Optimization of Advertising Resources over Time: A Strategic Analysis

AMS subject classification: 90B60, 90B50, 90A80.

Electrocardiogram breathing: A study of electrocardiogram derived respiration plus multiresolution wavelet analysis of heartbeat interval variability in sleep study patients

This dissertation presents a unique research opportunity by using recordings which provide electrocardiogram (ECG) plus a reference breathing signal (RBS). ECG derived breathing (EDR) is measured and correlated against RBS. Standard deviations of multiresolution wavelet analysis coefficients (SDMW) are obtained from heart rate and classified using RBS. Prior works by others used select patients for sleep apnea scoring with EDR but no RBS. Another prior work classified select heart disease patients with SDMW but no RBS. This study used randomly chosen sleep disorder patient recordings; central and obstructive apneas, with and without heart disease.^ Implementation required creating an application because existing systems were limited in power and scope. A review survey was created to choose a development environment. The survey is presented as a learning tool and teaching resource. Development objectives were rapid development using limited resources (manpower and money). Open Source resources were used exclusively for implementation. ^ Results show: (1) Three groups of patients exist in the study. Grouping RBS correlations shows a response with either ECG interval or amplitude variation. A third group exists where neither ECG intervals nor amplitude variation correlate with breathing. (2) Previous work done by other groups analyzed SDMW. Similar results were found in this study but some subjects had higher SDMW, attributed to a large number of apneas, arousals and/or disconnects. SDMW does not need RBS to show apneic conditions exist within ECG recordings. (3) Results in this study support the assertion that autonomic nervous system variation was measured with SDMW. Measurements using RBS are not corrupted due to breathing even though respiration overlaps the same frequency band.^ Overall, this work becomes an Open Source resource which can be reused, modified and/or expanded. It might fast track additional research. In the future the system could also be used for public domain data. Prerecorded data exist in similar formats in public databases which could provide additional research opportunities. ^

Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Probing the innermost regions of AGN via time-resolved spectral analysis

The dynamics and geometry of the material inflowing and outflowing close to the supermassive black hole in active galactic nuclei are still uncertain. X-rays are the most suitable way to study the AGN innermost regions because of the Fe Kα emission line, a proxy of accretion, and Fe absorption lines produced by outflows. Winds are typically classified as Warm Absorbers (slow and mildly ionized) and Ultra Fast Outflows (fast and highly ionized). Transient Obscurers -optically thick winds that produce strong spectral hardening in X-rays, lasting from days to months- have been observed recently. Emission and absorption features vary on time-scales from hours to years, probing phenomena at different distances from the SMBH. In this work, we use time-resolved spectral analysis to investigate the accretion and ejection flows, to characterize them individually and search for correlations. We analyzed XMM-Newtomn data of a set of the brightest Seyfert 1 galaxies that went through an obscuration event: NGC 3783, NGC 3227, NGC 5548, and NGC 985. Our aim is to search for emission/absorption lines in short-duration spectra (∼ 10ks), to explore regions as close as the SMBH as the statistics allows for, and possibly catch transient phenomena. First we run a blind search to detect emission/absorption features, then we analyze their evolution with Residual Maps: we visualize simultaneously positive and negative residuals from the continuum in the time-energy plane, looking for patterns and relative time-scales. In NGC 3783 we were able to ascribe variations of the Fe Kα emission line to absorptions at the same energy due to clumps in the obscurer, whose presence is detected at >3σ, and to determine the size of the clumps. In NGC 3227 we detected a wind at ∼ 0.2c at ∼ 2σ, briefly appearing during an obscuration event.

Dissociating the spatio-temporal characteristics of cortical neuronal activity associated with human volitional swallowing in the healthy adult brain

Human swallowing represents a complex highly coordinated sensorimotor function whose functional neuroanatomy remains incompletely understood. Specifically, previous studies have failed to delineate the temporo-spatial sequence of those cerebral loci active during the differing phases of swallowing. We therefore sought to define the temporal characteristics of cortical activity associated with human swallowing behaviour using a novel application of magnetoencephalography (MEG). In healthy volunteers (n = 8, aged 28-45), 151-channel whole cortex MEG was recorded during the conditions of oral water infusion, volitional wet swallowing (5 ml bolus), tongue thrust or rest. Each condition lasted for 5 s and was repeated 20 times. Synthetic aperture magnetometry (SAM) analysis was performed on each active epoch and compared to rest. Temporal sequencing of brain activations utilised time-frequency wavelet plots of regions selected using virtual electrodes. Following SAM analysis, water infusion preferentially activated the caudolateral sensorimotor cortex, whereas during volitional swallowing and tongue movement, the superior sensorimotor cortex was more strongly active. Time-frequency wavelet analysis indicated that sensory input from the tongue simultaneously activated caudolateral sensorimotor and primary gustatory cortex, which appeared to prime the superior sensory and motor cortical areas, involved in the volitional phase of swallowing. Our data support the existence of a temporal synchrony across the whole cortical swallowing network, with sensory input from the tongue being critical. Thus, the ability to non-invasively image this network, with intra-individual and high temporal resolution, provides new insights into the brain processing of human swallowing. © 2004 Elsevier Inc. All rights reserved.

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Time series analysis of lava flux

We have applied time series analytical techniques to the flux of lava from an extrusive eruption. Tilt data acting as a proxy for flux are used in a case study of the May–August 1997 period of the eruption at Soufrière Hills Volcano, Montserrat. We justify the use of such a proxy by simple calibratory arguments. Three techniques of time series analysis are employed: spectral, spectrogram and wavelet methods. In addition to the well-known ~9-hour periodicity shown by these data, a previously unknown periodic flux variability is revealed by the wavelet analysis as a 3-day cycle of frequency modulation during June–July 1997, though the physical mechanism responsible is not clear. Such time series analysis has potential for other lava flux proxies at other types of volcanoes.

Time-frequency analysis combining wavelets and the envelope technique for fault identification in rotary machines

Nowadays the method based on demodulation by envelope finds wide application in industry as a technique for evaluation of bearings and other components in rotating machinery. In recent years the application of Wavelets for fault diagnosis in machinery has also obtained good development. This article demonstrates the effectiveness of the combined application of Wavelets and envelope technique (also known as HFRT High-Frequency Resonance Technique) to remove background noise from signals collected from defect bearings and identification of the characteristic frequencies of defects. A comparison of the results obtained with the isolated application of only one method against the combined technique is performed showing the increased capacity in detection of faults in rolling bearings. © (2013) Trans Tech Publications, Switzerland.

Evidence of the heterogeneous market hypothesis using wavelet multi -resolution analysis

In finance literature many economic theories and models have been proposed to explain and estimate the relationship between risk and return. Assuming risk averseness and rational behavior on part of the investor, the models are developed which are supposed to help in forming efficient portfolios that either maximize (minimize) the expected rate of return (risk) for a given level of risk (rates of return). One of the most used models to form these efficient portfolios is the Sharpe's Capital Asset Pricing Model (CAPM). In the development of this model it is assumed that the investors have homogeneous expectations about the future probability distribution of the rates of return. That is, every investor assumes the same values of the parameters of the probability distribution. Likewise financial volatility homogeneity is commonly assumed, where volatility is taken as investment risk which is usually measured by the variance of the rates of return. Typically the square root of the variance is used to define financial volatility, furthermore it is also often assumed that the data generating process is made of independent and identically distributed random variables. This again implies that financial volatility is measured from homogeneous time series with stationary parameters. In this dissertation, we investigate the assumptions of homogeneity of market agents and provide evidence for the case of heterogeneity in market participants' information, objectives, and expectations about the parameters of the probability distribution of prices as given by the differences in the empirical distributions corresponding to different time scales, which in this study are associated with different classes of investors, as well as demonstrate that statistical properties of the underlying data generating processes including the volatility in the rates of return are quite heterogeneous. In other words, we provide empirical evidence against the traditional views about homogeneity using non-parametric wavelet analysis on trading data, The results show heterogeneity of financial volatility at different time scales, and time-scale is one of the most important aspects in which trading behavior differs. In fact we conclude that heterogeneity as posited by the Heterogeneous Markets Hypothesis is the norm and not the exception.

A framework for the response time analysis of fixed-priority tasks with stochastic inter-arrival times

Real-time scheduling usually considers worst-case values for the parameters of task (or message stream) sets, in order to provide safe schedulability tests for hard real-time systems. However, worst-case conditions introduce a level of pessimism that is often inadequate for a certain class of (soft) real-time systems. In this paper we provide an approach for computing the stochastic response time of tasks where tasks have inter-arrival times described by discrete probabilistic distribution functions, instead of minimum inter-arrival (MIT) values.

Time-frequency and time-scale characterisation of the beat-by-beat high-resolution electrocardiogram

Proceedings of the Sixth Portuguese Conference on Bioemedical Engineering faro, Portugal

Carbon prices. Dynamic analysis of European and Californian markets

Dissertação para obtenção do Grau de Doutor em Alterações Climáticas e Políticas de Desenvolvimento Sustentável