Aliaga, R. J. (2017). Real-Time Estimation of Zero Crossings of Sampled Signals for Timing Using Cubic Spline Interpolation. IEEE Trans. Nucl. Sci., 64(8), 2414–2422.
Abstract: A scheme is proposed for hardware estimation of the location of zero crossings of sampled signals with subsample resolution for timing applications, which consists of interpolating the signal with a cubic spline near the zero crossing and then finding the root of the resulting polynomial. An iterative algorithm based on the bisection method is presented that obtains one bit of the result per step and admits an efficient digital implementation using fixed-point representation. In particular, the root estimation iteration involves only two additions, and the initial values can be obtained from finite impulse response (FIR) filters with certain symmetry properties. It is shown that this allows online real-time estimation of timestamps in free-running sampling detector systems with improved accuracy with respect to the more common linear interpolation. The method is evaluated with simulations using ideal and real timing signals, and estimates are given for the resource usage and speed of its implementation.
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Albiol, A., Corbi, A., & Albiol, F. (2017). Automatic intensity windowing of mammographic images based on a perceptual metric. Med. Phys., 44(4), 1369–1378.
Abstract: Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at . Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Coleiro, A., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2017). All-sky search for high-energy neutrinos from gravitational wave event GW170104 with the ANTARES neutrino telescope. Eur. Phys. J. C, 77(12), 911–7pp.
Abstract: Advanced LIGO detected a significant gravitational wave signal (GW170104) originating from the coalescence of two black holes during the second observation run on January 4th, 2017. Anall-sky high-energy neutrino follow-up search has been made using data from the Antares neutrino telescope, including both upgoing and downgoing events in two separate analyses. No neutrino candidates were found within +/- 500 s around the GW event time nor any time clustering of events over an extended time window of +/- 3 months. The non-detection is used to constrain isotropic-equivalent high-energy neutrino emission from GW170104 to less than similar to 1.2 x 10(55) erg for a E-2 spectrum. This constraint is valid in the energy range corresponding to the 5-95% quantiles of the neutrino flux [3.2 TeV; 3.6 PeV], if the GW emitter was below the Antares horizon at the alert time.
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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., Tönnis, C., et al. (2017). Search for high-energy neutrinos from bright GRBs with ANTARES. Mon. Not. Roy. Astron. Soc., 469(1), 906–915.
Abstract: Gamma-ray bursts are thought to be sites of hadronic acceleration, thus neutrinos are expected from the decay of charged particles, produced in p gamma interactions. The methods and results of a search for muon neutrinos in the data of the ANTARES neutrino telescope from four bright GRBs (GRB 080916C, GRB 110918A, GRB 130427A and GRB 130505A) observed between 2008 and 2013 are presented. Two scenarios of the fireball model have been investigated: the internal shock scenario, leading to the production of neutrinos with energies mainly above 100 TeV, and the photospheric scenario, characterized by a low-energy component in neutrino spectra due to the assumption of neutrino production closer to the central engine. Since no neutrino events have been detected in temporal and spatial coincidence with these bursts, upper limits at 90 per cent confidence level on the expected neutrino fluxes are derived. The non-detection allows for directly constraining the bulk Lorentz factor of the jet Gamma and the baryon loading f(p).
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