Embedded neural network for fire classification using an array of gas sensors

Bashyal, Shristy; Venayagamoorthy, Ganesh K.; Paudel, Bijay Raj

doi:10.1109/sas13374.2008.4472960

Cited by 6 publications

References 6 publications

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Compensation of Sensors Nonlinearity with Neural Networks

Cotton

Wilamowski

2010

2010 24th IEEE International Conference on Advanced Information Networking and Applications

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This paper describes a method of linearizing the nonlinear characteristics of many sensors using an embedded neural network. The proposed method allows for complex neural networks with very powerful architectures to be embedded on a very inexpensive 8-bit microcontroller. In order to accomplish this unique training software was developed as well as a cross compiler. The Neuron by Neuron process was as developed in assembly language to allow the fastest and shortest code on the embedded system. The embedded neural network also required an accurate approximation for hyperbolic tangent to be used as the neuron activation function.This process was then demonstrated on a robotic arm kinematics problem.

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Compensation of Sensors Nonlinearity with Neural Networks

Cotton

Wilamowski

2010

2010 24th IEEE International Conference on Advanced Information Networking and Applications

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Prevent Cooking Risks in Kitchen of Elderly People: Adaptable Reasoning Engine Based on Fuzzy Logic for Smart Oven

Abdulrazak

Yared

2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Hermite neural network-based intelligent sensors for harsh environments

Patra

Bornand

Chakraborty

2009

2009 International Joint Conference on Neural Networks

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We propose a novel computationally efficient artificial neural network (NN) for design and development of intelligent sensors to operate in harsh environments which can have wide variation of environmental conditions. The proposed Hermite NN (HeNN) models the inverse characteristics of a sensor and can provide linearized sensor response characteristics irrespective of change in environmental conditions, even wh~n the environmental parameters influence the sensor characteristics nonlinearly. By taking an example of a capacitive pressure sensor, we have shown through extensive computer simulations that the HeNN-based model can linearize its response with maximum full scale (FS) error of ±0.5% when it is operated in a harsh environment with temperature variation of -50 to 200 0 C and influenced nonlinearly. We have compared performance of the proposed HeNN-based model with a MLP-based model and shown its superior performance in terms of FS error and computational complexity.

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Embedded neural network for fire classification using an array of gas sensors

Cited by 6 publications

References 6 publications

Compensation of Sensors Nonlinearity with Neural Networks

Compensation of Sensors Nonlinearity with Neural Networks

Prevent Cooking Risks in Kitchen of Elderly People: Adaptable Reasoning Engine Based on Fuzzy Logic for Smart Oven

Hermite neural network-based intelligent sensors for harsh environments

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