Abstract
This paper describes a sensing technique which utilizes artificial neural networks and a distributed sensor. The concept resembles the manner in which humans use their fingertips to touch objects, and subsequently use their brains to classify and recognize images detected. There are three major components of the designed sensor: a compliant skin is made of rubber with wedge-shaped ridge, an energy transducer which is a piezoelectric polyvinyledene fluoride film, and neural networks which are used to characterize signals through a learning process. The backpropagation is used as a learning rule, thus, learning required a set of input patterns each paired with a desired output pattern. The objective for the networks is to learn and memorize, after proper training, in order to reproduce desired outputs. Experimental results show that the sensor with artificial neural nets is very effective at recognizing Braille characters including SIX, SEVEN, EIGHT, UP, DOWN, LEFT, and RIGHT.
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