A comprehensive study of the AGN feedback cycle in galaxy clusters from high resolution X-ray and radio observations

At the center of galaxy clusters, a dramatic interplay known as feedback cycle occurs between the hot intracluster medium (ICM) and the active galactic nucleus (AGN) of the central galaxy. The footprints of this interplay are evident from X-ray observations of the ICM, where X-ray cavities and shock fronts are associated with radio lobe emission tracing energetic AGN outbursts. While such jet activity reduces the efficiency of the hot gas to cool to lower temperatures, residual cooling can generate warm and cold gas clouds around the central galaxy. The condensed gas parcels can ultimately reach the core of the galaxy and be accreted by the AGN. This picture is the result of tremendous advances over the last three decades. Yet, a deeper understanding of the details of how the heating–cooling regulation is achieved and maintained is still missing. In this Thesis, we delve into key aspects of the feedback cycle. To this end, we leverage high-resolution (sub-arcsecond), multifrequency observations (mainly X-ray and radio) of several top-level facilities (e.g., Chandra, JVLA, VLBA, LOFAR). First, we investigate which conditions trigger a feedback response to gas cooling, by studying the properties of clusters where feedback is just about to start. Then, we focus on the details of how the AGN–ICM interaction progresses by examining cavity and shock heating in the cluster RBS797, an exemplary case of the jet feedback paradigm. Furthermore, we explore the importance of shock heating and the coupling of distinct jet power regimes (i.e., FRII, FRI and FR0 radio galaxies) to the environment. Ultimately, as heating models rely on the connection between the direct evidence (the jets) and the smoking gun (the X-ray cavities) of feedback, we examine the cases in which these two are dramatically misaligned.

High-resolution stratigraphy and seismic site characterization of late Quaternary paleovalley systems (Adriatic coastal plain, Italy)

In highly urbanized coastal lowlands, effective site characterization is crucial for assessing seismic risk. It requires a comprehensive stratigraphic analysis of the shallow subsurface, coupled with the precise assessment of the geophysical properties of buried deposits. In this context, late Quaternary paleovalley systems, shallowly buried fluvial incisions formed during the Late Pleistocene sea-level fall and filled during the Holocene sea-level rise, are crucial for understanding seismic amplification due to their soft sediment infill and sharp lithologic contrasts. In this research, we conducted high-resolution stratigraphic analyses of two regions, the Pescara and Manfredonia areas along the Adriatic coastline of Italy, to delineate the geometries and facies architecture of two paleovalley systems. Furthermore, we carried out geophysical investigations to characterize the study areas and perform seismic response analyses. We tested the microtremor-based horizontal-to-vertical spectral ratio as a mapping tool to reconstruct the buried paleovalley geometries. We evaluated the relationship between geological and geophysical data and identified the stratigraphic surfaces responsible for the observed resonances. To perform seismic response analysis of the Pescara paleovalley system, we integrated the stratigraphic framework with microtremor and shear wave velocity measurements. The seismic response analysis highlights strong seismic amplifications in frequency ranges that can interact with a wide variety of building types. Additionally, we explored the applicability of artificial intelligence in performing facies analysis from borehole images. We used a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age to outline a novel, deep-learning-based approach for performing automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. We propose an automated model to rapidly characterize sediment cores, reproducing the sedimentologist's interpretation, and providing guidance for stratigraphic correlation and subsurface reconstructions.

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.

Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Avaliação do potencial das imagens quickbird ortorretificadas para o mapeamento das propriedades localizadas na área de expansão e vazios urbanos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detecção de rodovias em imagens digitais de alta resolução com o uso da teoria de morfologia matemática

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de um Modelo de Difusão Anisotrópica e Morfologia Matemática na Detecção de Feições Cartográficas

Pós-graduação em Ciências Cartográficas - FCT

Metodologia automática para extração de cruzamentos de rodovias em imagens de alta resolução

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geoestística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Espacialização do biovolume de plantas aquáticas submersas a partir da integração de dados obtidos por sensores remotos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação da aplicabilidade de sistema computacional de visualização de imagens aéreas de alta resolução em tempo real e multitemporal à degradação do rio Corumbataí

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

HTS and HTSn: New shape descriptors based on Hough transform statistics

With the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose a new shape description method, called Hough Transform Statistics (HTS), which uses statistics from the Hough space to characterize the shape of objects or regions in digital images. A modified version of this method, called Hough Transform Statistics neighborhood (HTSn), is also presented. Experiments carried out on three popular public image databases showed that the HTS and HTSn descriptors are robust, since they presented precision-recall results much better than several other well-known shape description methods. When compared to Beam Angle Statistics (BAS) method, a shape description method that inspired their development, both the HTS and the HTSn methods presented inferior results regarding the precision-recall criterion, but superior results in the processing time and multiscale separability criteria. The linear complexity of the HTS and the HTSn algorithms, in contrast to BAS, make them more appropriate for shape analysis in high-resolution image retrieval tasks when very large databases are used, which are very common nowadays. (C) 2014 Elsevier Inc. All rights reserved.

Imagen de hipertrofia bilateral del proceso coronoides mandibular a través de la tomografía computarizada cone-beam

The hypertrophy of the mandibular coronoid process is a rare condition, progressive and of unknown etiology, characterized by the over development of the coronoid process determining their size increased, resulting in progressive and painful limitation of mouth opening. The mandibular hypomobility is a present symptom in several diseases and associates it to a specific cause requires detailed clinical and imaginologic examination. The cone-beam computed tomography has the potential to provide complementary informations above than conventional radiography. It also has the advantage of eliminate overlaps, provide high resolution image, helping not only in diagnosis, but also in planning the surgical procedure. Here we report a case of male patient, who appeared at the clinic for an evaluation of a trismus. He had no pain, only discomfort for progressive limitation of mouth opening. On physical examination, only a limited mouth opening has been found. Although the movements of laterality and protrusión were posible, they were very limited. Cone-beam computed tomography examination was performed, that showed an image compatible with bilateral hypertrophy of the mandibular coronoid process.

A multifrequency study of the Lockman Hole region: preparing for a LOFAR deep survey

In this thesis, we have presented two deep 1.4 GHz and 345 MHz overlapping surveys of the Lockman Hole field taken with the Westerbork Synthesis Radio Telescope. We extracted a catalogue of ~6000 radio sources from the 1.4 GHz mosaic down to a flux limit of ~55 μJy and a catalogue of 334 radio sources down to a flux limit of ~4 mJy from the inner 7 sq. degree region of the 345 MHz image. The extracted catalogues were used to derive the source number counts at 1.4 GHz and at 345 MHz. The source counts were found to be fully consistent with previous determinations. In particular the 1.4 GHz source counts derived by the present sample provide one of the most statistically robust determinations in the flux range 0.1 < S < 1 mJy. During the commissioning program of the LOFAR telescope, the Lockman Hole field was observed at 58 MHz and 150 MHz. The 150 MHz LOFAR observation is particularly relevant as it allowed us to obtain the first LOFAR flux calibrated high resolution image of a deep field. From this image we extracted a preliminary source catalogue down to a flux limit of ~15 mJy (~10σ), that can be considered complete down to 20‒30 mJy. A spectral index study of the mJy sources in the Lockman Hole region, was performed using the available catalogues ( 1.4 GHz, 345 MHz and 150 MHz) and a deep 610 MHz source catalogue available from the literature (Garn et al. 2008, 2010).