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

Aplicação do Comando Numérico Computadorizado na Otimização em Prototipagem de Placa de Circuito Impresso
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.
José Gleury Galvino Pereira e Adriana Maria Rebouças do Nascimento

Subhash Chandran's CV
Subhash Chandran's CV
Created with the AltaCV template.
subhash

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

供应链管理大作业
物管1601-0121618990120
郭洋

MICROPROCESSOR REPORT
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.
SIFAT

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

Cecilia's CV
My uni CV
Cecilia Hu

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.
leo.ranaldi