@inproceedings{
    peltonen08,
    editor = {Principe, Jose C. and Erdogmus, Deniz and Adali, T\"ulay},
    author = "Peltonen, Jaakko and Uusitalo, Mikko A. and Pajarinen, Joni",
    responsibleperson = "Joni Pajarinen",
    title = "{Nano-scale fault tolerant machine learning for cognitive radio}",
    url = "http://dx.doi.org/10.1109/MLSP.2008.4685473",
    booktitle = "{Proceedings of IEEE International Workshop on Machine Learning for Signal Processing}",
    address = "Canc\'un, Mexico",
    abstract = "We introduce a machine learning based channel state classifier for cognitive radio, designed for nano-scale implementation. The system uses analog computation, and consists of cyclostationary feature extraction and a radial basis function network for classification. The description of the system is partially abstract, but our design choices are motivated by domain knowledge and we believe the system will be feasible for future nanotechnology implementation. We describe an error model for the system, and simulate experimental performance and fault tolerance of the system in recognizing WLAN signals, under different levels of input noise and computational errors. The system performs well under the expected non-ideal manufacturing and operating conditions.\par $\copyright$ 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
    month = "October",
    flags = "AIRC HIIT public",
    year = "2008",
    internalpdf = "peltonen08_mlsp.pdf",
    impactfactor = "D3",
    pages = "163-168"
}