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Vol. 145, No. 10, September 2012, pp. 1-9




Implementation of ANN in FPGA for improved Temperature Drift of the MEMS Flow Sensor

1 I. Mellal, 1 M. Laghrouche, 1 B. Idjeri, 2 R. Beguenane, 1 S. Ameur

1 Laboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires (LAMPA)

Electronic Department, Mouloud Mammeri University, Tizi Ouzou, Algeria

2 Royal Military College of Canada

E-mail: idir.mellal@gmail.com, larouche_67@yahoo.fr



Received:   25 September 2012   /Accepted: 22 October 2012   /Published: 31 October 2012

Digital Sensors and Sensor Sysstems


Abstract: Temperature is one of the most important factors influencing accurate silicon sensor device. A model based on Neural Networks (NN), has been implemented to generate fluid velocity data, knowing fluid temperature measurements. The proposed model based on neural networks can provide the calibrated response characteristics irrespective of change in the sensor characteristics due to change in ambient temperature. In this paper, we describe a hardware implementation of the modelling and temperature compensation in Silicon Hot Wire Sensor (SHWS). The Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) transfers the nonlinear characteristics of the flow sensor at specific range of temperature to linearized the response. The first preliminary results show that the whole SoC can be implemented in the Field Programmable Gate Array XC2V1000 circuit which offers many advantages.


Keywords: ANN, FPGA, MEMS Sensor, Temperature drift



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