The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Effect of variables on the thickness of an edible coating applied on frozen fish establishment of the concept of safe dipping time

Glazing is a technique used to retard fish deterioration during storage. This work focuses on the study of distinct variables (fish temperature, coating temperature, dipping time) that affect the thickness of edible coatings (water glazing and 1.5% chitosan) applied on frozen fish. Samples of frozen Atlantic salmon (Salmo salar) at -15, -20, and -25 °C were either glazed with water at 0.5, 1.5 or 2.5 °C or coated with 1.5% chitosan solution at 2.5, 5 or 8 °C, by dipping during 10 to 60 s. For both water and chitosan coatings, lowering the salmon and coating solution temperatures resulted in an increase of coating thickness. At the same conditions, higher thickness values were obtained when using chitosan (max. thickness of 1.41±0.05 mm) compared to water (max. thickness of 0.84±0.03 mm). Freezing temperature and crystallization heat were found to be lower for 1.5% chitosan solution than for water, thus favoring phase change. Salmon temperature profiles allowed determining, for different dipping conditions, whether the salmon temperature was within food safety standards to prevent the growth of pathogenic microorganisms. The concept of safe dipping time is proposed to define how long a frozen product can be dipped into a solution without the temperature raising to a point where it can constitute a hazard.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Postharvest shelf life extension of blueberries using a chitosan-based edible coating containing aloe vera juice

Poster

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Hot-dip galvanized steel dip-coatedwith ureasilicate hybrid in simulated concrete pore solution: Assessment of coating morphology and corrosion protection efficiency

The barrier effect and the performance of an organic–inorganic hybrid (OIH) sol–gel coating are highlydependent on the coating deposition method as well as processing conditions. In this work, studies onthe influence of experimental parameters using the dip coating method were performed. Factors suchas residence time (Rt), a curing step between each dip step and the number of layers of sol–gel OIHfilms deposited on HDGS to prevent corrosion in highly alkaline environments were studied. These OIHcoatings were obtained using a functionalized siloxane, 3-isociantepropyltriethoxysilane that reactedwith a diamino-functionalized oligopolymer (Jeffamine®D-230). The barrier efficiency of OIH coatings insimulated concrete pore solutions (SCPS) was assessed in the first moments of contact, by electrochemicalimpedance spectroscopy and potentiodynamic methods. The durability and stability of the OIH coatings inSCPS was monitored during eight days by macrocell current density. The morphological characterizationof the surface was performed by scanning electronic microscopy before and after exposure to SCPS.Glow discharge optical emission spectroscopy was used to obtain quantitative composition profiles toinvestigate the thickness of the OIH coatings as a function of the number of layers deposited and theinfluence of the Rt in the coating thickness.

Effect of chitosan-Aloe vera coating on postharvest quality of blueberry (Vaccinium corymbosum) fruit

The present study was carried out to evaluate the effect of chitosan-based edible coatings with Aloe vera extract on the postharvest blueberry fruit quality during storage at 5 °C. Firstly, A. vera fractions (pulp and liquid) were extracted from leaves and evaluated in terms of antifungal and antioxidant capacities. The choice of the most adequate chitosan and A. vera fraction concentrations to be incorporated in coating formulation was made based on the wettability of the corresponding coating solutions. Coatings with 0.5% (w/v) chitosan + 0.5% (w/v) glycerol + 0.1% (w/v) Tween 80 + 0.5% (v/v) A. vera liquid fraction presented the best characteristics to uniformly coat blueberry surface. Physico-chemical (i.e., titratable acidity, pH, weight loss) and microbiological analyses of coated blueberries (non-inoculated or artificially inoculated with Botrytis cinerea) were performed during 25 d. Microbiological growth and water loss levels were approximately reduced by 50% and 42%, respectively, in coated blueberries after 25 d compared to uncoated blueberries. After 15 d, weight loss values were 6.2% and 3.7% for uncoated and chitosanA. vera coated blueberries, respectively. Uncoated fruits presented mold contamination after 2 d of storage (2.0 ± 0.32 log CFU g1), whilst fruits with chitosan-based coatings with A. vera presented mold contamination only after 9 d of storage (1.3 ± 0.35 log CFU g1). Overall, coatings developed in this study extend blueberries shelf-life for about 5 d, demonstrating for the first time that the combination of chitosan and A. vera liquid fraction as edible coating materials has great potential in expanding the shelf-life of fruits.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Influence of chitosan coating on protein-based nanohydrogels properties and in vitro gastric digestibility

Chitosan coating was applied in Lactoferrin (Lf)-Glycomacropeptide (GMP) nanohydrogels by layer-by-layer coating process. A volume ratio of 0.1 of Lf-GMP nanohydrogels (0.2 mg.mL-1, at pH 5.0) to chitosan (1 mg.mL-1, at pH 3) demonstrated to be the optimal condition to obtain stable nanohydrogels with size of 230 ± 12 nm, a PdI of 0.22 ± 0.02 and a -potential of 30.0 ± 0.15 mV. Transmission electron microscopy (TEM) images showed that the application of chitosan coating in Lf-GMP did not affect the spherical shape of nanohydrogels and confirmed the low aggregation of nanohydrogels in solution. The analysis of chemical interactions between chitosan and Lf-GMP nanohydrogels were performed by Fourier transform infrared spectroscopy (FTIR) and by circular dichroism (CD) that revealed that a specific chemical interaction occurring between functional groups of protein-based nanohydrogels and active groups of the chitosan was established. The effect of chitosan coating on release mechanisms of Lf-GMP nanohydrogels at acid conditions (pH 2, 37 ºC) was evaluated by the encapsulation of a model compound (caffeine) in these systems. Linear Superposition Model was used to fit the experimental data and revealed that Fick and relaxation mechanisms are involved in caffeine release. It was also observed that the Fick contribution increase with the application of chitosan coating. In vitro gastric digestion was performed with Lf-GMP nanohydrogels and Lf-GMP nanohydrogels with chitosan coating and it was observed that the presence of chitosan improve the stability of Lf and GMP (proteins were hydrolysed at a slower rate and were present in solution by longer time). Native electrophoreses revealed that the nanohydrogels without coating remained intact in solution until 15 min and with chitosan coating remained intact until 60 min, during gastric digestion.

Robust learning in safety-related domains : machine learning methods for solving safety-related application problems

Magdeburg, Univ., Fak. für Informatik, Diss., 2009

Machine learning with Lipschitz classifiers

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Multimodal automatic user disposition recognition in human-machine interaction

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013

Citation-based Plagiarism Detection : applying citation pattern analysis to identify currently non-machine-detectible disguised plagiarism in scientific publications

Magdeburg, Univ., Fak. für Informatik, Diss., 2013