University of Almería

About: University of Almería is a education organization based out in Almería, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 4674 authors who have published 10905 publications receiving 233036 citations. The organization is also known as: University of Almeria & Universidad de Almería.

A review of the mathematical models for predicting the heating value of biomass materials

Abstract: Following the discovery of fire, biomass became the main source of energy used by mankind. Advanced societies have largely replaced the use of biomass with the use of fossil fuels, but our dependence on these ever scarcer resources, plus the need to reduce CO2 emissions in the face of climate change, is forcing us to make use of renewable sources of energy, including biomass. The exploitation of this resource often requires that its heating value be known. This can be determined either directly (though not cheaply) or by the use of models that predict it using a number of easily and economically determined variables. The present review gathers together the most recent models for predicting the heating value of biomass, assesses their areas of application, and highlights errors that have been made in their formulation, transcription, and in the references made to them. Different models have relied upon elemental, proximal, structural, physical and chemical analyses to determine the values of necessary variables, although those relying on the results of the first two types of analysis have been the most popular. The simplest models and those with the widest range of applications are those most often referred to in the literature. The frequency with which important information has been left unconsidered in some studies, which has led to errors in the expressions presented, as well as errors of transcription and referencing, suggest that future work should be undertaken with greater diligence.

Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

Abstract: Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Xiaoli, also called RX algorithm. A variation of this algorithm consists of applying the same concept to a local sliding window centered around each image pixel. The computational cost is very high for RX algorithm and it strongly increases for its local versions. However, current advances in high performance computing help to reduce the run-time of these algorithms. So, for the standard RX, it is possible to achieve a processing time similar to the data acquisition time and to increase the practical interest for its local versions. In this paper, we discuss several optimizations which exploit different forms of acceleration for these algorithms. First, we explain how the calculation of the correlation matrix and its inverse can be accelerated through optimization techniques based on the properties of these particular matrices and the efficient use of linear algebra libraries. Second, we describe parallel implementations of the RX algorithm, optimized for multicore platforms. These are well-known, inexpensive and widely available high performance computing platforms. The ability to detect anomalies of the global and local versions of RX is explored using a wide set of experiments, using both synthetic and real data, which are used for comparing the optimized versions of the global and local RX algorithms in terms of anomaly detection accuracy and computational efficiency. The synthetic images have been generated under different noise conditions and anomalous features. The two real scenes used in the experiments are a hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and another data set collected by the HYperspectral Digital Image Collection Experiment (HYDICE). Experimental results indicate that the proposed optimizations can significantly improve the performance of the considered algorithms without reducing their anomaly detection accuracy.