XXIV Congreso Argentino de Hipertensión Arterial en Mendoza : abril de 2017

Fil: Renna, Nicolás F..Fil: Miatello, Roberto M.

La investigación en la formación médica

Fil: Renna, Nicolás F.. Universidad Nacional de Cuyo. Facultad de Ciencias MédicasFil: Miatello, Roberto M. . Universidad Nacional de Cuyo. Facultad de Ciencias Médica

Review of Reactors with Potential Use in Thermochemical Energy Storage in Concentrated Solar Power Plants

The aim of this study is to perform a review of the state-of-the-art of the reactors available in the literature, which are used for solid-gas reactions or thermal decomposition processes around 1000 ºC that could be further implemented for thermochemical energy storage in CSP (concentrated solar power) plants, specifically for SPT (solar power tower) technology. Both direct and indirect systems can be implemented, with direct and closed systems being the most studied ones. Among direct and closed systems, the most used configuration is the stacked bed reactor, with the fixed bed reactor being the most frequent option. Out of all of the reactors studied, almost 70% are used for solid-gas chemical reactions. Few data are available regarding solar efficiency in most of the processes, and the available information indicates relatively low values. Chemical reaction efficiencies show better values, especially in the case of a fluidized bed reactor for solid-gas chemical reactions, and fixed bed and rotary reactors for thermal decompositions.The work is partially funded by the Spanish government (ENE2015-64117-C5-1-R (MINECO/FEDER) and ENE2015-64117-C5-2-R (MINECO/FEDER)). The authors would like to thank the Catalan Government for the quality accreditation given to their research groups GREA (2017 SGR 1537) and DIOPMA (2017 SGR 118). GREA and DIOPMA are certified agents TECNIO in the category of technology developers from the Government of Catalonia. Dr. Aran Solé would like to thank Ministerio de Economía y Competitividad de España for Grant Juan de la Cierva, FJCI-2015-25741

Source Separation with Side Information Based on Gaussian Mixture Models With Application in Art Investigation

In this paper, we propose an algorithm for source separation with side information where one observes the linear superposition of two source signals plus two additional signals that are correlated with the mixed ones. Our algorithm is based on two ingredients: first, we learn a Gaussian mixture model (GMM) for the joint distribution of a source signal and the corresponding correlated side information signal; second, we separate the signals using standard computationally efficient conditional mean estimators. The paper also puts forth new recovery guarantees for this source separation algorithm. In particular, under the assumption that the signals can be perfectly described by a GMM model, we characterize necessary and sufficient conditions for reliable source separation in the asymptotic regime of low-noise as a function of the geometry of the underlying signals and their interaction. It is shown that if the subspaces spanned by the innovation components of the source signals with respect to the side information signals have zero intersection, provided that we observe a certain number of linear measurements from the mixture, then we can reliably separate the sources; otherwise, we cannot. Our proposed framework -- which provides a new way to incorporate side information to aid the solution of source separation problems where the decoder has access to linear projections of superimposed sources and side information — is also employed in a real-world art investigation application involving the separation of mixtures of X-ray images. The simulation results showcase the superiority of our algorithm against other state-of-the-art algorithms

Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing

Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: $(i)$ controlling the device energy consumption when using the service; $(ii)$ reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: $(i)$ the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; $(ii)$ the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.This work was supported in part by the European Union (Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant 655282 – F. Renna), in part by EPSRC under Grant EP/M00113X/1 and Grant EP/K033166/1, and in part by Innovate U.K. (project ACAME under Grant 131983)