Paper Info
Title
Experimental evaluation of latency coding for gas recognition
Abstract
Commercial gas recognition systems use advanced computationally intensive signal processing/pattern recognition algorithms to identify gases and discriminate between them. This severely impacts on the size and cost of such systems but also limits their large-scale deployment. Biologically-inspired gas recognition schemes have the potential to greatly simplify the task of gas recognition, enabling the advent of low cost and low power miniature gas systems. In this paper, we present an experimental evaluation of bio-inspired latency coding for gas recognition. The performance of this bio-inspired approach was evaluated against four commonly used pattern recognition algorithms, namely K Nearest Neighbors (KNN), neural networks (Multi-Layer Perceptron (MLP), Radial Basis Function (RBF)) and density models (Gaussian Mixture Models (GMM). Reported experimental results suggest that latency coding could perform as well if not better than more computationally intensive pattern recognition techniques.
Year
DOI
Venue
2013
10.1109/IDT.2013.6727123
Design and Test Symposium
Keywords
Field
DocType
bio-inspired materials,encoding,gas sensors,multilayer perceptrons,neural nets,radial basis function networks,Gaussian mixture models,K nearest neighbors,bio-inspired latency coding,density models,gas recognition,multilayer perceptron,neural networks,pattern recognition algorithms,radial basis function,electronic nose,gas sensors,glomerular convergence,latency coding,olfaction
k-nearest neighbors algorithm,Signal processing,Latency (engineering),Computer science,Speech recognition,Coding (social sciences),Artificial neural network,Perceptron,Mixture model,Neural gas
Conference
ISSN
Citations 
PageRank 
2162-061X
0
0.34
References 
Authors
1
4
Name
Order
Citations
PageRank
Jaber Hassan J. Al Yamani100.34
Farid Boussaïd220924.05
Amine Bermak349390.25
Dominique Martinez4265.73