Hybrid Adaptive Filter development for the minimisation of transient fluctuations superimposed on electrotelluric field recordings mainly by magnetic storms

The method of Hybrid Adaptive Filtering (HAF) aims to recover the recorded electric field signals from anomalies of magnetotelluric origin induced mainly by magnetic storms. An adaptive filter incorporating neuro-fuzzy technology has been developed to remove any significant distortions from the equivalent magnetic field signal, as retrieved from the original electric field signal by reversing the magnetotelluric method. Testing with further unseen data verifies the reliability of the model and demonstrates the effectiveness of the HAF method

MONOFRACTAL AND MULTIFRACTAL ANALYSIS IN SHORT - TERM TIME DYNAMICS OF ULF GEOMAGNETIC FIELD MEASURED IN CRETE, GREECE

In this work, a monofractal and multifractal characterization of the short-term time dynamical fluctuations of the ultra low frequency (ULF) geomagnetic field, measured by one station installed in Creete, Greece, has been carried out. Time scale properties of the three ULF geomagnetic components, two horizontal (x, y) and one vertical (z) have been analyzed through the power spectral density, Higuchi method and Hurst R/S analysis. Results point out the presence of fractal features expressing long-range time correlation with scaling coefficients, which are the clue of persistent mechanism. Using a set of multifractal parameters, defined from the shape of the multifractal spectrum, it has been observed that the degree of multifractality, that characterizes the original signals, is "weaker" if compared to the residual signals, obtained from the original ones after removing the four observed periodicities (24-, 12-, 8- and 6-h periodicties). Furthermore the horizontal χ and y components have revealed to be less multifractal than the vertical z-component

CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

Succinct Indices for Range Queries with applications to Orthogonal Range Maxima

We consider the problem of preprocessing $N$ points in 2D, each endowed with a priority, to answer the following queries: given a axis-parallel rectangle, determine the point with the largest priority in the rectangle. Using the ideas of the \emph{effective entropy} of range maxima queries and \emph{succinct indices} for range maxima queries, we obtain a structure that uses O(N) words and answers the above query in $O(\log N \log \log N)$ time. This is a direct improvement of Chazelle's result from FOCS 1985 for this problem -- Chazelle required $O(N/\epsilon)$ words to answer queries in $O((\log N)^{1+\epsilon})$ time for any constant $\epsilon > 0$.Comment: To appear in ICALP 201

Digital Avatars for Older People’s Care

Es el preprint de: Bertoa M.F., Moreno N., Perez-Vereda A., Bandera D., Álvarez-Palomo J.M., Canal C. (2020) Digital Avatars for Older People’s Care. In: García-Alonso J., Fonseca C. (eds) Gerontechnology. IWoG 2019. Communications in Computer and Information Science, vol 1185. Springer, Cham. doi:10.1007/978-3-030-41494-8_6.The continuous increase in life expectancy poses a challenge for health systems in modern societies, especially with respect to older people living in rural low-populated areas, both in terms of isolation and difficulty to access and communicate with health services. In this paper, we address these issues by applying the Digital Avatars framework to Gerontechnology. Building on our previous work on mobile and social computing, in particular the People as a Service model, Digital Avatars make intensive use of the capabilities of current smartphones to collect information about their owners, and applies techniques of Complex Event Processing extended with uncertainty for inferring the habits and preferences of the user of the phone and building with them a virtual profile. These virtual profiles allow to monitor the well-being and quality of life of older adults, reminding pharmacological treatments and home health testings, and raising alerts when an anomalous situation is detected.This work has been funded by the Spanish Government under grant PGC2018-094905-B-100