REVIEW OF RANKED SET SAMPLING: MODIFICATIONS AND APPLICATIONS

Abstract

This paper is related to the application of Artificial Neural Networks (ANN) for noise cancelation in electric digital signals. The use of
ANN is a novel technique in noise reduction, which only needs the noisy version of the signal to obtain the clean one.
In this work is employed a FIR Multilayer Perceptron network. First is applied a software approach in Matlab, where the network is
trained, and it’s designed a strategy to reach the optimal network for noise cancelation in two cases study: simulated noise in Matlab and
noise obtained through a Data Acquisition System connected to a sensor. Good results were achieved in the cancelation of both noises.
Second approach is used to describe the ANN in hardware. The optimal FIR Multilayer Perceptron architecture of Matlab is implemented
in VHDL (Very High Speed Integrated Circuit Hardware Description Language) to download in a Xilinx XC3S1200E FPGA (Field
Programmable Gate Array), setting as goal in the design highlight the parallelism of ANN operation. After been obtained the VHDL
design, a noise cancelation application is simulated, with good results, considering the errors produced by the less accuracy of the
numerical format of VHDL ANN. Finally the design is downloaded in the FPGA and is checked that works according to the results of the
software approach.