given epoch to be uncorrelated with those from previous epochs. It is shown that for a non-optimal filter the autocorrelations between observations at successive epochs are non-zero which has implications for proper application of DIA. Whilst less problematic for longer data sampling periods, high rate data using real-time PPP results in significant time correlations between residuals over short periods. It is possible to model time correlations in the residuals as an autoregressive process. Using the autoregressive parameters, the effect of time correlation in the residuals can be removed, creating so-called whitened residuals and their variances. Thus a whitened test statistic can be formed, that satisfies the preferred assumption of uncorrelated residuals over time. The effectiveness of this whitened test statistic and its impact on quality control is evaluated.Fugro Intersite B.V.: The Natural Environment Research Council

Bias Removal Approach in System Identification and Arma Spectral Estimation

Electrical Engineerin

A matching filter and envelope system for timbral blending of the bass guitar

A method of intelligent filter curve estimation from signal spectra is investigated to assess its viability of blending the perceived timbres of two signals together. By influencing the spectrum of a source signal with that of a modifier, its magnitude spectrum will be reshaped to resemble the modifier signal and reflect some of its timbral characteristics more closely. A system of transplanting the time-domain signal envelope of a signal onto a host is also presented in a combined system. The intended purpose of such a system is in the development of a hybrid acoustic-electric instrument where the timbral products of an expressive performance may be used to manipulate the spectrum and envelope of musical signals. A bass guitar is studied as the source instrument given the wide range of expressive techniques that may be executed on the instrument. Further analysis of spectra gathered from bass guitar performance techniques are used to provide deeper insight into performance techniques that may be performed on the instrument

Postfiltering techniques in low bit-rate speech coders

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 78-80).by Azhar K. Mustapha.M.Eng