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Controle digital de um conversor CC-CC
Um artigo sobre controle digital de conversores CC-CC tipo buck utilizando microcontrolador STM cortex-m0+.
Ana Caroline Tondo Bonafim, Jefferson Willian França Góes, Raul Scarmosin Freitas
Survey on Bi-LSTM CNNs CRF for Italian Sequence Labeling and Multi-Task Learning
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.
Visuele cryptografie met transparanten
In deze handleiding worden twee technieken beschreven voor visuele cryptografie. Bij de eerste techniek worden twee transparanten met schijnbaar willekeurige patronen van zwarte blokjes over elkaar geschoven om een geheime afbeelding tevoorschijn te laten komen. De tweede techniek gebruikt twee afbeeldingen in grijstinten die transparant over elkaar geschoven worden om een geheime afbeelding op te roepen. De enige voorkennis die nodig is om deze technieken te kunnen uitvoeren, is het gebruik van een rekenblad (hier: Excel) en van een fotobewerkingsprogramma.
Van den Broeck Luc
Robot localization in a mapped environment using Adaptive Monte Carlo algorithm
Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization.
Gender Inequality In The Workplace: An Interactive Data Visualization Application
Evolutionary Language Development in Multi-Agent Cooperative Learning Games
Lazaridou., et al 2017 proposed a framework for language learning that relies on multi-agent communication. The agents in the framework were setup in a referential game where they communicated about many images. In this paper, we propose an experiment where agents develop a private language for referring to specified sentences given a set of sentences. The challenge is for the agents to learn a method of distinguishing differences between sentences and to develop a shared language to be able to refer to particular sentences by those distinguishing features. We will evaluate the agents' ability to accurately identify and differentiate the sentences. In addition, we will identify patterns in the methods that the agents develop to refer to the different types of sentences.Keywords: Reinforcement learning, multi-agent coordination
Scientific computing in Statistical Mechanics: time decay of orientation order in 2D hard disk system
In this work we successfully applied diverse computational techniques to calculate important quantities in one problem in Statistical Mechanics: the hard disk system in two dimensions. We calculated the global and local orientation order and the time decay constant of the global orientation correlation function. We also computed the Voronoi construction to visuallize the spatial distribution of the local orientation order.
Paulo Freitas Gomes