Algoritmos de aprendizaje neurocomputacionales para su implementación hardware

Las redes de neuronas artificiales son un paradigma de aprendizaje y procesamiento automático inspirado en el funcionamiento del sistema nervioso, se emplean en toda tipo de aplicaciones, con lo que van apareciendo nuevas aplicaciones donde la utilización de ordenadores no da una solución de manera. Los sistemas en tiempo real y las redes de sensores son dos de las tecnologías más extendidas donde la utilización de modelos neurocomputacionales requiere un desarrollo en dispositivos y el empleo de técnicas de programación diferente a las convencionales. En este tipo de aplicaciones otros dispositivos hardware como las FPGAs o microcontroladores son más adecuados a la hora de la implementación de redes neuronales artificiales. Los sistemas en tiempo real están sujetos a unas limitaciones temporales y la estructura de las FPGAs permite implementar este tipo de diseños debido a que se diseña el dispositivo a nivel hardware, consiguiendo unos tiempos de respuesta muy acotados, exigidos en una aplicación en tiempo real. En esta línea de investigación existen dos posibles alternativas para este tipo de sistemas: (i) implementaciones específicas de algoritmos conocidos adaptados a los dispositivos hardware; (ii) implementaciones de nuevos algoritmos que se adapten mejor a este dispositivo. Otra tecnología muy importante es las redes de sensores inalámbricas, debido a los avances tecnológicos registrados en la última década sobre la capacidad de los microcontroladores usados en este tipo de aplicaciones. Una motivación de esta tesis es de dotar de inteligencia a las redes de sensores en este tipo de escenarios para proporcionar de una cierta lógica a la toma de decisiones Se busca alcanzar los siguientes objetivos: (i) analizar el algoritmo Backpropagation, desarrollando y evaluando diferentes técnicas de optimización para cada dispositivo; (ii) evaluar una alternativa al algoritmo Backpropagation para poder realizar una implementación hardware maximizando el uso de recursos; (iii) realizar una implementación hardware eficiente del algoritmo C-Mantec para evaluar su posible utilización en aplicaciones de sistemas en tiempo real; y (iv) evaluar una implementación del algoritmo constructivo C-Mantec sobre microcontroladores para su utilización en redes de sensores. Se implementa el algoritmo Backpropagation una FPGA y un microcontrolador. La implementación en FPGA se precisa diseñar con diferentes modificaciones, que se centran en introducir una nueva neurona ``Primera Capa'', incorporar la multiplexación por división en el tiempo del bloque multiplicador, así como tabular e interpolar los valores de la función sigmoidea, permitiendo un reducción de recursos, en media, de un 25,8\% de celdas lógicas y un 50'3\% de bloques específicos. En el caso específico del microcontrolador la modificación del tipo de representación de los datos permite un incremento en la velocidad de cómputo de entre 8 a 18 veces más rápido, además de una reducción importante en la cantidad de memoria utilizada. La implementación ``on-chip'' del algoritmo C-Mantec ha sido realizada de forma específica para su implantación en una placa FPGA, haciendo un análisis del tiempo de cómputo se observa una disminución del tiempo empleado en las implementaciones sobre FPGA en relación a las realizadas sobre PC. Esto es debido a que el tiempo de cómputo en un ordenador crece de forma exponencial y de manera lineal en una FPGA, dando lugar a implementaciones hasta 47 veces más rápidas. La implementación hardware del algoritmo C-Mantec es un 15\% más eficientes en recursos hardware utilizados en comparación a la del algoritmo Backpropagation, permitiendo mayor número de neuronas en la arquitectura. Para finalizar es importante mencionar que el tiempo de aprendizaje de las implementaciones FPGAs en ambos algoritmos es notablemente menor que el tiempo empleado por un PC, siendo la del C-Mantec sustancialmente inferior al del Backpropagation. Además el tiempo de ejecución del modelo es considerablemente inferior para el C-Mantec, lo que supone una ventaja en la fase de explotación del modelo. El algoritmo C-Mantec se ha implementado en la placa Arduino, para lo que se ha modificado el paradigma de representación de datos reduciendo considerablemente la memoria utilizada para el almacenamiento de variables y aumentando la velocidad de procesamiento, debido a que la unidad aritmético lógica en este tipo de representación son más simples. El algoritmo implementado se ha empleado como una red de sensor/actuador en tres casos de estudios con el fin de demostrar la eficiencia y la versatilidad de la aplicación resultante. Los tres casos de estudios seleccionados son problemas definidos en entornos cambiantes, y por lo tanto la toma de decisiones del sensor/actuador ha de adaptarse en consecuencia a los cambios observados, por lo que requieren una reconversión del modelo de red neuronal que controla el proceso de decisión. Los tiempos de reprogramación observados son significativamente bajos en los tres casos de estudio, siendo en consecuencia el consumo de energía del dispositivo también bastante pequeño. Incluso sin una comparación exhaustiva con el caso tradicional en el que el nuevo código tiene que ser transmitido desde una unidad de control central, los resultados hacen evidente una reducción en el gasto energético, cualidad muy importante en este tipo de tecnología debida a la corta duración de las baterías que lo alimentan. Como resultado, se ha demostrado la idoneidad del algoritmo C-Mantec para su aplicación en una tarea compleja utilizando un microcontrolador Arduino UNO. Hoy en día, dada la existencia de dispositivos con mucho más poder de cómputo y recursos que la placa considerada, el presente estudio permite confirmar la potencial aplicación del algoritmo propuesto en tareas reales que necesitan sensores/actuadores. Las futuras líneas de investigación pueden ser muy variadas, algunas de los iniciados son evolucionar la implementación hardware del algoritmo Backpropagation; o analizar la posibilidad de emplear las FPGAs como aceleradoras hardware para simulaciones de sistemas complejos. Además de estudiar otros modelos computacionales con otras reglas de; o analizar la posibilidad de aplicar los sistemas neurocomputacionales en tiempo real en tareas complejas

Solving Scheduling Problems with Genetic Algorithms using a Priority Encoding Scheme

Scheduling problems are very hard computational tasks with several applications in multitude of domains. In this work we solve a practical problem motivated by a real industry situation, in which we apply a genetic algorithm for finding an acceptable solution in a very short time interval. The main novelty introduced in this work is the use of a priority based chromosome codification that determines the precedence of a task with respect to other ones, permitting to introduce in a very simple way all problem constraints, including setup costs and workforce availability. Results show the suitability of the approach, obtaining real time solutions for tasks with up to 50 products.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tech

Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Memoria del segundo simposium sobre historia, sociedad y cultura de México y América Latina

La presente obra reúne 20 ponencias de las 27 que se presentaron en el “Segundo simposium sobre historia, sociedad y cultura de México y América Latina”, realizado el 8 y 9 de noviembre de 2006, en el Centro de Investigación en Ciencias Sociales y Humanidades (CICSyH) de la Universidad Autónoma del Estado de México (UAEM), en Toluca, Estado de México

Maximizing resource recovery from urban wastewater through an innovative facility layout

[EN] This research work proposes an innovative layout for urban wastewater treatment based on anaerobic technology, microalgal cultivation and membrane technology. The proposed Water Resource Recovery Facility (WRRF) system can treat urban wastewater efficiently, complying with legal discharge limits and allowing for resource recovery, i.e. energy, nutrients and reclaimed water. In addition, the proposed layout produces less solid wastes than a conventional wastewater treatment plant (WWTP) and it is possible to recover energy as biogas, not only from the original wastewater sources but also from the biomass generated in the WRRF system

Resource recovery from sulphate-rich sewage through an innovative anaerobic-based water resource recovery facility (WRRF)

[EN] This research work proposes an innovative water resource recovery facility (WRRF) for the recovery of energy, nutrients and reclaimed water from sewage, which represents a promising approach towards enhanced circular economy scenarios. To this aim, anaerobic technology, microalgae cultivation, and membrane technology were combined in a dedicated platform. The proposed platform produces a high-quality solid- and coliform-free effluent that can be directly discharged to receiving water bodies identified as sensitive areas. Specifically, the content of organic matter, nitrogen and phosphorus in the effluent was 45 mg COD.L-1 , 14.9 mg N.L-1 and 0.5 mg P.L-1 , respectively. Harvested solar energy and carbon dioxide biofixation in the form of microalgae biomass allowed remarkable methane yields (399 STP L CH 4.kg(-1) CODinf ) to be achieved, equivalent to theoretical electricity productions of around 0.52 kWh per m 3 of wastewater entering the WRRF. Furthermore, 26.6% of total nitrogen influent load was recovered as ammonium sulphate, while nitrogen and phosphorus were recovered in the biosolids produced (650 +/- 77 mg N.L-1 and 121.0 +/- 7.2 mg P.L-1).This research was supported by the Spanish Ministry of Economy and Competitiveness (MINECO, Projects CTM2014-54980-C2-1-R and CTM2014-54980-C2-2-R) jointly with the European Regional Development Fund (ERDF), which are gratefully acknowledged. This research was also supported by the Spanish Ministry of Education, Culture and Sport via two pre-doctoral FPU fellowships (FPU14/05082 and FPU15/02595) and by the Spanish Ministry of Economy and Competitiveness via two pre-doctoral FPI fellowships (BES-2015-071884, BES-2015-073403) and one Juan de la Cierva contract (FJCI-2014-21616). The authors would also like to acknowledge the support received from Generalitat Valenciana via two VALithornd post-doctoral grants (APOSTD/2014/049 and APOSTD/2016/104) and via the fellowships APOTI/2016/059 and CPI-16-155, as well as the financial aid received from the European Climate KIC association for the 'MAB 2.0' Project (APIN0057_ 2015-3.6-230_ P066-05) and Universitat Politecnica de Valencia via a pre-doctoral FPI fellowship to the seventh author.Seco Torrecillas, A.; Aparicio Antón, SE.; Gonzalez-Camejo, J.; Jiménez Benítez, AL.; Mateo-Llosa, O.; Mora-Sánchez, JF.; Noriega-Hevia, G.... (2018). Resource recovery from sulphate-rich sewage through an innovative anaerobic-based water resource recovery facility (WRRF). Water Science & Technology. 78(9):1925-1936. https://doi.org/10.2166/wst.2018.492S19251936789Bair, R. A., Ozcan, O. O., Calabria, J. L., Dick, G. H., & Yeh, D. H. (2015). Feasibility of anaerobic membrane bioreactors (AnMBR) for onsite sanitation and resource recovery (nutrients, energy and water) in urban slums. Water Science and Technology, 72(9), 1543-1551. doi:10.2166/wst.2015.349Barat, R., Serralta, J., Ruano, M. V., Jiménez, E., Ribes, J., Seco, A., & Ferrer, J. (2013). Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants. Water Science and Technology, 67(7), 1481-1489. doi:10.2166/wst.2013.004Batstone, D. J., Hülsen, T., Mehta, C. M., & Keller, J. (2015). Platforms for energy and nutrient recovery from domestic wastewater: A review. Chemosphere, 140, 2-11. doi:10.1016/j.chemosphere.2014.10.021Bilad, M. R., Arafat, H. A., & Vankelecom, I. F. J. (2014). Membrane technology in microalgae cultivation and harvesting: A review. Biotechnology Advances, 32(7), 1283-1300. doi:10.1016/j.biotechadv.2014.07.008Carrington E.-G. 2001 Evaluation of Sludge Treatments for Pathogen Reduction. http://europa.eu.int/comm/environment/pubs/home.htm.Cookney, J., Mcleod, A., Mathioudakis, V., Ncube, P., Soares, A., Jefferson, B., & McAdam, E. J. (2016). Dissolved methane recovery from anaerobic effluents using hollow fibre membrane contactors. Journal of Membrane Science, 502, 141-150. doi:10.1016/j.memsci.2015.12.037De Morais, M. G., & Costa, J. A. V. (2007). Biofixation of carbon dioxide by Spirulina sp. and Scenedesmus obliquus cultivated in a three-stage serial tubular photobioreactor. Journal of Biotechnology, 129(3), 439-445. doi:10.1016/j.jbiotec.2007.01.009Giménez, J. B., Robles, A., Carretero, L., Durán, F., Ruano, M. V., Gatti, M. N., … Seco, A. (2011). Experimental study of the anaerobic urban wastewater treatment in a submerged hollow-fibre membrane bioreactor at pilot scale. Bioresource Technology, 102(19), 8799-8806. doi:10.1016/j.biortech.2011.07.014Giménez, J. B., Martí, N., Ferrer, J., & Seco, A. (2012). Methane recovery efficiency in a submerged anaerobic membrane bioreactor (SAnMBR) treating sulphate-rich urban wastewater: Evaluation of methane losses with the effluent. Bioresource Technology, 118, 67-72. doi:10.1016/j.biortech.2012.05.019Giménez, J. B., Bouzas, A., Carrere, H., Steyer, J.-P., Ferrer, J., & Seco, A. (2018). Assessment of cross-flow filtration as microalgae harvesting technique prior to anaerobic digestion: Evaluation of biomass integrity and energy demand. Bioresource Technology, 269, 188-194. doi:10.1016/j.biortech.2018.08.052González-Camejo, J., Serna-García, R., Viruela, A., Pachés, M., Durán, F., Robles, A., … Seco, A. (2017). Short and long-term experiments on the effect of sulphide on microalgae cultivation in tertiary sewage treatment. Bioresource Technology, 244, 15-22. doi:10.1016/j.biortech.2017.07.126Martí, N., Barat, R., Seco, A., Pastor, L., & Bouzas, A. (2017). Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants. Journal of Environmental Management, 196, 340-346. doi:10.1016/j.jenvman.2016.12.074Moosbrugger R. , WentzelM. & EkamaG.1992Simple Titration Procedures to Determine H2CO3 Alkalinity and Short-chain Fatty Acids in Aqueous Solutions Containing Known Concentrations of Ammonium, Phosphate and Sulphide Weak Acid/Bases. Water. Res. Commission, Report, No. TT 57/92.Morales, N., Boehler, M., Buettner, S., Liebi, C., & Siegrist, H. (2013). Recovery of N and P from Urine by Struvite Precipitation Followed by Combined Stripping with Digester Sludge Liquid at Full Scale. Water, 5(3), 1262-1278. doi:10.3390/w5031262Pretel, R., Durán, F., Robles, A., Ruano, M. V., Ribes, J., Serralta, J., & Ferrer, J. (2015). Designing an AnMBR-based WWTP for energy recovery from urban wastewater: The role of primary settling and anaerobic digestion. Separation and Purification Technology, 156, 132-139. doi:10.1016/j.seppur.2015.09.047Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). Economic and environmental sustainability of submerged anaerobic MBR-based (AnMBR-based) technology as compared to aerobic-based technologies for moderate-/high-loaded urban wastewater treatment. Journal of Environmental Management, 166, 45-54. doi:10.1016/j.jenvman.2015.10.004Sharma, B., Sarkar, A., Singh, P., & Singh, R. P. (2017). Agricultural utilization of biosolids: A review on potential effects on soil and plant grown. Waste Management, 64, 117-132. doi:10.1016/j.wasman.2017.03.002Sialve, B., Bernet, N., & Bernard, O. (2009). Anaerobic digestion of microalgae as a necessary step to make microalgal biodiesel sustainable. Biotechnology Advances, 27(4), 409-416. doi:10.1016/j.biotechadv.2009.03.001Sid, S., Volant, A., Lesage, G., & Heran, M. (2017). Cost minimization in a full-scale conventional wastewater treatment plant: associated costs of biological energy consumption versus sludge production. Water Science and Technology, 76(9), 2473-2481. doi:10.2166/wst.2017.423Viruela, A., Murgui, M., Gómez-Gil, T., Durán, F., Robles, Á., Ruano, M. V., … Seco, A. (2016). Water resource recovery by means of microalgae cultivation in outdoor photobioreactors using the effluent from an anaerobic membrane bioreactor fed with pre-treated sewage. Bioresource Technology, 218, 447-454. doi:10.1016/j.biortech.2016.06.11

O31 Integrative analysis reveals a molecular stratification of systemic autoimmune diseases

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Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat