Joint Bayesian separation and restoration of CMB from convolutional mixtures

We propose a Bayesian approach to joint source separation and restoration for astrophysical diffuse sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps in different directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques.Comment: 11 pages, 6 figures. Submitted to MNRA

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review

In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings

Neural networks and separation of Cosmic Microwave Background and astrophysical signals in sky maps

The Independent Component Analysis (ICA) algorithm is implemented as a neural network for separating signals of different origin in astrophysical sky maps. Due to its self-organizing capability, it works without prior assumptions on the signals, neither on their frequency scaling, nor on the signal maps themselves; instead, it learns directly from the input data how to separate the physical components, making use of their statistical independence. To test the capabilities of this approach, we apply the ICA algorithm on sky patches, taken from simulations and observations, at the microwave frequencies, that are going to be deeply explored in a few years on the whole sky, by the Microwave Anisotropy Probe (MAP) and by the {\sc Planck} Surveyor Satellite. The maps are at the frequencies of the Low Frequency Instrument (LFI) aboard the {\sc Planck} satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic diffuse emissions from thermal dust and synchrotron. We show that the ICA algorithm is able to recover each signal, with precision going from 10% for the Galactic components to percent for CMB; radio sources are almost completely recovered down to a flux limit corresponding to $0.7\sigma_{CMB}$, where $\sigma_{CMB}$ is the rms level of CMB fluctuations. The signal recovering possesses equal quality on all the scales larger then the pixel size. In addition, we show that the frequency scalings of the input signals can be partially inferred from the ICA outputs, at the percent precision for the dominant components, radio sources and CMB.Comment: 15 pages; 6 jpg and 1 ps figures. Final version to be published in MNRA

Planck pre-launch status : The Planck mission

Peer reviewe

DROP SIZE DISTRIBUTION IN SPRAYS BY IMAGE-PROCESSING

An automatic analysis system has been developed and used to analyze photographs obtained by high-speed microphotography, the final aim being to derive spatial resolved size distributions of drops in sprays. The problem of determining whether photographic images of particles are in focus or not is solved by obtaining a calibration of geometric parameters of particle images as functions both of the particle position in the camera's field of view and of the particle diameter. On the basis of the results of this calibration on the particular photographic system being used, the drops are automatically rejected or sized and counted. This is done through a procedure based on the geometrical characterization of drop images at different ranges of gray levels. The main body of such procedure is constituted by an algorithm of original design (connected components detection algorithm) which allows for the simultaneous detection of the boundaries of drop images at different gray levels and generates a hierarchical structure among them. Size distributions obtained by means of the procedure described in the paper offer significant reduction in experimental time as well as improvement in experimental accuracy, in relation to manual sizing and counting techniques

Neuronal contact guidance and YAP signaling on ultra-small nanogratings

Contact interaction of neuronal cells with extracellular nanometric features can be exploited to investigate and modulate cellular responses. By exploiting nanogratings (NGs) with linewidth from 500 nm down to 100 nm, we here study neurite contact guidance along ultra-small directional topographies. The impact of NG lateral dimension on the neuronal morphotype, neurite alignment, focal adhesion (FA) development and YAP activation is investigated in nerve growth factor (NGF)-differentiating PC12 cells and in primary hippocampal neurons, by confocal and live-cell total internal reflection fluorescence (TIRF) microscopy, and at molecular level. We demonstrate that loss of neurite guidance occurs in NGs with periodicity below 400 nm and correlates with a loss of FA lateral constriction and spatial organization. We found that YAP intracellular localization is modulated by the presence of NGs, but it is not sensitive to their periodicity. Nocodazole, a drug that can increase cell contractility, is finally tested for rescuing neurite alignment showing mild ameliorative effects. Our results provide new indications for a rational design of biocompatible scaffolds for enhancing nerve-regeneration processes

Source Separation In Noisy Astrophysical Images Modelled

Astrophysical radiation maps provide images which are superpositions of various cosmological components such as the cosmic microwave background (CMB) radiation, galactic dust, synchrotron, free-free emission and extragalactic radio sources. All these components are of great interest to cosmologists and in particular CMB, in addition to being the picture of the early universe, carries important information that would help us to choose between existing evolution theories of the universe. In this work we present a technique for the separation of these components in the presence of receiver noise. In contrast with most work in the literature, we make use of the spatial information in the images in the form of correlation between pixels which we model using Markov Random Fields. The spatial information is included in the MRF model through a Bayesian estimation framework. We provide comparisons with the results obtained by FastICA