Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]

Signal processing for microwave imaging systems with very sparse array

This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv

Fast retrieval of weather analogues in a multi-petabyte meteorological archive

The European Centre for Medium-Range Weather Forecasts (ECMWF) manages the largest archive of meteorological data in the world. At the time of writing, it holds around 300 petabytes and grows at a rate of 1 petabyte per week. This archive is now mature, and contains valuable datasets such as several reanalyses, providing a consistent view of the weather over several decades. Weather analogue is the term used by meteorologists to refer to similar weather situations. Looking for analogues in an archive using a brute force approach requires data to be retrieved from tape and then compared to a user-provided weather pattern, using a chosen similarity measure. Such an operation would be very long and costly. In this work, a wavelet-based fingerprinting scheme is proposed to index all weather patterns from the archive, over a selected geographical domain. The system answers search queries by computing the fingerprint of the query pattern and looking for close matched in the index. Searches are fast enough that they are perceived as being instantaneous. A web-based application is provided, allowing users to express their queries interactively in a friendly and straightforward manner by sketching weather patterns directly in their web browser. Matching results are then presented as a series of weather maps, labelled with the date and time at which they occur. The system has been deployed as part of the Copernicus Climate Data Store and allows the retrieval of weather analogues from ERA5, a 40-years hourly reanalysis dataset. Some preliminary results of this work have been presented at the International Conference on Computational Science 2018 (Raoult et al. (2018))

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented

Picoflare jets power the solar wind emerging from a coronal hole on the Sun.

Coronal holes are areas on the Sun with open magnetic field lines. They are a source region of the solar wind, but how the wind emerges from coronal holes is not known. We observed a coronal hole using the Extreme Ultraviolet Imager on the Solar Orbiter spacecraft. We identified jets on scales of a few hundred kilometers, which last 20 to 100 seconds and reach speeds of ~100 kilometers per second. The jets are powered by magnetic reconnection and have kinetic energy in the picoflare range. They are intermittent but widespread within the observed coronal hole. We suggest that such picoflare jets could produce enough high-temperature plasma to sustain the solar wind and that the wind emerges from coronal holes as a highly intermittent outflow at small scales

Picoflare jets power the solar wind emerging from a coronal hole on the Sun

Coronal holes are areas on the Sun with open magnetic field lines. They are a source region of the solar wind, but how the wind emerges from coronal holes is not known. We observed a coronal hole using the Extreme Ultraviolet Imager on the Solar Orbiter spacecraft. We identified jets on scales of a few hundred kilometers, which last 20 to 100 seconds and reach speeds of ~100 kilometers per second. The jets are powered by magnetic reconnection and have kinetic energy in the picoflare range. They are intermittent but widespread within the observed coronal hole. We suggest that such picoflare jets could produce enough high-temperature plasma to sustain the solar wind and that the wind emerges from coronal holes as a highly intermittent outflow at small scales.Comment: This is the author's version of the work. The definitive version was published in Science on 24 August 202