@article{
 title = {Wavelet filter for noise reduction and signal compression in an artificial nose},
 type = {article},
 year = {2007},
 keywords = {Artificial neural networks,Artificial noses,Wavelet transform},
 volume = {7},
 id = {1c3627ef-faea-3a4d-80f8-5a5b9852c795},
 created = {2019-02-14T18:02:00.144Z},
 file_attached = {false},
 profile_id = {74e7d4ea-3dac-3118-aab9-511a5b337e8f},
 last_modified = {2019-02-14T18:02:00.144Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This work presents results of the use of a wavelet filter for noise reduction and data compression of signals generated by artificial nose sensors. To verify the performance of the wavelet analysis in the treatment of odor patterns, we compare two widely used artificial nose classifiers, multi-layer perceptron neural network and time delay neural network in the analysis of signals generated by eight conducting polymer sensors exposed to gases derived from the petroliferous industry. © 2005 Elsevier B.V. All rights reserved.},
 bibtype = {article},
 author = {Zanchettin, C. and Ludermir, T.B.},
 doi = {10.1016/j.asoc.2005.06.004},
 journal = {Applied Soft Computing Journal},
 number = {1}
}

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