Search results for "wavelet."
showing 7 items of 327 documents
A data aggregation strategy based on wavelet for the internet of things
2017
The advent of emerging information and communication technologies, such as RFID, small size sensors and sensor networks, has made accessible a huge amount of information that requires sophisticated and efficient search algorithms to support queries on that data. In this paper we focus on the problem of aggregating data collected from these devices to efficiently support queries, inferences or statistics on them. In general, data aggregation techniques are necessary to efficiently collect information in a compact and cost-effective way. Some current solutions try to meet the above criteria, by exploiting different data aggregation techniques, for instance BitVector or Q_Digest. In this manus…
A wavelet Methodology for EEG Time-frequency Analysis in a Time Discrimination Task
2009
EEG signals recorded by surface electrodes placed on the scalp can be thought as non- stationary stochastic processes in both time and space, especially in response to external stimuli. Cognitive tasks, in particular, are reflected by changes in EEG dynamics concerning both rhythms energy and connectivity across different brain regions. In the frequency-domain, EEG analysis is complicated and time-frequency methodologies are needed. The Wavelet Transform, in particular, represents a powerful tool for analysing, within a time-frequency embedding, the EEG. In this study we applied a wavelet-based methodology to extract quantitative time-frequency parameters from EEG signals recorded during a …
Spectral Structures in Econometrics: Modern Techniques in Wavelet Analysis and Band Limited Estimation
2007
This thesis presents a number of innovative techniques that can be used in the analysis of econometric data sequences in which the underlying components can be identified by their spectral signatures. To present these techniques intelligibly requires the preparatory expositions of Fourier analysis and of the theory of linear filtering that are presented in Chapters 2 and 3. Amongst the techniques for extracting components from short non stationary sequences that are described in Chapter 3 is a variant of the Hodrick--Prescott filter with a smoothing parameter that varies locally. This enables us to extract from the data trends that incorporate a number of structural breaks. The inadequacy o…
Hilbert-Huang versus morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm
2009
Background. Compared to the waveform or spectrum analysis of event-related potentials (ERPs), time-frequency representation (TFR) has the advantage of revealing the ERPs time and frequency domain information simultaneously. As the human brain could be modeled as a complicated nonlinear system, it is interesting from the view of psychological knowledge to study the performance of the nonlinear and linear time-frequency representation methods for ERP research. In this study Hilbert-Huang transformation (HHT) and Morlet wavelet transformation (MWT) were performed on mismatch negativity (MMN) of children. Participants were 102 children aged 8–16 years. MMN was elicited in a passive oddbal…
Volatility co-movements: a time scale decomposition analysis
2013
In this paper we investigate short-run co-movements before and after the Lehman Brothers’ collapse among the volatility series of US and a number of European countries. The series under investigation (implied and realized volatility) exhibit long-memory and, in order to avoid missspecification errors related to the parameterization of a long memory multivariate model, we rely on wavelet analysis. More specifically, we retrieve the time series of wavelet coefficients for each volatility series for high frequency scales, using the Maximal Overlapping Discrete Wavelet transform and we apply Maximum Likelihood for a factor decomposition of the short-run covariance matrix. The empirical evidence…
Testing for Contagion: a Time-Scale Decomposition
2010
The aim of the paper is to test for financial contagion by estimating a simultaneous equation model subject to structural breaks. For this purpose, we use the Maximum Overlapping Discrete Wavelet Transform, MODWT, to decompose four asset returns into different scale components (each associated with a given frequency range). The decomposition will enable us to obtain the moment conditions necessary to (over)identify a structural form model with a single dummy and the one with multiple dummies capturing shifts in the co-movement of asset returns occurring during periods of financial turmoil. A Montecarlo simulation exercise shows that test based on a single dummy structural form model has goo…
Wavelet Analysis and Denoising: New Tools for Economists
2007
This paper surveys the techniques of wavelets analysis and the associated methods of denoising. The Discrete Wavelet Transform and its undecimated version, the Maximum Overlapping Discrete Wavelet Transform, are described. The methods of wavelets analysis can be used show how the frequency content of the data varies with time. This allow us to pinpoint in time such events as major structural breaks. The sparse nature of the wavelets representation also facilitates the process of noise reduction by nonlinear \textit{wavelet shrinkage,} which can be used to reveal the underlying trends in economic data. An application of these techniques to the UK real GDP (1873--2001) is described. The purpo…