Advanced Neural Optimization Techniques for Reliable MEMS Sensor Response Enhancement

Abstract

Micro-Electro-Mechanical Systems (MEMS) sensors play a crucial role in modern engineering applications, including aerospace navigation, industrial automation, healthcare monitoring, robotics, automotive systems, and Internet of Things (IoT) environments. Despite their widespread adoption, MEMS sensors often suffer from noise interference, nonlinear response characteristics, temperature-induced drift, sensitivity degradation, calibration errors, and environmental disturbances that adversely affect measurement accuracy and reliability. Traditional calibration and signal processing methods provide partial solutions but frequently fail to adapt to dynamic operating conditions and complex sensor behaviors. Recent advances in artificial intelligence, deep learning, and neural optimization have demonstrated significant potential for intelligent sensor enhancement through adaptive learning and automated response correction. This research proposes an Advanced Neural Optimization Technique for Reliable MEMS Sensor Response Enhancement (ANOT MEMS) that integrates MEMS signal acquisition, adaptive preprocessing, neural feature learning, deep optimization algorithms, intelligent calibration, nonlinear compensation, and response stabilization mechanisms.