Proyecto de instalación de autoproducción de energía y mejora de la eficiencia energética de una cooperativa agrícola

Treball Final de Grau en Enginyeria Elèctrica. Codi: EE1045. Curs acadèmic 2014-2015Este documento es el proyecto de final de grado de la titulación Grado en Ingeniería Eléctrica de la Universidad Jaume I de Castellón para dar cumplimiento a la normativa de la titulación. Se ha desarrollado después de la estancia en prácticas en la empresa Grupo Suncs Castelló siguiendo y ampliando uno de los proyectos en los que se pudo colaborar durante la estancia allí, relacionado con la autoproducción de energía mediante el uso de paneles fotovoltaicos y cuyo objetivo es la reducción en el importe de la factura eléctrica mediante el cambio de la iluminación por luminarias LED, el uso de baterías de condensadores para reducir la energía reactiva de la instalación, la correcta elección de la tarifa eléctrica y la implantación de placas solares en la cubierta del edificio para generar aproximadamente un 20% de la energía consumida

Sparse subspace averaging for order estimation

This paper addresses the problem of source enumeration for arbitrary geometry arrays in the presence of spatially correlated noise. The method combines a sparse reconstruction (SR) step with a subspace averaging (SA) approach, and hence it is named sparse subspace averaging (SSA). In the first step, each received snapshot is approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l0-norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. Our simulation results show that SSA provides robust order estimates under a variety of noise models.This work was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grants TEC2017-86921-C2-2-R (CAIMAN), PID2019-104958RB-C43 (ADELE), and BES-2017-080542, and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM

Coordinación territorial de las provincias de Loreto- Nauta, Requena y Ucayali del programa nacional de asistencia solidaria pensión 65

El informe es el resultado de más de 3 años de experiencia profesional obtenida en la Unidad Territorial de Loreto como coordinador territorial en las provincias de Loreto-Nauta, Requena y Ucayali del programa Nacional de Asistencia Solidaria PENSIÓN 65. El trabajo desarrollado se orienta principalmente a monitorear a los promotores en la ejecución de acciones vinculada a los procesos de afiliación, notificación, verificación de supervivencia y transferencia a usuarios; así como coordinar, articular con actores locales y comunales para el adecuado cumplimiento de los procesos y metas del programa Pensión 65. El Programa Nacional de Asistencia Solidaria “Pensión 65” fue creado en respuesta al objetivo del Estado de otorgar protección a los grupos sociales especialmente vulnerables, a través de la entrega de una subvención monetaria a los adultos mayores que viven en condición de pobreza extrema, el Programa contribuye a reducir su condición de vulnerabilidad. Es importante destacar que Pensión 65 es más que la entrega de una subvención económica porque además contribuye a la mejora del bienestar de las usuarias y usuarios. - Por ello, como parte fundamental de nuestro trabajo, coordinamos con el Ministerio de Salud y el Sistema Integral de Salud (SIS) para que usuarias y usuarios sean asegurados automáticamente, y puedan acceder a servicios de salud de calidad esto representa un avance en la recuperación de sus derechos como ciudadanos. En Pensión 65, también promovemos la mejora de la calidad de vida de los adultos mayores, a partir de la revalorización de su imagen social y de su rol como portadores de costumbres y tradiciones, que refuerzan la memoria colectiva y la identidad local, constituyéndose así en un valor para sus comunidades.Trabajo de suficiencia profesiona

Passive detection of rank-one Gaussian signals for known channel subspaces and arbitrary noise

This paper addresses the passive detection of a common signal in two multi-sensor arrays. For this problem, we derive a detector based on likelihood theory for the case of one-antenna transmitters, independent Gaussian noises with arbitrary spatial structure, Gaussian signals, and known channel subspaces. The detector uses a likelihood ratio where all but one of the unknown parameters are replaced by their maximum likelihood (ML) estimates. The ML estimation of the remaining parameter requires a numerical search, and it is therefore estimated using a sample-based estimator. The performance of the proposed detector is illustrated by means of Monte Carlo simulations and compared with that of the detector for unknown channels, showing the advantage of this knowledge.The work of D. Ramírez was partially supported by MCIN/AEI/10.13039/501100011033/ FEDER, UE, under grant PID2021-123182OB-I00 (EPiCENTER), by the Office of Naval Research (ONR) Global under contract N62909-23-1-2002, and by The Comunidad de Madrid under grant IntCARE-CM. The work of I. Santamaría was partly supported under grant PID2019-104958RB-C43 (ADELE) funded by MCIN/AEI/10.13039/501100011033. The work of L. L. Scharf was supported by the Office of Naval Research (ONR) under contract N00014-21-1-2145 and the Air Force Office of Scientific Research (AFOSR) under contract FA9550-21-1-0169

Subspace averaging for source enumeration in large arrays

Subspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of extracting many subspaces, which may then be approximated by a single extrinsic average. An automatic order determination rule for this extrinsic average is then the rule for determining the number of sources. Experimental results are presented for cases where the number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh limit.The work of I. Santamaría has been partially supported by the Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of D. Ramírez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013-41718-R) and the COMONSENS Network (TEC2015-69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015-69868-C2-1-R) and CAIMAN (TEC2017-86921- C2-2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of L. L. Scharf was supported by the National Science Foundation (NSF) under grant CCF-1018472

Scale-invariant subspace detectors based on first- and second-order statistical models

The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.The work by I. Santamaria was supported by the Ministerio de Ciencia e Innovación of Spain, and AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN) and PID2019-104958RB-C43 (ADELE). The work by Louis Scharf is supported by the Air Force Office of Scientific Research under contract FA9550-18-1-0087, and by the National Science Foundation (NSF) under contract CCF-1712788. The work of David Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grant TEC2017-86921-C2-2-R (CAIMAN), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM)

Subspace averaging and order determination for source enumeration

In this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yuejie Chi. The work of V. Garg and I. Santamaria was supported in part by the Ministerio de Economía y Competitividad (MINECO) of Spain, and in part by the AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN), TEC2015-69648-REDC, and BES-2017-080542. The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grants TEC2015-69868-C2-1-R (ADVENTURE) and TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICOCM). The work of L. L. Scharf was supported in part by the U.S. NSF under Contract CISE-1712788

Experimental evaluation of Interference Alignment under imperfect channel state information

Interference Alignment (IA) has been revealed as one of the most attractive transmission techniques for the K-user interference channel. In this work, we employ a multiuser Multiple-Input Multiple-Output (MIMO) testbed to analyze, in realistic indoor scenarios, the impact of channel state information errors on the sum-rate performance of IA. We restrict our study to a 3-user interference network in which each user transmits a single data stream using two transmit and two receive antennas. For this MIMO interference network, only two different IA solutions exist. We also evaluate the performance gain obtained in practice by using the IA solution that maximizes the sum-rate.This work has been funded by Xunta de Galicia, Ministerio de Ciencia e Innovación of Spain, and FEDER funds of the European Union under grants with numbers 10TIC003CT, 09TIC008105PR, TEC2010-19545-C04-01, TEC2010-19545-C04-03, AP2009-1105, AP2006-2965, and CSD2008-00010

An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors

An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.The work of D. Ram´ırez was supported by the Ministerio de Economía, Industria y Competitividad (MINECO) and AEI/FEDER funds of the E.U., under grants TEC2013- 41718-R (OTOSIS), TEC2015-69648-REDC (COMONSENS Network), TEC2015-69868-C2-1-R (ADVENTURE), and CAIMAN (TEC2017-86921-C2-2-R) and The Comunidad de Madrid under grant S2013/ICE-2845 (CASI-CAM-CM). The work of I. Santamaria and S. Van Vaerenbergh was supported by MINECO and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of L. Scharf was supported in part by the National Science Foundation under grant CCF-1712788

Can the structure of motor variability predict learning rate?

Recent studies show that motor variability is actively regulated as an exploration tool to promote learning in reward-based tasks. However, its role in learning processes during error-based tasks, when a reduction of the motor variability is required to achieve good performance, is still unclear. In this study, we hypothesized that error-based learning not only depends on exploration but also on the individuals’ ability to measure and predict the motor error. Previous studies identified a less auto-correlated motor variability as a higher ability to perform motion adjustments. Two experiments investigated the relationship between motor learning and variability, analyzing the long-range autocorrelation of the center of pressure fluctuations through the score of a Detrended Fluctuation Analysis in balance tasks. In Experiment 1, we assessed the relationship between variability and learning rate using a standing balance task. Based on the results of this experiment, and to maximize learning, we performed a second experiment with a more difficult sitting balance task and increased practice. The learning rate of the 2 groups with similar balance performances but different scores was compared. Individuals with a lower score showed a higher learning rate. Because the scores reveal how the motor output changes over time, instead of the magnitude of those changes, the higher learning rate is mainly linked to the higher error sensitivity rather than the exploration strategies. The results of this study highlight the relevance of the structure of output motor variability as a predictor of learning rate in error-based tasks