Wavelet design by means of multi-objective GAs for motor imagery EEG analysis

Wavelet-based analysis has been broadly used in the study of brain-computer interfaces (BCI), but in most cases these wavelet functions have not been designed taking into account the requirements of this field. In this study we propose a method to automatically generate wavelet-like functions by means of genetic algorithms. Results strongly indicate that it is possible to generate (evolve) wavelet functions that improve the classification accuracy compared to other well-known wavelets (e.g. Daubechies and Coiflets)

Modeling IoT-aware Business Processes - A State of the Art Report

This research report presents an analysis of the state of the art of modeling Internet of Things (IoT)-aware business processes. IOT links the physical world to the digital world. Traditionally, we would find information about events and processes in the physical world in the digital world entered by humans and humans using this information to control the physical world. In the IoT paradigm, the physical world is equipped with sensors and actuators to create a direct link with the digital world. Business processes are used to coordinate a complex environment including multiple actors for a common goal, typically in the context of administrative work. In the past few years, we have seen research efforts on the possibilities to model IoT- aware business processes, extending process coordination to real world entities directly. This set of research efforts is relatively small when compared to the overall research effort into the IoT and much of the work is still in the early research stage. To create a basis for a bridge between IoT and BPM, the goal of this report is to collect and analyze the state of the art of existing frameworks for modeling IoT-aware business processes.Comment: 42 page

Advanced Forward Modeling and Inversion of Stokes Profiles Resulting from the Joint Action of the Hanle and Zeeman Effects

A big challenge in solar and stellar physics in the coming years will be to decipher the magnetism of the solar outer atmosphere (chromosphere and corona) along with its dynamic coupling with the magnetic fields of the underlying photosphere. To this end, it is important to develop rigorous diagnostic tools for the physical interpretation of spectropolarimetric observations in suitably chosen spectral lines. Here we present a computer program for the synthesis and inversion of Stokes profiles caused by the joint action of atomic level polarization and the Hanle and Zeeman effects in some spectral lines of diagnostic interest, such as those of the He I 10830 A and D_3 multiplets. It is based on the quantum theory of spectral line polarization, which takes into account all the relevant physical mechanisms and ingredients (optical pumping, atomic level polarization, Zeeman, Paschen-Back and Hanle effects). The influence of radiative transfer on the emergent spectral line radiation is taken into account through a suitable slab model. The user can either calculate the emergent intensity and polarization for any given magnetic field vector or infer the dynamical and magnetic properties from the observed Stokes profiles via an efficient inversion algorithm based on global optimization methods. The reliability of the forward modeling and inversion code presented here is demonstrated through several applications, which range from the inference of the magnetic field vector in solar active regions to determining whether or not it is canopy-like in quiet chromospheric regions. This user-friendly diagnostic tool called "HAZEL" (from HAnle and ZEeman Light) is offered to the astrophysical community, with the hope that it will facilitate new advances in solar and stellar physics.Comment: 62 pages, 19 figures, 3 tables. Accepted for publication in Ap

The Minimum Description Length Principle and Model Selection in Spectropolarimetry

It is shown that the two-part Minimum Description Length Principle can be used to discriminate among different models that can explain a given observed dataset. The description length is chosen to be the sum of the lengths of the message needed to encode the model plus the message needed to encode the data when the model is applied to the dataset. It is verified that the proposed principle can efficiently distinguish the model that correctly fits the observations while avoiding over-fitting. The capabilities of this criterion are shown in two simple problems for the analysis of observed spectropolarimetric signals. The first is the de-noising of observations with the aid of the PCA technique. The second is the selection of the optimal number of parameters in LTE inversions. We propose this criterion as a quantitative approach for distinguising the most plausible model among a set of proposed models. This quantity is very easy to implement as an additional output on the existing inversion codes.Comment: Accepted for publication in the Astrophysical Journa

Hierarchy and Competition in CSCW applications: Model and case study

CSCW applications need to adapt themselves to the functional and organizational structures of people that use them. However they do not usually support division in groups with a certain hierarchical structure among them. In this paper, we propose and study a theoretical model of groupware appliations that reflects those hierarchical interactions. The proposed model is also intended to evaluate the effects in performance derived from competitive and collaborative relationships among the components of a hierarchy of groups. In order to demonstrate the above ideas, a groupware game, called Alymod, was designed and implemented using a modified version of a well-known CSCW Toolkit, namely Groupkit. Groupkit was modified in order to support group interactions in the same CSCW application. In Alymod, participants compete or collaborate within a hierarchical structure to achieve a common goal (completing gaps in a text, finishing numerical series, resolving University course examinations, etc.).Publicad

Three dimensionality in the wake of the flow around a circular cylinder at Reynolds number 5000

The turbulent flow around a circular cylinder has been investigated at Re=5000Re=5000 using direct numerical simulations. Low frequency behavior, vortex undulation, vortex splitting, vortex dislocations and three dimensional flow within the wake were found to happen at this flow regime. In order to successfully capture the wake three dimensionality, different span-wise lengths were considered. It was found that a length LZ=2pDLZ=2pD was enough to capture this behavior, correctly predicting different aspects of the flow such as drag coefficient, Strouhal number and pressure and velocity distributions when compared to experimental values. Two instability mechanisms were found to coexist in the present case study: a global type instability originating in the shear layer, which shows a characteristic frequency, and a convective type instability that seems to be constantly present in the near wake. Characteristics of both types of instabilities are identified and discussed in detail. As suggested by Norberg, a resonance-type effect takes place in the vortex formation region, as the coexistence of both instability mechanisms result in distorted vortex tubes. However, vortex coherence is never lost within the wake.Peer ReviewedPostprint (author's final draft

Planet cartography with neural learned regularization

Finding potential life harboring exo-Earths is one of the aims of exoplanetary science. Detecting signatures of life in exoplanets will likely first be accomplished by determining the bulk composition of the planetary atmosphere via reflected/transmitted spectroscopy. However, a complete understanding of the habitability conditions will surely require mapping the presence of liquid water, continents and/or clouds. Spin-orbit tomography is a technique that allows us to obtain maps of the surface of exoplanets around other stars using the light scattered by the planetary surface. We leverage the potential of deep learning and propose a mapping technique for exo-Earths in which the regularization is learned from mock surfaces. The solution of the inverse mapping problem is posed as a deep neural network that can be trained end-to-end with suitable training data. We propose in this work to use methods based on the procedural generation of planets, inspired by what we found on Earth. We also consider mapping the recovery of surfaces and the presence of persistent cloud in cloudy planets. We show that the a reliable mapping can be carried out with our approach, producing very compact continents, even when using single passband observations. More importantly, if exoplanets are partially cloudy like the Earth is, we show that one can potentially map the distribution of persistent clouds that always occur on the same position on the surface (associated to orography and sea surface temperatures) together with non-persistent clouds that move across the surface. This will become the first test one can perform on an exoplanet for the detection of an active climate system. For small rocky planets in the habitable zone of their stars, this weather system will be driven by water, and the detection can be considered as a strong proxy for truly habitable conditions.Comment: 12 pages, 9 figures, accepted for publication in A&A, code on https://github.com/aasensio/neural_exocartograph