Statistical signal extraction using stable processes

The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation.

Monetary policy with signal extraction from the bond market

Monetary policy is conducted in an environment of uncertainty. This paper sets upa model where the central bank uses real-time data from the bond market togetherwith standard macroeconomic indicators to estimate the current state of theeconomy more efficiently, while taking into account that its own actions influencewhat it observes. The timeliness of bond market data allows for quicker responsesof monetary policy to disturbances compared to the case when the central bankhas to rely solely on collected aggregate data. The information content of theterm structure creates a link between the bond market and the macroeconomythat is novel to the literature. To quantify the importance of the bond market asa source of information, the model is estimated on data for the United Statesand Australia using Bayesian methods. The empirical exercise suggests that thereis some information in the US term structure that helps the Federal Reserve toidentify shocks to the economy on a timely basis. Australian bond prices seemto be less informative than their US counterparts, perhaps because Australia is arelatively small and open economy.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

Mean chemical characteristics and volcano signatures of ice cores from Belukha, the Everest and three Svalbard sites

Ice cores from outside the Greenland and Antarctic ice sheets are difficult to date because of seasonal melting and multiple sources (terrestrial, marine, biogenic and anthropogenic) of sulfates deposited onto the ice. Here we present a method of volcanic sulfate extraction that relies on fitting sulfate profiles to other ion species measured along the cores in moving windows in log space. We verify the method with a well dated section of the Belukha ice core from central Eurasia. There are excellent matches to volcanoes in the preindustrial, and clear extraction of volcanic peaks in the post-1940 period when a simple method based on calcium as a proxy for terrestrial sulfate fails due to anthropogenic sulfate deposition. We then attempt to use the same statistical scheme to locate volcanic sulfate horizons within three ice cores from Svalbard and a core from Mount Everest. Volcanic sulfate is <5% of the sulfate budget in every core, and differences in eruption signals extracted reflect the large differences in environment between western, northern and central regions of Svalbard. The Lomonosovfonna and Vestfonna cores span about the last 1000 years, with good extraction of volcanic signals, while Holtedahlfonna which extends to about AD1700 appears to lack a clear record. The Mount Everest core allows clean volcanic signal extraction and the core extends back to about AD700, slightly older than a previous flow model has suggested. The method may thus be used to extract historical volcanic records from a more diverse geographical range than hitherto.

Search for s-channel single top-quark production in proton-proton collisions at s√=8 TeV with the ATLAS detector

This Letter presents a search at the LHC for s-channel single top-quark production in proton-proton collisions at a centre-of-mass energy of 8 TeV. The analyzed data set was recorded by the ATLAS detector and corresponds to an integrated luminosity of 20.3 fb−1. Selected events contain one charged lepton, large missing transverse momentum and exactly two b-tagged jets. A multivariate event classifier based on boosted decision trees is developed to discriminate s-channel single top-quark events from the main background contributions. The signal extraction is based on a binned maximum-likelihood fit of the output classifier distribution. The analysis leads to an upper limit on the s-channel single top-quark production cross-section of 14.6 pb at the 95% confidence level. The fit gives a cross-section of σs=5.0±4.3 pb, consistent with the Standard Model expectation.

Giffen goods and market making

This paper shows that information effects per se are not responsible forthe Giffen goods anomaly affecting competitive traders demands in multi-asset, noisy rational expectations equilibrium models. The role thatinformation plays in traders strategies also matters. In a market withrisk averse, uninformed traders, informed agents havea dual motive for trading: speculation and market making. Whilespeculation entails using prices to assess the effect of private signalerror terms, market making requires employing them to disentangle noisetraders effects in traders aggregate orders. In a correlated environment,this complicates a trader s signal-extraction problem and maygenerate upward-sloping demand curves. Assuming either (i) that competitive,risk neutral market makers price the assets, or that (ii) the risktolerance coefficient of uninformed traders grows without bound, removesthe market making component from informed traders demands, rendering themwell behaved in prices.

Multiple filtering devices for the estimation of cyclical DSGE models

We propose a method to estimate time invariant cyclical DSGE models using the informationprovided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural and non-structuralparameters jointly using a signal extraction approach. We employ simulated data to illustratethe properties of the procedure and compare our conclusions with those obtained when just onefilter is used. We revisit the role of money in the transmission of monetary business cycles.

Irreversible investment in stochastically cyclical markets

This paper presents a new framework for studying irreversible (dis)investment whena market follows a random number of random-length cycles (such as a high-tech productmarket). It is assumed that a firm facing such market evolution is always unsure aboutwhether the current cycle is the last one, although it can update its beliefs about theprobability of facing a permanent decline by observing that no further growth phasearrives. We show that the existence of regime shifts in fluctuating markets suffices for anoption value of waiting to (dis)invest to arise, and we provide a marginal interpretationof the optimal (dis)investment policies, absent in the real options literature. Thepaper also shows that, despite the stochastic process of the underlying variable has acontinuous sample path, the discreteness in the regime changes implies that the samplepath of the firm s value experiences jumps whenever the regime switches all of a sudden,irrespective of whether the firm is active or not.

¿Son excesivamente suaves las series de contabilidad nacional trimestral?

Este trabajo analiza si las series de Contabilidad Nacional Trimestral de España son excesivamente suaves y, por lo tanto, si son realmente informativas de la evolución de la economía española a corto plazo. Mediante la utilización de las técnicas de análisis espectral se observa que las series trimestrales españolas presentan una variabilidad mayor que las de otros países de la OCDE en el intervalo de frecuencias más bajas (asociadas al comportamiento de la serie a largo plazo ) y una variabilidad menor en el intervalo de frecuencias más altas (asociadas al ruido que contiene la serie). El motivo de este comportamiento diferencial de las series trimestrales españolas se encuentra en el método utilizado por el Instituto Nacional de Estadística por estimar la señal ciclo-tendencia de los indicadores utilizados como referencia, concretamente, el conocido como filtro de líneas aéreas modificado (LAM)

¿Son excesivamente suaves las series de contabilidad nacional trimestral?

Este trabajo analiza si las series de Contabilidad Nacional Trimestral de España son excesivamente suaves y, por lo tanto, si son realmente informativas de la evolución de la economía española a corto plazo. Mediante la utilización de las técnicas de análisis espectral se observa que las series trimestrales españolas presentan una variabilidad mayor que las de otros países de la OCDE en el intervalo de frecuencias más bajas (asociadas al comportamiento de la serie a largo plazo ) y una variabilidad menor en el intervalo de frecuencias más altas (asociadas al ruido que contiene la serie). El motivo de este comportamiento diferencial de las series trimestrales españolas se encuentra en el método utilizado por el Instituto Nacional de Estadística por estimar la señal ciclo-tendencia de los indicadores utilizados como referencia, concretamente, el conocido como filtro de líneas aéreas modificado (LAM)

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.