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Master's Dissertation
DOI
10.11606/D.74.2014.tde-22092014-135707
Document
Author
Full name
Diego Santiago dos Santos
Institute/School/College
Knowledge Area
Date of Defense
Published
Pirassununga, 2014
Supervisor
Committee
Tech, Adriano Rogério Bruno (President)
Brennecke, Käthery
Fiorelli, Juliano
Title in Portuguese
Utilização da tecnologia bluetooth associada a redes neurais artificiais (PMC) para monitoramento e rastreamento de suínos
Keywords in Portuguese
Monitoramento de animais
MultiLayer Perceptron
RSSI
Sistema de comunicação sem fio
Abstract in Portuguese
O presente trabalho teve como objetivo apresentar uma metodologia que permita encontrar o posicionamento e rastrear as diferentes localizações de um suíno em uma baia, utilizando o valor do Receiver Signal Strenght Indicator (RSSI), entre o dispositivo móvel (suíno) e três dispositivos fixos, e uma Rede Neural Artificial do tipo Perceptron Multicamadas (PMC), responsável por interpretar os sinais RSSI e transformá-los em valores conhecidos, como em um plano cartesiano, com coordenadas no eixo X e eixo Y. A região de teste foi dividida em 289 pontos, sendo 286 utilizados para coleta de dados e para o treinamento da rede PMC. Para cada ponto, foram armazenados a sua posição dentro da baia e o valor RSSI entre o dispositivo móvel e os três dispositivos fixos. O processo foi repetido para 8 pontos escolhidos aleatoriamente dentro do espaço de teste e inseridos como entradas na rede PMC. Após treinamentos e operações realizadas com diversas arquiteturas foi possível concluir que àquela dotada de 10 neurônios na camada intermediária consistiu na melhor alternativa, cujos resultados de monitoramento e rastreamento das posições do dispositivo móvel foram encontradas com valores aceitáveis de exatidão.
Title in English
Using Bluetooth technology associated with Artificial Neural Networks (MLP) for monitoring and tracking pigs
Keywords in English
Animal tracking
MultiLayer Perceptron
RSSI
Wireless communication systems
Abstract in English
This paper aims to present a methodology to find the positioning and tracking of the different locations of a pig in a stall, using the value of the Receiver Signal Strength Indicator (RSSI), between the mobile device (pig) and three devices fixed, and an Artificial Neural Network Multilayer Perceptron (MLP), responsible for interpreting the RSSI signals and turning them into known values, such as on a Cartesian plane, with coordinates on X axis and Y axis. The test region was divided into 289 points, with 286 points used for data collection and training of PMC network, and for each point, it was stored its position inside the stall and its RSSI value between the mobile devices and the three fixed. The process was repeated for 8 points chosen randomly within the space of test and entered as inputs into the PMC network. After training and operations with various architectures it was concluded that the architecture with 10 neurons in the hidden layer was the best alternative, whose the results of monitoring and tracking the positions of mobile device were found with acceptable accuracy.
 
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ME3692116COR.pdf (642.87 Kbytes)
Publishing Date
2014-09-29
 
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