André Mora

Gaia Data Release 1 - Astrometry: one billion positions, two million proper motions and parallaxes

Gaia Data Release 1 (Gaia DR1) contains astrometric results for more than 1 billion stars brighter than magnitude 20.7 based on observations collected by the Gaia satellite during the first 14 months of its operational phase. We give a brief overview of the astrometric content of the data release and of the model assumptions, data processing, and validation of the results. For stars in common with the Hipparcos and Tycho-2 catalogues, complete astrometric single-star solutions are obtained by incorporating positional information from the earlier catalogues. For other stars only their positions are obtained by neglecting their proper motions and parallaxes. The results are validated by an analysis of the residuals, through special validation runs, and by comparison with external data. Results. For about two million of the brighter stars (down to magnitude ~11.5) we obtain positions, parallaxes, and proper motions to Hipparcos-type precision or better. For these stars, systematic errors depending e.g. on position and colour are at a level of 0.3 milliarcsecond (mas). For the remaining stars we obtain positions at epoch J2015.0 accurate to ~10 mas. Positions and proper motions are given in a reference frame that is aligned with the International Celestial Reference Frame (ICRF) to better than 0.1 mas at epoch J2015.0, and non-rotating with respect to ICRF to within 0.03 mas/yr. The Hipparcos reference frame is found to rotate with respect to the Gaia DR1 frame at a rate of 0.24 mas/yr. Based on less than a quarter of the nominal mission length and on very provisional and incomplete calibrations, the quality and completeness of the astrometric data in Gaia DR1 are far from what is expected for the final mission products. The results nevertheless represent a huge improvement in the available fundamental stellar data and practical definition of the optical reference frame.

Gaia Data Release 2 - The astrometric solution

Context. Gaia Data Release 2 (Gaia DR2) contains results for 1693 million sources in the magnitude range 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 22 months of its operational phase. Aims. We describe the input data, models, and processing used for the astrometric content of Gaia DR2, and the validation of these resultsperformed within the astrometry task. Methods. Some 320 billion centroid positions from the pre-processed astrometric CCD observations were used to estimate the five astrometric parameters (positions, parallaxes, and proper motions) for 1332 million sources, and approximate positions at the reference epoch J2015.5 for an additional 361 million mostly faint sources. These data were calculated in two steps. First, the satellite attitude and the astrometric calibration parameters of the CCDs were obtained in an astrometric global iterative solution for 16 million selected sources, using about 1% of the input data. This primary solution was tied to the extragalactic International Celestial Reference System (ICRS) by means of quasars. The resulting attitude and calibration were then used to calculate the astrometric parameters of all the sources. Special validation solutions were used to characterise the random and systematic errors in parallax and proper motion. Results. For the sources with five-parameter astrometric solutions, the median uncertainty in parallax and position at the reference epoch J2015.5 is about 0.04 mas for bright (G < 14 mag) sources, 0.1 mas at G = 17 mag, and 0.7 masat G = 20 mag. In the proper motion components the corresponding uncertainties are 0.05, 0.2, and 1.2 mas yr−1, respectively.The optical reference frame defined by Gaia DR2 is aligned with ICRS and is non-rotating with respect to the quasars to within 0.15 mas yr−1. From the quasars and validation solutions we estimate that systematics in the parallaxes depending on position, magnitude, and colour are generally below 0.1 mas, but the parallaxes are on the whole too small by about 0.03 mas. Significant spatial correlations of up to 0.04 mas in parallax and 0.07 mas yr−1 in proper motion are seen on small (< 1 deg) and intermediate (20 deg) angular scales. Important statistics and information for the users of the Gaia DR2 astrometry are given in the appendices.

FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

The main goal of information fusion is to combine heterogeneous information to obtain a single composite of potential comparable alternative solutions that can be classified and ranked. The crux of information fusion, which is a type of data fusion, is threefold: (i) data must be comparable and numerical, using some normalization process; (ii) imprecision in data must be taken into consideration; (iii) an appropriate aggregation function to combine values into a single score must be selected. Recently, computational intelligence concepts and techniques to perform data/information fusion are emerging as suitable tools. Although with a different perspective, another field where much work has also been done for combining heterogeneous information is multi-criteria decision-making. In general, multi-criteria problems are modelled by choosing a set of relevant criteria - usually dealing with heterogeneous data - that have to be aggregated (i.e. fused) to obtain a single rating for each candidate alternative. In this paper we propose an algorithm for data/information fusion, which includes concepts from multi-criteria decision-making and computational intelligence, specifically, fuzzy multi-criteria decision-making and mixture aggregation operators with weighting functions. The application field of interest for this work is safe spacecraft landing with hazard avoidance; hence two existing hazard maps will be used to illustrate the versatility of the algorithm.

Optical character recognition using automatically generated Fuzzy classifiers

Character recognition using Fuzzy classifiers has been showing very promising results. However, the definition of the membership functions together with the design of the classification rules is a challenging task even considering just the 10 digits and 23 characters of the Roman alphabet. In this paper we present a solution for the semi-automatic design of a Fuzzy classifier for letters and digits to be applied on the automatic recognition of cars license plates on unstructured conditions. Based on a training set of fuzzified examples of measures, taken from digital images of single characters, the CART algorithm learns the rules that regulate the design of the different characters and generates fuzzy rules that implement the fuzzy classifiers in a completely automatic way. After, a fuzzy inference engine executes the rules to obtain the characters classification. To take advantage of syntactical correction, a hierarchical classifier with two layers of classifiers is proposed: one classifier distinguishes between letters or digits; the second layer classifies either the letters or the digits. The performance achieved by the two-layer classifier is shown and discussed.

Monitoring of Drusen and Geographic Atrophy Area Size after Cataract Surgery Using the MD3RI Tool for Computer-Aided Contour Drawing

Background/Aims: To monitor possible changes in the cumulated drusen or geographic atrophy area size (CDGAS) of nonexudative age-related macular degeneration (AMD) in patients before and after cataract surgery, using a new tool for computer-aided image quantification. Methods: Randomized, prospective, clinical trial. 54 patients with cataract and nonexudative AMD were randomly assigned into an early surgery group (ES = 28) and a control group (CO = 26) with a 6-month delay of surgery. CDGAS was determined with the MD3RI tool for contour drawing in a central region of digitized fundus photographs, measuring 3,000 µm in diameter. To evaluate CDGAS progression, differences in pixels and square millimeters were calculated by equivalent tests. Results: Forty-nine patients completed the visits over the 12-month period (ES = 27 and CO = 22). Mean pixel values increased from 201.5 (11.33 × 10–3 mm2) to 202.7 (11.39 × 10–3 mm2) in the ES group and from 191.6 (10.77 × 10–3 mm2) to 194.6 (10.94 × 10–3 mm2) in the CO group. Finally, equivalence of CDGAS differences between ES and CO could be demonstrated. No exudative AMD was recorded during the study period. Conclusion: In our cohorts, no significant changes were found in CDGAS 12 months after cataract surgery. The MD3RI software could serve as an efficient, precise and objective tool for AMD quantification and monitoring in future trials.

Gaia Data Release 1. The archive visualisation service

Context: The first Gaia data release (DR1) delivered a catalogue of astrometry and photometry for over a billion astronomical sources. Within the panoply of methods used for data exploration, visualisation is often the starting point and even the guiding reference for scientific thought. However, this is a volume of data that cannot be efficiently explored using traditional tools, techniques, and habits. Aims: We aim to provide a global visual exploration service for the Gaia archive, something that is not possible out of the box for most people. The service has two main goals. The first is to provide a software platform for interactive visual exploration of the archive contents, using common personal computers and mobile devices available to most users. The second aim is to produce intelligible and appealing visual representations of the enormous information content of the archive. Methods: The interactive exploration service follows a client-server design. The server runs close to the data, at the archive, and is responsible for hiding as far as possible the complexity and volume of the Gaia data from the client. This is achieved by serving visual detail on demand. Levels of detail are pre-computed using data aggregation and subsampling techniques. For DR1, the client is a web application that provides an interactive multi-panel visualisation workspace as well as a graphical user interface. Results: The Gaia archive Visualisation Service offers a web-based multi-panel interactive visualisation desktop in a browser tab. It currently provides highly configurable 1D histograms and 2D scatter plots of Gaia DR1 and the Tycho-Gaia Astrometric Solution (TGAS) with linked views. An innovative feature is the creation of ADQL queries from visually defined regions in plots. [abridged]

The Web and the New Generation of Medical Information Systems

The advent of the Internet has allowed physicians, patients and other healthcare providers to access an unprecedented volume of information in an easy and cost-effective way. This new scenario opens up new frontiers to medical activity and changes the way physicians act with obvious repercussions on healthcare services, quality and effectiveness. However, despite the perspectives that information systems offer to healthcare professionals, their implementation is not easy. In fact, there are many difficult problems to overcome in the design of medical information systems for efficient knowledge extraction. The huge amount of information on patients accumulated in modern healthcare institutions is difficult to manage and often not as useful as it should be, because it is either inaccessible, too slow to be clinically used or too difficult to access justifying the need for more effective information management techniques and statistical analysis for knowledge discovery and extraction. The privacy of information required both for legal and ethical issues, and the quality and reliability of information are also important issues in this kind of systems. Also, in many real-world situations information is still kept on paper due to the unavailability of adequate computer support or to the traditional technophobia of many users, pushing the need for better, more appealing and user-friendly human interfaces. The interoperability of information between different healthcare institutions with different cultures and languages is another important problem that must be overcome by the use of adequate technology. New technologies such as mobile computing and wireless internet access also open up new frontiers in medical information systems by enabling the establishment of ubiquitous information networks that can be accessed virtually anywhere and anytime.

Fuzzy-fusion approach for land cover classification

The use of computational intelligent techniques for feature extraction and classification from earth observation satellite images, like Landsat multispectral images, can contribute to improve remote sensing analysis. Image fusion techniques are applied to fuse the spectral images into a higher-level image of the land cover distribution. In this paper we propose a fuzzy-fusion inference approach for satellite image classification based on a fuzzy process, which uses both a hybrid method to train the classifier and reinforcement aggregation operators in the inference scheme. The approach was tested with land cover maps for the district of Mandimba of the Niassa province, Mozambique and was validated against an expert classification and then with Decision trees and Artificial Neural Networks.

Real-time image recovery using temporal image fusion

In computer vision systems an unpredictable image corruption can have significant impact on its usability. Image recovery methods for partial image damage, in particular in moving scenarios, can be crucial for recovering corrupted images. In these situations, image fusion techniques can be successfully applied to congregate information taken at different instants and from different points-of-view to recover damaged parts. In this article we propose a technique for temporal and spatial image fusion, based on fuzzy classification, which allows partial image recovery upon unexpected defects without user intervention. The method uses image alignment techniques and duplicated information from previous images to create fuzzy confidence maps. These maps are then used to detect damaged pixels and recover them using information from previous frames.

Evaluative Study of PSO/Snake Hybrid Algorithm and Gradient Path Labeling for Calculating Solar Differential Rotation

PSO/Snake hybrid algorithm is a merge of particle swarm optimization PSO, a successful population based optimization technique, and the Snake model, a specialized image processing algorithm. In the PSO/Snake hybrid algorithm each particle in the population represents only a portion of the solution and the population, as a whole, will converge to the final complete solution. In this model there is a one-to-one relation between Snake model snaxels and PSO particles with the PSOs kinematics being modified accordingly to the snake model dynamics. This paper provides an evaluative study on the performance of the customized PSO/Snake algorithm in solving a real-world problem from astrophysics domain and comparing the results with Gradient Path Labeling GPL image segmentation algorithm. The GPL algorithm segments the image into regions according to its intensity from where the relevant ones can be selected based on their features. A specific type of solar features called coronal bright points have been tracked in a series of solar images using both algorithms and the solar differential rotation is calculated accordingly. The final results are compared with those already reported in the literature.