Este trabalho apresenta o resultado de uma pesquisa de doutorado realizada em uma pré-escola municipal com crianças de 3 a 5 anos de idade. Teve como objetivo, investigar a produção das culturas infantis a partir das experiências com a linguagem das histórias em quadrinhos (HQs). Neste sentido, voltou seu olhar para as produções gráficas das crianças de modo a compreender como estas se apropriam dos códigos das HQs, o que deles reproduzem e (re)inventam. Trata-se de um estudo de caso em que foram utilizados como procedimentos metodológicos: a observação e registro de campo, relatos orais, análise de documentos, análise das HQs e desenhos das crianças. Buscou-se a interlocução com a filosofia, sociologia da infância, pedagogia da infância e arte. Demonstra-se serem as HQs parte da cultura material da infância, que as crianças compartilham entre si, com as professoras e com suas famílias. Constatou-se um trabalho pedagógico escolarizante que, marcado por uma visão adultocêntrica, procura acelerar processos de escrita e formar para competências. Deparou-se com movimentos de resistências das crianças, que reivindicavam constantemente gestos de ruptura contra tal modelo.

In this paper we give one new proposal in finding optimal triangulation which is based on our authorial method for generating triangulation (Block method). We present two cases in calculation the triangulation weights (classical case and case based on block method). We also provide their equality and established relationship in calculation the weights for both models, with an emphasis on simplicity of calculations which occurs in the second case. The main goal of this paper is on the speed of obtaining optimal triangulation.

This paper addresses Internet of Things (IoT) with state-of-art approach. The purpose is to give insight into concept of “smart living”, a concept that meets requirements of today’s modern society. Implementation of this new technology requires new hardware and software installed and run on devices (“things”) connected to the Internet anytime and anywhere. In order to make possible this new technology for wide use, few technological, standards and legal issues need to be solved. In a view of this a new low power wireless sensor network protocol is proposed in the IoT spirit.

Ground Moving Target Indicator (GMTI) and High Resolution Radar (HRR) can track position and velocity of ground moving target. Pose, angle between position and velocity, can be derived from kinematics estimates of position and velocity and it is often used to reduce the search space of a target identification (ID) and Automatic Target Recognition (ATR)  algorithms. Due to low resolution in some radar systems, the GMTI estimated pose may exhibit large errors contributing to a faulty identification of potential targets. Our goal is to define new methodology to improve pose estimate. Besides applications in target tracking, there are numerous commercial applications in machine learning, augmented reality and body tracking.

In this work we determine chromosome polarity based on three machine learning methods: multilayer perceptron (MLP) neural networks, k-nearest neighbor ( k-nn ) method and support vector machines (SVM). In all three machine learning methods only two chromosome features, total length of the chromosome and the cetromere location, were used to determine the chromosome polarity.  Classification results obtained are 95.94%, 95.255%, and 95.88% for MLP neural networks, k-nn method and SVM respectively.

Ground Moving Target Indicator (GMTI) and High Resolution Radar (HRR) can track position and velocity of ground moving target. Pose, angle between position and velocity, can be derived from kinematics estimates of position and velocity and it is often used to reduce the search space of a target identification (ID) and Automatic Target Recognition (ATR)  algorithms. Due to low resolution in some radar systems, the GMTI estimated pose may exhibit large errors contributing to a faulty identification of potential targets. Our goal is to define new methodology to improve pose estimate. Besides applications in target tracking, there are numerous commercial applications in machine learning, augmented reality and body tracking.