Multifrequency Inductive Sensor System for Classification of Bimetallic Coins

Abstract

The reliable classification of coin sets from different countries and counterfeit coins is an essential means of securing cash circulation. Especially bimetallic coins, such as 2-Euro coins, are often subjected to counterfeit by mixing them with coins of other countries or by imitations. In this article, a real-time embedded sensor system is proposed, based on inductance spectroscopy to characterize and identify bimetallic coins having similar geometric properties and looking similar at a first view. The system is based on inductance spectroscopy varying the penetration depth of the magnetic field in the bimetallic structure of the coin, which generally contains buried layers of other metals. The experimental evaluation shows that the bimetallic coins from different countries can be identified and classified by the use of the support vector machine, a machine learning algorithm. Experimental results showed that the system reached a classification accuracy up to 100% within a response rate of 36.62 ms per five bimetallic coins.