Gallery — Recent

Aneesh Jain's Resume Created with the Recent Grad Resume Template

Com a evolução constante da eletrônica, a necessidade de produzir protótipos em placa de circuito impresso é cada vez mais cobrada e importante para se elaborar tecnologia de forma rápida, mas sem deixar a qualidade de produção baixa. Pensando nisso, este artigo propõe o desenvolvimento de uma fresadora CNC com base no comando numérico computadorizado para a confecção de trilhas na placa de circuito impresso de forma otimizada. Deste modo, são mostrados passo a passo os pilares teóricos que compõem a base de conhecimento para que se possa entender e desenvolver a ferramenta que irá usinar e por sua vez produzir o protótipo de forma eficaz. Os resultados obtidos em relação à montagem da ferramenta e o material usinado foi classificado com satisfatório, já que a máquina CNC conseguiu atingir seus objetivos, perfurando, cortando a placa e isolando as trilhas formando assim o circuito.

Subhash Chandran's CV Created with the AltaCV template.

Satish Kumar's CV. Created with the Infographics CV template.

物管1601-0121618990120

Gesture controlled robot is a robot which can be controlled by simple gesture. The user just needs to wear a gesture device which include a sensor. The sensor will record the movement of hand in a specific direction which will result in the movement of the robot in the respective direction. The robot and the gesture device are connected wirelessly via radio waves. The wireless communication enables the user to interact with the robot in a more friendly way.

Rishi Shah's Résumé Created with the CV for Freshers template.

My uni CV

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.