AI-based Image Processing Technique for Dielectrophoresis (DEP) in BioMEMs Applications

The integration of artificial intelligence (AI) methodology in the flexible electronics, BioMEMs and Dielectrophoresis (DEP) system provides an accurate, comprehensive, simple and cost-effective approach especially in the real time cell sorting, target identification, monitoring, biological entities separation, analysis and postulation. Here, a novel approach integrating AI algorithms with DEP technology for BioMEMs system has been demonstrated in real-time bacteria identification via detecting, identifying simultaneously, lock-in monitoring bacterial movement trajectories, analyzing, measuring and calculating bacteria’s velocities of different frequencies. The AI-based bacterial recognition system creates real-time, fast, accurate, unlimited bacteria lock-in monitoring with high accuracy. Furthermore, the bacteria can be tracked, monitored, and analyzed using big data analysis and Deep Learning (DL) in real-time via a visual control interface system, as part of the Internet of Things (IoT) and intelligent information sharing between BioMEMS sensors and IoT networks.