Blind restoration of images with penalty-based decision making : a consensus approach

In this thesis we show a relationship between fuzzy decision making and image processing . Various applications for image noise reduction with consensus methodology are introduced. A new approach is introduced to deal with non-stationary Gaussian noise and spatial non-stationary noise in MRI

Use of idempotent functions in the aggregation of different filters for noise removal

The majority of existing denoising algorithms obtain good results for a specific noise model, and when it is known previously. Nonetheless, there is a lack in denoising algorithms that can deal with any unknown noisy images. Therefore, in this paper, we study the use of aggregation functions for denoising purposes, where the noise model is not necessary known in advance; and how these functions affect the visual and quantitative results of the resultant images

Consensus image method for unknown noise removal

Noise removal has been, and it is nowadays, an important task in computer vision. Usually, it is a previous task preceding other tasks, as segmentation or reconstruction. However, for most existing denoising algorithms the noise model has to be known in advance. In this paper, we introduce a new approach based on consensus to deal with unknown noise models. To do this, different filtered images are obtained, then combined using multifuzzy sets and averaging aggregation functions. The final decision is made by using a penalty function to deliver the compromised image. Results show that this approach is consistent and provides a good compromise between filters.This work is supported by the European Commission under Contract No. 238819 (MIBISOC Marie Curie ITN). H. Bustince was supported by Project TIN 2010-15055 of the Spanish Ministry of Science

Spatially-variant noise filtering in magnetic resonance imaging : a consensus-based approach

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a sub-sampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach

La agresividad en la conducción: una visión a partir de las investigaciones internacionales

La conducta agresiva al volante no es nueva, lo que inquieta es, como demuestran algunos estudios, que se está incrementando de forma preocupante. Cuando conducimos seguimos una tendencia consistente en que la respuesta agresiva se convierta en habitual cada vez que sucede un acontecimiento "desagradable" para el conductor. Estos comportamientos adquieren mayor trascendencia porque, según nos muestran también otros estudios, provocan accidentes de tráfico. En este proyecto se han realizado una serie de búsquedas documentales en distintas y numerosas bases de datos. Para ello, se han utilizado los necesarios “perfiles de búsqueda” (acotamientos incluidos). A los resultados de dichas búsquedas se han calculado diferentes índices de referencia como los de productividad (Bradfor y Lotka), cohesión, dispersión, etc., para finalmente trabajar más de doscientos productos científicos originales (la mayoría artículos procedentes de revistas con jueces) sobre los que se ha practicado un análisis de contenidos. La agresividad en la conducción, desde el punto de la investigación, no es un tema fácil. Por el contrario plantea dificultades como son la propia conceptualización de agresividad en la conducción, la disparidad de metodologías que existen para estudiar este fenómeno con sus correspondientes aspectos negativos asociados y las dificultades que entraña desde el punto de vista de la intervención (especialmente desde la norma y el correspondiente sistema sancionador), entre otras. En esta revisión hemos obtenido muchas respuestas a cuestiones clave que desconocíamos. El por qué del comportamiento agresivo, los factores que tienden a potenciarlo, etc. Cuestiones todas ellas claves si pensamos desde un marco de utilidad práctica

The geomorphic dimension global change : risks and opportunities

Fil: Hurtado, Martín Adolfo. Instituto de Geomorfología y Suelos (IGS). Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; ArgentinaFil: Forte, Luis M.. Instituto de Geomorfología y Suelos (IGS). Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; ArgentinaFil: Bruschi, Viola María. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: Bonachea, Jaime. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: Rivas, Victoria. DGUOT. Universidad de Cantabria. Santander; EspañaFil: Gómez Arozamena, José. DCMQ. Universidad de Cantabria. Santander; EspañaFil: Dantas Ferreira, Marcilene. Departamento de Engenharia Civil. Universidade Federal de SÆo Carlos. SÆo Paulo; BrasilFil: Remondo, Juan. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: González, A.. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: Díaz de Terán, J.R.. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: Salas, L.. Departamento de Ciencias de la Tierra y Física de la Materia Condensada. Universidad de Cantabria; EspañaFil: Cendrero, Antonio. Instituto de Geomorfología y Suelos (IGS). Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; Argentin

Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Sanjuan, J.... (2013). Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment. Energies. 6(9):4489-4507. doi:10.3390/en6094489S4489450769Brooks, A., Lu, E., Reicher, D., Spirakis, C., & Weihl, B. (2010). Demand Dispatch. IEEE Power and Energy Magazine, 8(3), 20-29. doi:10.1109/mpe.2010.936349Chan, S. C., Tsui, K. M., Wu, H. C., Hou, Y., Wu, Y.-C., & Wu, F. (2012). Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges. IEEE Signal Processing Magazine, 29(5), 68-85. doi:10.1109/msp.2012.2186531Mohan Saini, L., & Kumar Soni, M. (2002). Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Transactions on Power Systems, 17(3), 907-912. doi:10.1109/tpwrs.2002.800992Hyndman, R. J., & Fan, S. (2010). Density Forecasting for Long-Term Peak Electricity Demand. IEEE Transactions on Power Systems, 25(2), 1142-1153. doi:10.1109/tpwrs.2009.2036017McSharry, P. E., Bouwman, S., & Bloemhof, G. (2005). Probabilistic Forecasts of the Magnitude and Timing of Peak Electricity Demand. IEEE Transactions on Power Systems, 20(2), 1166-1172. doi:10.1109/tpwrs.2005.846071Amin-Naseri, M. R., & Soroush, A. R. (2008). Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management, 49(6), 1302-1308. doi:10.1016/j.enconman.2008.01.016Maksimovich, S. M., & Shiljkut, V. M. (2009). The Peak Load Forecasting Afterwards Its Intensive Reduction. IEEE Transactions on Power Delivery, 24(3), 1552-1559. doi:10.1109/tpwrd.2009.2014267Moazzami, M., Khodabakhshian, A., & Hooshmand, R. (2013). A new hybrid day-ahead peak load forecasting method for Iran’s National Grid. Applied Energy, 101, 489-501. doi:10.1016/j.apenergy.2012.06.009Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169Hernández, L., Baladrón, C., Aguiar, J., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Lloret, J. (2013). Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks. Energies, 6(6), 2927-2948. doi:10.3390/en6062927Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.640044

Verification of the ΔKeff hypothesis along the fatigue crack path in thin and thick Al specimens

Elber assumed that the actual driving force for fatigue crack growth (FCG) is the effective stress intensity factor ΔKeff. To verify this hypothesis, both DC(T) and C(T) specimens are cut from a 6351-T6 Al alloy circular bar with two different thicknesses, 2 and 30mm, tested under fixed ΔK and Kmax to simulated plane stress and plane strain FCG conditions. A strain-gage bonded on the back face of the specimens is used to measure the crack length and a custom-made Labview program is used to control the applied load, maintaining ΔK and Kmax constant along the crack path. Moreover, the crack opening load is redundantly measured during the FCG tests, using far field strains from the back face gage and near field strains from a series of gages bonded along the crack path, as well as an independent digital image correlation system to measure displacement/strain fields on the face of the specimens. These tests show that the Al specimens reproduce the behavior previously observed in similar tests in 1020 steel: a significant decrease of the opening load as the cracks grow along the specimens, while maintaining a FCG rate essentially constant under the fixed {ΔK, Kmax} loading, a behavior that cannot be explained by the ΔKeff hypothesis

The role of CCR5/CXCR3 expressing CD8+ cells in liver damage and viral control during persistent hepatitis C virus infection

20 p.Background/Aims:CXCR3 and CCR5 play a major role in recruiting cytotoxic T cells (Tc) and secreting secondary type 1 cytokines (Tc1) in the liver. HCV could impair their expression as a survival mechanism. The role of these chemokine receptors on CD8+ cells in chronic hepatitis C is analysed. Methods:Serum, chemokines, peripheral blood and intrahepatic lymphocytes from chronic hepatitis C patients were studied. CXCR3 / CCR5 expressing CD8+ cells were quantified by flow-cytometry. Serum chemokines concentration (CXCL10/CCL3) was measured by ELISA. Basal data were correlated with liver inflammation. Longitudinal data were obtained during treatment and correlated with virologic response. Results:CCR5/CXCR3 expressing CD8+ cells were enriched in the liver and correlated with inflammation. Chronic HCV patients presented the same frequency of CCR5high/CXCR3high expressing CD8+ cells in peripheral blood as in healthy controls but higher serum concentration of CXCL10/CCL3. Treatment with PEG-interferon a-2b plus ribavirin increased CCR5high/CXCR3high expressing CD8+ cells frequency in peripheral blood and decreased CXCL10/CCL3 serum concentration. Increase in CXCR3high expressing CD8+ cells after 24 weeks of treatment was correlated with SVR. Conclusions:In chronic hepatitis C, anti-viral treatment induces an increase in CD8+ cells expressing chemokine receptors associated with Tc1 response and a reduction in their ligands. Achievement of viral control is associated with an increase in CXCR3high expressing CD8+ cells during treatmentSchering-Plough-SpainJunta de Comunidades de Castilla-La Manch