A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Application of chemometric analysis to infrared spectroscopy for the identification of wood origin

Chemical characteristics of wood are used in this study for plant taxonomy classification based on the current Angiosperm Phylogeny Group classification (APG III System) for the division, class and subclass of woody plants. Infrared spectra contain information about the molecular structure and intermolecular interactions among the components in wood but the understanding of this information requires multivariate techniques for the analysis of highly dense datasets. This article is written with the purposes of specifying the chemical differences among taxonomic groups, and predicting the taxa of unknown samples with a mathematical model. Principal component analysis, t-test, stepwise discriminant analysis and linear discriminant analysis, were some of the chosen multivariate techniques. A procedure to determine the division, class, subclass and order of unknown samples was built with promising implications for future applications of Fourier Transform Infrared spectroscopy in wood taxonomy classification

TECSCE: HW/SW Codesign Framework for Data Parallelism Based on Software Component

Part 1: Design MethodologiesInternational audienceThis paper presents a hardware/software (HW/SW) codesign framework (TECSCE) which enables software developers to easily design complex embedded systems such as massive data-parallel systems. TECSCE is implemented by integrating TECS and SCE: TECS is a component technology for embedded software, and SCE provides an environment for system-on-a-chip designs. Since TECS is based on standard C language, it allows the developers to start the design process easily and fast. SCE is a rapid design exploration tool capable of efficient MPSoC implementation. TECSCE utilizes all these advantages since it supports transformation from component descriptions and component sources to SpecC specification, and lets the developers decide data partitioning and parallelization at a software component level. Moreover, TECSCE effectively duplicates software components, depending on their degree of data parallelizing, to generate multiple SpecC specification models. An application for creating a panoramic image removing objects, such as people, is illustrated as a case study. The evaluation of the case study demonstrates the effectiveness of the proposed framework