Design of an Embedded System for Fast Classification of Bimetallic Coins by Impedance Spectroscopy

A novel embedded impedance measurement system for counterfeit detection of bimetallic coins is presented. The system integrates Eddy current sensors with inductive spectroscopy, facilitating the detection of hidden inlay security features and the identification of surface minting. A salient feature of the system is the use of a single Eddy current sensor that operates across multiple frequencies. This feature eliminates the need for extensive calibration procedures typically required when multiple sensor coils are used for each excitation signal frequency. The system employs an undersampling technique, facilitating impedance measurements over a wide frequency in MHz range using a simple microcontroller. The employment of machine learning-based classification further enhances the system's accuracy, enabling precise coin classification. The system's high-speed classification capability of 203 coins per second leads to a substantial enhancement in counterfeit detection while reducing the system footprint, cost, and power consumption. The classification algorithm has been rigorously tested on multiple datasets with varying difficulty levels, ensuring its robustness and reliability under different conditions. The compact, real-time, and cost-effective design of a system represents a significant breakthrough in modern coin counterfeit detection, setting new standards for accuracy and efficiency.