A coupled schmitt trigger oscillator neural network for pattern recognition applications

Ting Zhang; Mohammad R. Haider; Iwan D. Alexander; Yehia Massoud

doi:10.1109/MWSCAS.2018.8624010

A coupled schmitt trigger oscillator neural network for pattern recognition applications

Ting Zhang, Mohammad R. Haider, Iwan D. Alexander, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Midwest Symposium on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	238-241
Number of pages	4
ISBN (Print)	9781538673928
DOIs	https://doi.org/10.1109/MWSCAS.2018.8624010
State	Published - Jan 22 2019
Externally published	Yes

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10.1109/MWSCAS.2018.8624010

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@inproceedings{e94e6403760042bfae56c87fab9d33e4,

title = "A coupled schmitt trigger oscillator neural network for pattern recognition applications",

abstract = "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.",

author = "Ting Zhang and Haider, {Mohammad R.} and Alexander, {Iwan D.} and Yehia Massoud",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-13",

year = "2019",

month = jan,

day = "22",

doi = "10.1109/MWSCAS.2018.8624010",

language = "English (US)",

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booktitle = "Midwest Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - GEN

T1 - A coupled schmitt trigger oscillator neural network for pattern recognition applications

AU - Zhang, Ting

AU - Haider, Mohammad R.

AU - Alexander, Iwan D.

AU - Massoud, Yehia

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13

PY - 2019/1/22

Y1 - 2019/1/22

N2 - 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.

AB - 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.

UR - https://ieeexplore.ieee.org/document/8624010/

UR - http://www.scopus.com/inward/record.url?scp=85062213810&partnerID=8YFLogxK

U2 - 10.1109/MWSCAS.2018.8624010

DO - 10.1109/MWSCAS.2018.8624010

M3 - Conference contribution

SN - 9781538673928

SP - 238

EP - 241

BT - Midwest Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

A coupled schmitt trigger oscillator neural network for pattern recognition applications

Abstract

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