On Sensing Principles Using Temporally Extended Bar Codes

Vasileios Athanasiou, Kiran Kumar Tadi, Mattan Hurevich, Shlomo Yitzchaik, Aldo Jesorka, Zoran Konkoli*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.

Original languageAmerican English
Article number9019829
Pages (from-to)6782-6791
Number of pages10
JournalIEEE Sensors Journal
Volume20
Issue number13
DOIs
StatePublished - 1 Jul 2020

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • Biosensors
  • Internet of Things
  • bar codes
  • data compression
  • ionic variations
  • pattern recognition

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