Statistical signal processing for mechanical systems

Random processes such as temperature and acoustic noise are found in all types of mechanical systems. Knowledge of these processes can lead to improved design and detection methods related to faulty operation. The goal of this dissertation is to contribute to the knowledge base of such processes. Specifically, we address statistical signal processing methods that are appropriate and consistent relative to the physics of these systems. Two generic problems associated with random signal measurements from mechanical systems are addressed.;Random processes associated with mechanical systems usually have complex spectral structure containing both continuous and line spectral components. Accordingly, they are called mixed random processes. One problem addressed is to use variability related to families of spectral estimators for a mixed random process to better characterize its spectral information. We show that tones are a significant source of bias and variability of families of spectral estimators. Expressions for estimating statistical and arithmetic variability of three common families of spectral estimators are provided. An important and immediate application of these results is tone detection.;We also address the statistical problem of estimating the bandwidth parameter of a Gauss-Markov process from a realization of fixed and finite duration at selectable sampling interval. The motivation is that continuous-time processes are often sampled at a rate far higher than their underlying dynamics. It is commonly assumed a faster sample rate is better. But in many real world situations, such as in adaptive feedback control schemes design, short time changes demand only limited time being utilized. Thus this problem is investigated. The bias and variance expressions of the parameter estimator are derived with a second order expansion. Three sample rate regions---finite, large and very large ones, corresponding to substantial, gradual, and very slight variance drop, are quantitatively identified. Guidelines in choosing sampling rate based on estimator performance requirement are provided.;The results are used to characterize the stochastic structure of the sound pressure process from an engine cooling fan with and without mock engine, and to perform a hypothesis test for deciding whether a design change has a significant effect on the sound

Using variability related to families of spectral estimators for mixed random processes

Traditionally, characterization of spectral information for wide sense stationary processes has been addressed by identifying a single best spectral estimator from a given family. If one were to observe significant variability in neighboring spectral estimators then the level of confidence in the chosen estimator would naturally be lessened. Such variability naturally occurs in the case of a mixed random process, since the influence of the point spectrum in a spectral density characterization arises in the form of approximations of Dirac delta functions. In this work we investigate the nature of the variability of the point spectrum related to three families of spectral estimators: Fourier transform of the truncated unbiased correlation estimator, the truncated periodogram, and the autoregressive estimator. We show that tones are a significant source of bias and variability. This is done in the context of Dirichlet and Fejer kernels, and with respect to order rates. We offer some expressions for estimating statistical and arithmetic variability. Finally, we include an example concerning helicopter vibration. These results are especially pertinent to mechanical systems settings wherein harmonic content is prevalent

The relationship between stock prices, house prices and consumption in OECD

This paper analyzes the relationship between stock prices, house prices and consumption using data for 16 OECD countries. The panel data analysis suggests that the long-run responsiveness of consumption to permanent changes in stock prices is higher for countries with a market-based financial system than for countries with a bank-based financial system. Splitting the sample into the 1980s and 1990s further shows an increased sensitivity in the 1990's of consumption to permanent changes in stock prices for both countries with bank-based financial systems as well as countries with market-based financial systems. The relationship between changes in consumption and changes in house prices is positive for the second sample period across all specifications and financial systems.

Range-based Risk Estimation in Euro Area Countries

This dissertation considers a range of topics on the use of range-based risk estimators for financial markets (with the exception of Chapter 5 discussed below). Chapter 1 provides an introduction to the existing literature and the research objectives of the dissertation. Chapter 2 uses time series of daily high-low ranges of national equity market indices to analyse daily volatility dynamics and volatility spillover across four European markets. Chapter 2 is based on the joint research with Gregory Connor. We develop a dynamic linear model of expected daily range which is a variant of Chou’s conditional autoregressive range model. We find significant, but not uniform, range-based volatility spillovers. During the crisis period (after July 2007) we find significant increases in daily range, increases in contemporaneous correlation, and increases in the influence of previous-day US market range on the conditional expected range of these European markets. A gamma-distribution-based model of realized daily range fits more closely than one based upon a Feller distribution, but it sacrifices the link to a specific distribution for underlying returns. In Chapter 3 we use information on the daily opening, close, high, and low prices of individual stocks to estimate range-based correlation and to construct a new estimator of market betas. We create a measure called “range-beta”, which is based on the daily range-based volatility and covariance estimators of Rogers and Zhou (2008). These range-based betas reflect the current day’s intra-day price movements. They avoid a weakness of return based betas, which typically are based on close-to-close returns. Our approach yields competitive estimates compared with traditional methodologies, and outperforms other methodologies when analysing highly liquid assets. Chapter 4 studies the relationship between options-implied and realized-range-based volatility estimates for Euro area countries. When both implied volatility and historical range-based volatility are used to forecast realized range-based volatility, we find that implied volatility outperforms historical range-based volatility. We also find that the stochastic volatility is priced with a negative market price of risk. The volatility implied from option prices is higher than the realized range-based volatility under the objective measure due to investor risk aversion. Chapter 5 considers financial market risk from a different perspective. Chapter 5 analyses the tone and information content of the two external policy reports of the Internal Monetary Fund (IMF), the IMF Article IV Staff Reports and Executive Board Assessments, for Euro area countries. In particular, we create a tone measure denoted WARNING, based on the existing DICTION 5.0 Hardship dictionary. We find that in the run-up to the current credit crises, average WARNING tone levels of Staff Reports for Slovenia, Luxembourg, Greece, and Malta are one standard deviation above the EMU sample mean; and for Spain and Belgium, they are one standard deviation below the mean value. Furthermore, on average for Staff Reports over the period 2005-2007, there are insignificant differences between the EMU sample mean and Staff Reports’ yearly averages. We also find the presence of a significantly increased level of WARNING tone in 2006 for the IMF Article IV Staff Reports. There is also a systematic bias of WARNING scores for Executive Board Assessments versus WARNING scores for the Staff Reports

Automatic modulation classification of communication signals

The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know in advance if the received communication signal is analogue modulated or digitally modulated. Furthermore, it is noted that the applications of the currently existing AMR algorithms designed for handling both analog and digital communication signals are rather restricted in practice. Motivated by this, an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals is developed in this dissertation. The proposed algorithm is able to recognize the concrete modulation type if the input is an analog communication signal and to estimate the number of modulation levels and the frequency deviation if the input is an exponentially modulated digital communication signal. For linearly modulated digital communication signals, the proposed classifier will classify them into one of several nonoverlapping sets of modulation types. In addition, in M-ary FSK (MFSK) signal classification, two classifiers have also been developed. These two classifiers are also capable of providing good estimate of the frequency deviation of a received MFSK signal. For further classification of linearly modulated digital communication signals, it is often necessary to blindly equalize the received signal before performing modulation recognition. This doing generally requires knowing the carrier frequency and symbol rate of the input signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm have been developed. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. Unlike the cyclic correlation based estimators, it does not require the transmitted symbols being non-circularly distributed. The symbol rate estimator is based on digital communication signals\u27 cyclostationarity related to the symbol rate. In order to adapt to the unknown symbol rate as well as the unknown excess bandwidth, the received signal is first filtered by using a bank of filters. Symbol rate candidates and their associated confident measurements are extracted from the fourth order cyclic moments of the filtered outputs, and the final estimate of symbol rate is made based on weighted majority voting. A thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio