Peak Detection Program for Identifying Maximum Values in Signal Sets

This article describes a program designed for detecting maximum values in signal sets, commonly referred to as peak detection. The implementation comprises a series of algorithms and computational steps, including signal acquisition through input interfaces, digital filtering using techniques like moving average or Butterworth filters to reduce noise, and peak calculation through comparative analysis of signal amplitudes. Key functions involve sliding window algorithms for real-time processing or full-dataset scanning for offline analysis, with threshold-based validation to distinguish true peaks from noise artifacts. Through this structured approach, the program enables accurate identification of signal maxima, facilitating deeper understanding of signal characteristics and behaviors. The modular architecture supports diverse applications including audio processing (e.g., volume peak detection), image processing (e.g., brightness spike identification), and industrial automation (e.g., sensor reading analysis).