Neural network in computer vision for RoboCup middle size league

Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots. Robotic soccer is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.info:eu-repo/semantics/publishedVersio

Vision and distance integrated sensor (Kinect) for an autonomous robot

This work presents an application of the Microsoft Kinect camera for an autonomous mobile robot. In order to drive autonomously one main issue is the ability to recognize signalling panels positioned overhead. The Kinect camera can be applied in this task due to its double integrated sensor, namely vision and distance. The vision sensor is used to perceive the signalling panel, while the distance sensor is applied as a segmentation filter, by eliminating pixels by their depth in the object’s background. The approach adopted to perceive the symbol from the signalling panel consists in: a) applying the depth image filter from the Kinect camera; b) applying Morphological Operators to segment the image; c) a classification is carried out with an Artificial Neural Network and a simple Multilayer Perceptron network that can correctly classify the image. This work explores the Kinect camera depth sensor and hence this filter avoids heavy computational algorithms to search for the correct location of the signalling panels. It simplifies the next tasks of image segmentation and classification. A mobile autonomous robot using this camera was used to recognize the signalling panels on a competition track of the Portuguese Robotics Open

CONTROLE DE ORIENTAÇÃO E VELOCIDADE DE CADEIRA DE RODAS POR VISÃO COMPUTACIONAL

This work proposes a computer vision system for wheelchair control based on facial coordinates and head position estimation. This work comprises the design of a low-cost motorized wheelchair, which aims to promote independence and quality of life for people who have motor difficulties to manipulate a joystick. The tests performed on the built prototype resulted in an accuracy of 87.78%, an precision of 90.00% and a sensitivity of 87.70%. Tests performed in the motorized wheelchair resulted in an average accuracy of 94.69%, average precision of 91.04% and average sensitivity of 89.38%.Este trabalho propõe um sistema de visão computacional para controle de cadeira de rodas com base em coordenadas faciais e estimativa da posição da cabeça. Este trabalho compõe o projeto de uma cadeira de rodas motorizada de baixo custo, que tem como objetivo promover independência e qualidade de vida a pessoas que possuem dificuldades motoras para manipular um joystick. Os testes realizados no protótipo construído resultaram em uma acurácia de 87,78%, a precisão de 90,00% e uma sensibilidade de 87,70%. Os testes realizados na cadeira de rodas motorizada resultaram em uma acurácia média de 94,69%, precisão média de 91,04% e sensibilidade média de 89,38%

Curva de Aprendizagem da Mortalidade Hospitalar da Substituição da Válvula Aórtica Transcateter: Insights do Registro Nacional Brasileiro

Resumo Fundamento Dados robustos sobre a curva de aprendizagem (LC) da substituição da válvula aórtica transcateter (TAVR) são escassos nos países em desenvolvimento. Objetivo Avaliar a LC da TAVR no Brasil ao longo do tempo. Métodos Analisamos dados do registro brasileiro de TAVR de 2008 a 2023. Pacientes de cada centro foram numerados cronologicamente em número sequencial de caso (NSC). A LC foi realizada usando um spline cúbico restrito ajustado para o EuroSCORE-II e o uso de próteses de nova geração. Ainda, os desfechos hospitalares foram comparados entre grupos definidos de acordo com o nível de experiência, com base no NSC: 1º ao 40º caso (experiência inicial), 41º ao 80º caso (experiência básica), 81º ao 120º caso (experiência intermediária) e 121º caso em diante (experiência alta). Análises adicionais foram conduzidas de acordo com o número de casos tratados antes de 2014 (>40 e ≤40 procedimentos). O nível de significância adotado foi p <0,05. Resultados Foram incluídos 3194 pacientes de 25 centros. A idade média foi 80,7±8,1 anos e o EuroSCORE II médio foi 7±7,1. A análise da LC demonstrou uma queda na mortalidade hospitalar ajustada após o tratamento de 40 pacientes. Um patamar de nivelamento na curva foi observado após o caso 118. A mortalidade hospitalar entre os grupos foi 8,6%, 7,7%, 5,9%, e 3,7% para experiência inicial, básica, intermediária e alta, respectivamente (p<0,001). A experiência alta foi preditora independente de mortalidade mais baixa (OR 0,57, p=0,013 vs. experiência inicial). Centros com baixo volume de casos antes de 2014 não mostraram uma redução significativa na probabilidade de morte com o ganho de experiência, enquanto centros com alto volume de casos antes de 2014 apresentaram uma melhora contínua após o caso de número 10. Conclusão Observou-se um fenômeno de LC para a mortalidade hospitalar do TAVR no Brasil. Esse efeito foi mais pronunciado em centros que trataram seus 40 primeiros casos antes de 2014 que naqueles que o fizeram após 2014

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio