Advances in Nonlinear Complexity Analysis for Partial Differential Equations

1 p. -- [Editorial Material]

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Instances of combinatorial optimization problems: complexity and generation

138 p.

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.

Complexity and Uniqueness of the Aromatic Profile of Smoked and Unsmoked Herreno Cheese

In this work, the volatile fraction of unsmoked and smoked Herreno cheese, a type of soft cheese from the Canary Islands, has been characterized for the first time. In order to evaluate if the position in the smokehouse could influence the volatile profile of the smoked variety, cheeses smoked at two different heights were studied. The volatile components were extracted by Solid Phase Microextraction using a divinylbenzene/carboxen/polydimethylsiloxane fiber, followed by Gas Chromatography/Mass Spectrometry. In total, 228 components were detected. The most numerous groups of components in the unsmoked Herreno cheese were hydrocarbons, followed by terpenes and sesquiterpenes, whereas acids and ketones were the most abundant. It is worth noticing the high number of aldehydes and ketones, and the low number of alcohols and esters in this cheese in relation to others, as well as the presence of some specific unsaturated hydrocarbons, terpenes, sesquiterpenes and nitrogenated derivatives. The smoking process enriches the volatile profile of Herreno cheese with ketones and diketones, methyl esters, aliphatic and aromatic aldehydes, hydrocarbons, terpenes, nitrogenated compounds, and especially with ethers and phenolic derivatives. Among these, methylindanones or certain terpenes like a-terpinolene, have not been detected previously in other types of smoked cheese. Lastly, the results obtained suggest a slightly higher smoking degree in the cheeses smoked at a greater height.