Stochastic Resonance Methods and Applications for Weak Signal Detection

This doctoral dissertation investigates stochastic resonance methods and their applications in weak signal detection. The research conducts in-depth analysis of stochastic resonance phenomena for weak signals, exploring novel detection techniques and methodologies. Key technical contributions include algorithm implementations for enhancing signal-to-noise ratios through bistable system modeling, where potential functions and Langevin equations are numerically solved using Runge-Kutta methods. The study examines practical applications across multiple domains including wireless communication systems (implementing matched filter enhancements with stochastic resonance preprocessing), medical imaging (adaptive threshold algorithms for MRI noise suppression), and physical experiments (FPGA-based real-time signal processing architectures). The dissertation proposes innovative approaches such as parameter-optimized stochastic resonance models with genetic algorithm tuning and multi-scale wavelet transform integration. These advancements provide valuable references for advancing weak signal detection technologies, featuring MATLAB/Python code implementations for system validation and performance comparison metrics including bit error rate analysis and receiver operating characteristics.