Comparative Fault Tolerance of Parallel Distributed Processing Networks
IEEE Transactions on Computers
Machine Learning
OFDM Wireless LANs: A Theoretical and Practical Guide
OFDM Wireless LANs: A Theoretical and Practical Guide
Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices
Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices
Investigating the Fault Tolerance of Neural Networks
Neural Computation
Cyclostationarity: half a century of research
Signal Processing
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Defect-tolerant nanoelectronic pattern classifiers: Research Articles
International Journal of Circuit Theory and Applications - Nanoelectronic Circuits
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
EURASIP Journal on Applied Signal Processing
Robustness of radial basis functions
Neurocomputing
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
On Node-Fault-Injection Training of an RBF Network
Advances in Neuro-Information Processing
On Weight-Noise-Injection Training
Advances in Neuro-Information Processing
Integrating nanowires with substrates using directed assembly and nanoscale soldering
IEEE Transactions on Nanotechnology
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Cyclostationary Signatures in Practical Cognitive Radio Applications
IEEE Journal on Selected Areas in Communications
Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement
IEEE Transactions on Neural Networks
Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training
IEEE Transactions on Neural Networks
Complete and partial fault tolerance of feedforward neural nets
IEEE Transactions on Neural Networks
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We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.