Navegar Autor

Jesús Soto

Evaluation of Clustering Algorithms on HPC Platforms

Cebrian JM, Imbernón B, Soto J, Cecilia JM. Evaluation of Clustering Algorithms on HPC Platforms. Mathematics [Internet] 2021;9:2156. Available from: http://dx.doi.org/10.3390/math9172156

Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform. 

Antibióticos como contaminantes emergentes

Martínez-Alcalá, I., Soto, J., & Lahora, A. (2020). Antibióticos como contaminantes emergentes. Riesgo ecotoxicológico y control en aguas residuales y depuradas. Ecosistemas29(3), 2070. https://doi.org/10.7818/ECOS.2070

La presencia de contaminantes emergentes en aguas es cada vez mayor. Especialmente preocupan los antibióticos, debido a que pueden dar lugar a la aparición de bacterias resistentes, pero también a que dichos antibióticos pueden afectar negativamente a los ecosistemas y a los organismos que los habitan. Los antibióticos empleados para el consumo humano terminan llegando a las estaciones depuradoras de aguas residuales (EDAR) donde se ha visto que se eliminan solo en parte…

Entrevista en Onda Regional

Murycia
MURyCÍA. Muret, novela histórica del profesor Jesús Soto vía @ormurcia

El pasado 14/01/2020 me entrevistaron en el programa MURyCÍA, de Onda Regional.

Novela histórica Muret

Muret, la batalla que acabó con la Gran Corona de Aragón
Muret, la batalla que decidió la Gran Corona de Aragón

Ya está a la venta mi novela MURET.

En ella relato la historia de Adán de Alascún, un joven aragonés cuya vida da un vuelco la noche que se celebra la victoria en la batalla de las Navas de Tolosa. Resignado por el peso de la culpa, descubre que su pasado ha sido un engaño. Un engaño que comenzó unos años atrás, cuando iba a casarse con su amada. Pronto un oscuro secreto se cobijará en su interior, que lo conducirá hacia el único propósito que lo mantiene vivo: la venganza.

La novela nos relata una historia de amor, traición y poder en el devenir del reino de Aragón. A través de sus páginas cabalgaremos parejos a la historia de Aragón en Occitania. Estaremos presentes en momentos decisivos que explican la cruzada cátara; la situación feudal de Occitania en el siglo XIII; la injerencia de los reinos colindantes por el dominio de una tierra rica y la influencia de la Corona de Aragón.

De la mano del joven Alascún viviremos el amor, el odio y la sinrazón que llevaba a los caballeros medievales a morir en las batallas. Adán nos guiará por un entramado de caminos de orgullo, ambición y felonía que confluirá en la villa de Muret. El castillo de Muret será testigo mudo de reyes vasallos, condes más poderosos que sus señores y cruzados que solo se arrodillan ante la cruz. Unas murallas frente a las que todos empeñarán sus destinos a un Juicio de Dios.

Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument

biosystemsYa está disponible nuestro nueva publicación.

Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument.

Highlights

•Different machine learning techniques have been tested to predict soil properties.
•The predicted soil properties are TC, TN, CEC, clay, silt and Na+.
•The best predictive machine learning technique has been the Gaussian Process.
•The Gaussian process is better compared to the traditional PLSR technique.
•The Gaussian Process is the candidate for the development of intelligent system.

High-Throughput Infrastructure for Advanced ITS Services

articulo_ITSServicesHoy nos han comunicado la publicación de un nuevo artículo, con el que llevábamos trabajando desde el año pasado.

High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring.

Abstract:

Novel cooperative intelligent transportation systems (ITS) serve as the basis for the provision of a number of services for drivers, occupants, and third parties. The vast amount of information to be collected, especially in vehicle-to-infrastructure (V2I) communication services, requires new algorithms and hardware platforms to cope with real-time requirements; however, this combination is not properly addressed in the literature. In this paper, we introduce a high-throughput hardware-software infrastructure to gather information from vehicles and efficiently process it to provide novel ITS services. We propose a parallelization approach of a fuzzy clustering technique on heterogeneous servers based on CPU and several GPUs, tailored to classification problems in V2I. The infrastructure is empirically tested to offer a geo-located pollution information service through the periodical collection of both vehicle’s position and status data. We offer a real service that correctly identifies highly polluting traffic areas and drivers. The results indicate a good performance of the system under high loads, and our scalability analysis reveals a good operation in real-ambitious deployments thanks to the use of the both CPU and multiple GPUs, showing that our proposal can efficiently host cooperative services involving high processing in the ITS context.

A novel fuzzy clustering approach to regionalise watersheds

paperHydrology Otra artículo que nos publican: A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters. Una nueva aplicación de los algoritmos de clasificación difusa.

Abstract

One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.