A multi-scale comparison of texture descriptors extracted from the wavelet and curvelet domains for small bowel tumor detection in capsule endoscopy exams

Traditional endoscopic methods do not reach the entire Gastrointestinal (GI) tract. Wireless Capsule Endoscopy (CE) is a diagnostic procedure that allows the visualization of the whole GI tract, acquiring video frames, at a rate of two frames per second, while travels through the GI tract, resulting in huge amounts of data per exam. These frames possess rich information about the condition of the stomach and intestine mucosa, encoded as color and texture patterns. It is known for a long time that human perception of texture is based in a multi-scale analysis of patterns, which can be modeled by multi-resolution approaches. Therefore, in the present paper it is proposed a frame classification scheme, based in different combinations of texture descriptors taken at different detail levels of the Discrete Wavelet Transform and Discrete Curvelet Transform domains, in order to compare the classification performance of these multi-resolution representations of the information within the CE frames. The classification step is performed by a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 91.7% of sensitivity and 89.4% specificity for features extracted from the DWT domain and 94.1% of sensitivity and 92.4% specificity for features extracted from the DCT domain. These promising results support the feasibility of the proposed method.Center Algoritm

Inspecção e reabilitação do Seminário Conciliar de Braga

Apresenta-se o diagnóstico, análise e reforço de um edifício dos anos 30, realizado essencialmente em betão armado, mas com paredes de contorno em cantaria de granito. Devido a uma alteração de uso, de seminário para arquivo, realizaram-se diferentes análises estruturais (lineares e não-lineares) para definir as condições de segurança do edifício. Dada a necessidade de reforço, discutem-se diferentes alternativas e detalha-se o projecto de execução

Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors

Traditional endoscopic methods do not allow the visualization of the entire Gastrointestinal (GI) tract. Wireless Capsule Endoscopy (CE) is a diagnostic procedure that overcomes this limitation of the traditional endoscopic methods. The CE video frames possess rich information about the condition of the stomach and intestine mucosa, encoded as color and texture patterns. It is known for a long time that human perception of texture is based in a multi-scale analysis of patterns, which can be modeled by multi-resolution approaches. Furthermore, modeling the covariance of textural descriptors has been successfully used in classification of colonoscopy videos. Therefore, in the present paper it is proposed a frame classification scheme based on statistical textural descriptors taken from the Discrete Curvelet Transform (DCT) domain, a recent multi-resolution mathematical tool. The DCT is based on an anisotropic notion of scale and high directional sensitivity in multiple directions, being therefore suited to characterization of complex patterns as texture. The covariance of texture descriptors taken at a given detail level, in different angles, is used as classification feature, in a scheme designated as Color Curvelet Covariance. The classification step is performed by a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 97.2% of sensitivity and 97.4% specificity. These promising results support the feasibility of the proposed method.Centre Algoritm

Design Optimization of a High Power LED Matrix Luminaire

This work presents a methodology for optimizing the layout and geometry of an m x n high power (HP) light emitting diode (LED) luminaire. Two simulators are used to analyze an LED luminaire model. The first simulator uses the finite element method (FEM) to analyze the thermal dissipation, and the second simulator uses the ray tracing method for lighting analysis. The thermal and lighting analysis of the luminaire model is validated with an error of less than 10%. The goal of the optimization process is to find a solution that satisfies both thermal dissipation and light efficiency. The optimization goal is to keep the LED temperature at an acceptable level while still obtaining uniform illumination on a target plane. Even though no optical accessories or active cooling systems are used in the model, the results demonstrate that it is possible to obtain satisfactory results even with a limited number of parameters. The optimization results show that it is possible to design luminaires with 4, 6 and up to 8 HP-LEDs, keeping the LED temperature at about 100 degrees C. However, the best uniformity on a target plane was found by the heuristic algorithm

Biomechanics of Competitive Swimming Strokes

The aim of this chapter has two folds: (i): to perform a biomechanical characterization of the four competitive swimming strokes, based on the kinematics, kinetics and neuromuscular analysis; (ii) to report the relationships established between all the domains and how it might influence the swimming performance

Design Optimization of a High Power LED Matrix Luminaire

This work presents a methodology for optimizing the layout and geometry of an m x n high power (HP) light emitting diode (LED) luminaire. Two simulators are used to analyze an LED luminaire model. The first simulator uses the finite element method (FEM) to analyze the thermal dissipation, and the second simulator uses the ray tracing method for lighting analysis. The thermal and lighting analysis of the luminaire model is validated with an error of less than 10%. The goal of the optimization process is to find a solution that satisfies both thermal dissipation and light efficiency. The optimization goal is to keep the LED temperature at an acceptable level while still obtaining uniform illumination on a target plane. Even though no optical accessories or active cooling systems are used in the model, the results demonstrate that it is possible to obtain satisfactory results even with a limited number of parameters. The optimization results show that it is possible to design luminaires with 4, 6 and up to 8 HP-LEDs, keeping the LED temperature at about 100 degrees C. However, the best uniformity on a target plane was found by the heuristic algorithm

Controlling Chaotic Maps using Next-Generation Reservoir Computing

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic H\'enon map, including controlling the system between unstable fixed-points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only 10 data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.Comment: 9 pages, 8 figure

Changes of the energetic profile in masters' swimmers over a season

The aim of this study was to track and compare the changes of performance and energetic profile of male and female masters swimmers during a season. Eleven female (age: 34.7±7.3-y) and fourteen male (age: 35.6±7.4-y) with 4.2±3.7-y and 3.9±1.6-y of experience in masters, respectively, performed an all-out 200 m freestyle to evaluate total energy expenditure (Etot), aerobic (Aer), anaerobic lactic (AnL) and alactic (AnAl) contributions. The oxygen uptake (VO2) was measured immediately after the 200 m trial and the VO2 reached during the trial was estimated through the backward extrapolation of the O2 recovery curve. Fingertip capillary blood samples were collected before the 200 m trial and 3, 5, and 7 minutes after its end. Significant differences were observed between male (TP1:177.50±30.96s; TP2:174.79±29.08s; TP3:171.21±22.38s) and female (TP1:205.18±24.47s; TP2: 197.45±20.97s; TP3: 193.45±18.12s) for 200 m freestyle performance at the three time periods (TPs). Male presented higher Etot in all TPs (TP1:230.40±48.40kJ; TP2:242.49±37.91kJ; TP3:257.94±46.32kJ) compared with that found for female swimmers (TP1:188.51±35.13kJ; TP2:193.18±20.98kJ; TP3:199.77±25.94kJ). Male presented higher AnL (TP1:33.42±6.82kJ; TP2:30.97±8.73kJ; TP3:30.66±8.27kJ) and AnAl (TP1:30.61±3.48kJ; TP2:30.61±3.48kJ; TP3:30.60±3.48kJ) than female (TP1:18.83±8.45kJ; TP2:14.98±4.17kJ; TP3:18.33±8.66kJ) and (TP1:24.32±2.22kJ; TP2:24.31±2.23kJ; TP3: 24.31±2.23kJ). Aerobic metabolism is the major contributor for Etot both in male (TP1:71.63±4.99%; TP2:74.05±5.03%; TP3:76.14±4.46%) and female swimmers (TP1:76.87±3.86%; TP2:79.40±3.63%; TP3:78.40±5.54%). The better performance obtained by male compared to female swimmers may be due to the different contributions of the energetic pathways. Aerobic metabolism was the major contributor to Etot in a 200 m race, in both genders. Partial aerobic contribution was higher in female, while partial anaerobic contribution was greater in male.info:eu-repo/semantics/publishedVersio

BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process