MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Análise da interface adesiva entre cimentos resinosos e diferentes terços da dentina intrarradicular

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determination of anticonvulsants in human plasma using SPME in a heated interface coupled online to liquid chromatography (SPME-LC)

A simple and sensitive method using solid phase microextraction (SPME) and liquid chromatography (LC) with heated online desorption (SPME-LC) was developed and validated to analyze anticonvulsants (AEDs) in human plasma samples. A heated lab-made interface chamber was used in the desorption procedure, which allowed the transference of the whole extracted sample. The SPME conditions were optimized by applying an experimental design. Important factors are discussed such as fiber coating types, pH, extraction time and desorption conditions. The drugs were analyzed by LC, using a C18 column (150 mm 4.6 mm 5 mm); and 50 mmol L1 , pH ¼ 5.50 ammonium acetate buffer : acetonitrile : methanol (55 : 22 : 23 v/v) as the mobile phase with a flow rate of 0.8 mL min1 . The suggested method presented precision (intra-assay and inter-assay), linearity and limit of quantification (LOQ) all adequate for the therapeutic drug monitoring (TDM) of AEDs in plasma.

Análise da interface adesiva entre pinos de fibra de vidro e dentina intrarradicular submetida à irrigação com diferentes agentes condicionantes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Determination of anticonvulsants in human plasma using SPME in a heated interface coupled online to liquid chromatography (SPME-LC)

A simple and sensitive method using solid phase microextraction (SPME) and liquid chromatography (LC) with heated online desorption (SPME-LC) was developed and validated to analyze anticonvulsants (AEDs) in human plasma samples. A heated lab-made interface chamber was used in the desorption procedure, which allowed the transference of the whole extracted sample. The SPME conditions were optimized by applying an experimental design. Important factors are discussed such as fiber coating types, pH, extraction time and desorption conditions. The drugs were analyzed by LC, using a C18 column (150 mm x 4.6 mm x 5 mm); and 50 mmol L-1, pH 5.50 ammonium acetate buffer : acetonitrile : methanol (55 : 22 : 23 v/v) as the mobile phase with a flow rate of 0.8 mL min(-1). The suggested method presented precision (intra-assay and inter-assay), linearity and limit of quantification (LOQ) all adequate for the therapeutic drug monitoring (TDM) of AEDs in plasma.

Effects and uptake of gold nanoparticles deposited at the air–liquid interface of a human epithelial airway model

The impact of nanoparticles (NPs) in medicine and biology has increased rapidly in recent years. Gold NPs have advantageous properties such as chemical stability, high electron density and affinity to biomolecules, making them very promising candidates as drug carriers and diagnostic tools. However, diverse studies on the toxicity of gold NPs have reported contradictory results. To address this issue, a triple cell co-culture model simulating the alveolar lung epithelium was used and exposed at the air-liquid interface. The cell cultures were exposed to characterized aerosols with 15 nm gold particles (61 ng Au/cm2 and 561 ng Au/cm2 deposition) and incubated for 4 h and 24 h. Experiments were repeated six times. The mRNA induction of pro-inflammatory (TNFalpha, IL-8, iNOS) and oxidative stress markers (HO-1, SOD2) was measured, as well as protein induction of pro- and anti-inflammatory cytokines (IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, GM-CSF, TNFalpha, INFgamma). A pre-stimulation with lipopolysaccharide (LPS) was performed to further study the effects of particles under inflammatory conditions. Particle deposition and particle uptake by cells were analyzed by transmission electron microscopy and design-based stereology. A homogeneous deposition was revealed, and particles were found to enter all cell types. No mRNA induction due to particles was observed for all markers. The cell culture system was sensitive to LPS but gold particles did not cause any synergistic or suppressive effects. With this experimental setup, reflecting the physiological conditions more precisely, no adverse effects from gold NPs were observed. However, chronic studies under in vivo conditions are needed to entirely exclude adverse effects.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

Development of a hand-gesture recognition system for human-computer interaction

The aim of this Master Thesis is the analysis, design and development of a robust and reliable Human-Computer Interaction interface, based on visual hand-gesture recognition. The implementation of the required functions is oriented to the simulation of a classical hardware interaction device: the mouse, by recognizing a specific hand-gesture vocabulary in color video sequences. For this purpose, a prototype of a hand-gesture recognition system has been designed and implemented, which is composed of three stages: detection, tracking and recognition. This system is based on machine learning methods and pattern recognition techniques, which have been integrated together with other image processing approaches to get a high recognition accuracy and a low computational cost. Regarding pattern recongition techniques, several algorithms and strategies have been designed and implemented, which are applicable to color images and video sequences. The design of these algorithms has the purpose of extracting spatial and spatio-temporal features from static and dynamic hand gestures, in order to identify them in a robust and reliable way. Finally, a visual database containing the necessary vocabulary of gestures for interacting with the computer has been created.