Reinforcement Learning Based Sensor Optimization for Bio-markers
Sajal Khandelwal Pawan Kumar Syed Azeemuddin
Radio frequency (RF) biosensors, in particular those based on inter-digitated
capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote
sensing, and wireless communication. Despite their advantages of low cost and
easy fabrication, their sensitivity can be hindered by design imperfections,
environmental factors, and circuit noise. This paper investigates enhancing the
sensitivity of IDC-based RF sensors using novel reinforcement learning based
Binary Particle Swarm Optimization (RLBPSO), and it is compared to Ant Colony
Optimization (ACO), and other state-of-the-art methods. By focusing on
optimizing design parameters like electrode design and finger width, the
proposed study found notable improvements in sensor sensitivity. The proposed
RLBPSO method shows best optimized design for various frequency ranges when
compared to current state-of-the-art methods.