Gesture recognition is a domain of grate interest of our days due to the multiple application possibilities, from the spatial or subaquatic robots manipulation, to the sign language used for communication by the peoples with hearing or speech disabilities. This paper shows an application of the artificial neural networks (ANN) implemented in field programmable gate arrays (FPGA) for the hand static gestures (postures) recognition. The adopted recognition method uses an ANN structured on two levels. The first level, a Feedforward ANN trained using supervised Hebbian algorithm, is used for input data preprocessing. The second one, used for data classification is a competitive ANN. Using an ANN for input data preprocessing offers flexibility regarding the implementation of a preprocessing method. This combination of two ANN leads to 100% recognition rate for the training set and two other sets oftest.
This paper presents a hardware implementation of a multilayer feed-forward neural network based on backpropagation. The implementation is assumed to design and implement modules that emulate FF-BP functions with computing blocks of the predefined System Generator library and user defined blocks integrated in the System Generator library. The main application of the developed structure is an artificial olfactory system used to recognize the type of coffee presented in a test chamber. Data acquisition was achieved through the PC-MIO-16E-1 acquisition card and a virtual instrument, developed in Labview, for signal pre-processing and data logging into text files. The patterns presented (the type of coffee) have been recognized through neural networks. In order to select the RNA with the highest accuracy in recognising the coffee type, several different RNAs were simulated.
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