909 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
A Functional Wavelet-Kernel Approach for Continuous-time Prediction
We consider the prediction problem of a continuous-time stochastic process on
an entire time-interval in terms of its recent past. The approach we adopt is
based on functional kernel nonparametric regression estimation techniques where
observations are segments of the observed process considered as curves. These
curves are assumed to lie within a space of possibly inhomogeneous functions,
and the discretized times series dataset consists of a relatively small,
compared to the number of segments, number of measurements made at regular
times. We thus consider only the case where an asymptotically non-increasing
number of measurements is available for each portion of the times series. We
estimate conditional expectations using appropriate wavelet decompositions of
the segmented sample paths. A notion of similarity, based on wavelet
decompositions, is used in order to calibrate the prediction. Asymptotic
properties when the number of segments grows to infinity are investigated under
mild conditions, and a nonparametric resampling procedure is used to generate,
in a flexible way, valid asymptotic pointwise confidence intervals for the
predicted trajectories. We illustrate the usefulness of the proposed functional
wavelet-kernel methodology in finite sample situations by means of three
real-life datasets that were collected from different arenas
Modeling the long-range wave propagation by a split-step wavelet method
International audienceA split-step wavelet method for simulating the long-range wave propagation is introduced. It is based on the fast wavelet transform. Compared to the split-step Fourier method, this method improves the computation efficiency while keeping a good accuracy. The propagation is performed iteratively by means of a pre-computed matrix containing the individual propagations of the wavelets. A fast computation method of this matrix is also presented. For the radiowave propagation in the low troposphere, a local image method is proposed to account for an impedance ground. Inhomogeneous atmospheres and irregular grounds are also considered. Finally, numerical tests of long-range propagations are performed to show the accuracy and time efficiency of this method
Multiresolution Approximation of a Bayesian Inverse Problem using Second-Generation Wavelets
Bayesian approaches are one of the primary methodologies to tackle an inverse
problem in high dimensions. Such an inverse problem arises in hydrology to
infer the permeability field given flow data in a porous media. It is common
practice to decompose the unknown field into some basis and infer the
decomposition parameters instead of directly inferring the unknown. Given the
multiscale nature of permeability fields, wavelets are a natural choice for
parameterizing them. This study uses a Bayesian approach to incorporate the
statistical sparsity that characterizes discrete wavelet coefficients. First,
we impose a prior distribution incorporating the hierarchical structure of the
wavelet coefficient and smoothness of reconstruction via scale-dependent
hyperparameters. Then, Sequential Monte Carlo (SMC) method adaptively explores
the posterior density on different scales, followed by model selection based on
Bayes Factors. Finally, the permeability field is reconstructed from the
coefficients using a multiresolution approach based on second-generation
wavelets. Here, observations from the pressure sensor grid network are computed
via Multilevel Adaptive Wavelet Collocation Method (AWCM). Results highlight
the importance of prior modeling on parameter estimation in the inverse
problem
Historical forest biomass dynamics modelled with Landsat spectral trajectories
Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin
Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method
As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/
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