Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Unmixing hyperspectral intimate mixtures

Proceedings of International Conference Conference Volume 7830 Image and Signal Processing for Remote Sensing XVI Lorenzo Bruzzone Toulouse, France | September 20, 2010

Longitudinal versus polar wrinkling of core-shell fibers with anisotropic size mismatches

We consider a fiber made of a soft elastic material, encased in a stiff elastic shell (core-shell geometry). If the core and shell dimensions are mismatched, e.g., because the core shrinks while the shell does not, but the two remain attached, then an elastic instability is triggered whereby wrinkles may appear on the shell. The wrinkle orientation may be longitudinal (along the fiber axis), polar (along the fiber perimeter), or a mixture of both, depending on the fiber's geometrical and material parameters. Here we investigate under what conditions longitudinal or polar wrinkling will occur.

Characterization of airborne particles generated from metal active gas welding process

This study is focused on the characterization of particles emitted in the metal active gas welding of carbon steel using mixture of Ar + CO2, and intends to analyze which are the main process parameters that influence the emission itself. It was found that the amount of emitted particles (measured by particle number and alveolar deposited surface area) are clearly dependent on the distance to the welding front and also on the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne fine particles seems to increase with the current intensity as fume-formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. These mixtures originate higher concentrations of fine particles (as measured by number of particles by cm 3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more severe worker's exposure.

Use of solvent extraction to remediate soils contaminated with hydrocarbons

The main objective of this research is to exploit the possibility of using an ex situ solvent extraction technique for the remediation of soils contaminated with semi-volatile petroleum hydrocarbons. The composition of the organic phase was chosen in order to form a single phase mixture with an aqueous phase and simultaneously not being disturbed (forming stable emulsions) by the soil particles hauling the contaminants. It should also permit a regeneration of the organic solvent phase. As first, we studied the miscibility domain of the chosen ternary systems constituted by ethyl acetate–acetone–water. This system proved to satisfy the previous requirements allowing for the formation of a single liquid phase mixture within a large spectrum of compositions, and also allowing for an intimate contact with the soil. Contaminants in the diesel range within different functional groups were selected: xylene, naphthalene and hexadecane. The analytical control was done by gas chromatography with FID detector. The kinetics of the extractions proved to be fast, leading to equilibrium after 10 min. The effect of the solid–liquid ratio on the extraction efficiency was studied. Lower S/L ratios (1:8, w/v) proved to be more efficient, reaching recoveries in the order of 95%. The option of extraction in multiple contacts did not improve the recovery in relation to a single contact. The solvent can be regenerated by distillation with a loss around 10%. The contaminants are not evaporated and they remain in the non-volatile phase. The global results show that the ex situ solvent extraction is technically a feasible option for the remediation of semi-volatile aromatic, polyaromatic and linear hydrocarbons.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

Bisphenol A migration from plastic materials: direct insight of ecotoxicity in Daphnia magna

Bisphenol A (BPA) is an endocrine disrupting chemical (EDC) whose migration from food packaging is recognized worldwide. However, the real overall food contamination and related consequences are yet largely unknown. Among humans, children’s exposure to BPA has been emphasized because of the immaturity of their biological systems. The main aim of this study was to assess the reproductive impact of BPA leached from commercially available plastic containers used or related to child nutrition, performing ecotoxicological tests using the biomonitoring species Daphnia magna. Acute and chronic tests, as well as single and multigenerational tests were done. Migration of BPA from several baby bottles and other plastic containers evaluated by GC-MS indicated that a broader range of foodstuff may be contaminated when packed in plastics. Ecotoxicological test results performed using defined concentrations of BPA were in agreement with literature, although a precocious maturity of daphnids was detected at 3.0 mg/L. Curiously, an increased reproductive output (neonates per female) was observed when daphnids were bred in the polycarbonate (PC) containers (145.1±4.3 % to 264.7±3.8 %), both in single as in multigenerational tests, in comparison with the negative control group (100.3±1.6 %). A strong correlated dose-dependent ecotoxicological effect was observed, providing evidence that BPA leached from plastic food packaging materials act as functional estrogen in vivo at very low concentrations. In contrast, neonate production by daphnids cultured in polypropylene and non-PC bottles was slightly but not significantly enhanced (92.5±2.0 % to 118.8±1.8 %). Multigenerational tests also revealed magnification of the adverse effects, not only on fecundity but also on mortality, which represents a worrying trend for organisms that are chronically exposed to xenoestrogens for many generations. Two plausible explanations for the observed results could be given: a non-monotonic dose–response relationship or a mixture toxicity effect.

Multiplexed electrochemical immunosensor for detection of celiac disease serological markers

Celiac disease (CD) is a gluten-induced autoimmune enteropathy characterized by the presence of antibodies against gliadin (AGA) and anti-tissue transglutaminase (anti-tTG) antibodies. A disposable electrochemical dual immunosensor for the simultaneous detection of IgA and IgG type AGA and antitTG antibodies in real patient’s samples is presented. The proposed immunosensor is based on a dual screen-printed carbon electrode, with two working electrodes, nanostructured with a carbon–metal hybrid system that worked as the transducer surface. The immunosensing strategy consisted of the immobilization of gliadin and tTG (i.e. CD specific antigens) on the nanostructured electrode surface. The electrochemical detection of the human antibodies present in the assayed serum samples was carried out through the antigen–antibody interaction and recorded using alkaline phosphatase labelled anti-human antibodies and a mixture of 3-indoxyl phosphate with silver ions was used as the substrate. The analytical signal was based on the anodic redissolution of enzymatically generated silver by cyclic voltammetry. The results obtained were corroborated with commercial ELISA kits indicating that the developed sensor can be a good alternative to the traditional methods allowing a decentralization of the analyses towards a point-of-care strategy.

Growth and characterization of Cu2ZnSn(S,Se)4 thin films for solar cells

Cu2ZnSnS4 (CZTS) and Cu2ZnSnSe4 (CZTSe) with their band gap energies around 1.45 eV and 1.0 eV, respectively, can be used as the absorber layer in thin film solar cells. By using a mixture of both compounds, Cu2ZnSn(S,Se)4 (CZTSSe), a band gap tuning may be possible. The latter material has already shown promising results such as solar cell efficiencies up to 10.1%. In this work, CZTSSe thin films were grown in order to study its structure and to establish the best growth precursors. SEM micrographs reveal an open columnar structure for most samples and EDS composition profiling of the cross sections show different selenium gradients. X-ray diffractograms show different shifts of the kesterite/stannite (1 1 2) peak, which indicate the presence of CZTSSe. From Raman scattering analysis, it was concluded that all samples had traces of CZTS and CZTSSe. The composition of the CZTSSe layer was estimated using X-ray diffraction and Raman scattering and both results were compared. It was concluded that Se diffused more easily in precursors with ternary Cu–Sn–S phases and metallic Zn than in precursors with ZnS and/or CZTS already formed. It was also showed that a combination of X-ray diffraction and Raman scattering can be used to estimate the ratio of S per Se in CZTSSe samples.

Re-use assessment of thermoset composite wastes as aggregate and filler replacement for concrete-polymer composite materials: a case study regarding GFRP pultrusion wastes

Glass fibre-reinforced plastics (GFRP), nowadays commonly used in the construction, transportation and automobile sectors, have been considered inherently difficult to recycle due to both the cross-linked nature of thermoset resins, which cannot be remoulded, and the complex composition of the composite itself, which includes glass fibres, polymer matrix and different types of inorganic fillers. Hence, to date, most of the thermoset based GFRP waste is being incinerated or landfilled leading to negative environmental impacts and additional costs to producers and suppliers. With an increasing awareness of environmental matters and the subsequent desire to save resources, recycling would convert an expensive waste disposal into a profitable reusable material. In this study, the effect of the incorporation of mechanically recycled GFRP pultrusion wastes on flexural and compressive behaviour of polyester polymer mortars (PM) was assessed. For this purpose, different contents of GFRP recyclates (0%, 4%, 8% and 12%, w/w), with distinct size grades (coarse fibrous mixture and fine powdered mixture), were incorporated into polyester PM as sand aggregates and filler replacements. The effect of the incorporation of a silane coupling agent was also assessed. Experimental results revealed that GFRP waste filled polymer mortars show improved mechanical behaviour over unmodified polyester based mortars, thus indicating the feasibility of GFRP waste reuse as raw material in concrete-polymer composites.

Properties of tantalum oxynitride thin films produced by magnetron sputtering: the influence of processing parameters

The main purpose of this work is to present and to interpret the change of structure and physical properties of tantalum oxynitride (TaNxOy) thin films, produced by dc reactive magnetron sputtering, by varying the processing parameters. A set of TaNxOy films was prepared by varying the reactive gases flow rate, using a N2/O2 gas mixture with a concentration ratio of 17:3. The different films, obtained by this process, exhibited significant differences. The obtained composition and the interpretation of X-ray diffraction results, shows that, depending on the partial pressure of the reactive gases, the films are: essentially dark grey metallic, when the atomic ratio (N + O)/Ta < 0.1, evidencing a tetragonal β-Ta structure; grey-brownish, when 0.1 < (N + O)/Ta < 1, exhibiting a face-centred cubic (fcc) TaN-like structure; and transparent oxide-type, when (N + O)/Ta > 1, evidencing the existence of Ta2O5, but with an amorphous structure. These transparent films exhibit refractive indexes, in the visible region, always higher than 2.0. The wear resistance of the films is relatively good. The best behaviour was obtained for the films with (N + O)/Ta ≈ 0.5 and (N + O)/Ta ≈ 1.3.