Three low-power MEMS micro-accelerometers were designed, the first with variable distance and the last with variable area. Then, electrostatic analysis and simulation by the finite element method was performed to show its behavior and performance. In the simulation and analysis the software COMSOL MULTIPHYSICS 5.1 was used. The results show that the variable area micro-accelerometer exhibits the best relationship between behavior and performance.
Derived from the pandemic COVID19 that we are currently experiencing as a security measure it is very important that people wear face masks especially in public places, to try to minimize the spread of the SARS-Cov2 virus; this Project consisted in the detection of faces with and without face masks applying Deep learning neural networks, it was developed by using Python and the libraries TensorFlow and OpenCV which allowed to apply learning rules to artificial vision systems. The above would allow the installation of artificial vision systems in public places soon to warn and invite people to wear face masks when detected by the system.
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