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Creators/Authors contains: "Alexander, Iwan D."

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  1. ; ; ; (, 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS))
    This paper demonstrates a coupled Schmitt trigger oscillator based oscillator neural network (SMT-ONN) for pattern recognition applications. Unlike previous ONN models, the SMT-ONN can be easily realized in both hardware and software levels. A mathematical model of the Schmitt Trigger Oscillator as well as the corresponding CMOS circuit are presented to validate the mathematical model. The SMT-ONN can realize the pattern recognition task by considering the convergence time and frequency as the recognition indicators. A Kuramoto model based frequency synchronization approach is utilized, and simulation results indicate less than 160 ms convergence time and close frequency match for a simplified pattern recognition application. 
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