Research on Noise Reduction Method for Broad-Spectrum Electromagnetic Radiation Detection Data Based on Improved Wavenet Model

In the realm of electromagnetic radiation (EMR) data collection and simulation, where noise is an ever-present challenge, this study introduces a novel approach for noise reduction employing an enhanced WaveNet model. EMR is deeply intertwined with modern life, finding applications in everything from household Wi-Fi networks to advanced mobile communications and industrial instrumentation. Nevertheless, the potential health and environmental ramifications of EMR necessitate the precise monitoring and assessment of its impact. The central issue in EMR monitoring lies in effectively distinguishing the valuable signal from intrusive background noise. Our method combines the capabilities of WaveNet, a potent deep learning architecture, with a Fast Fourier Transform (FFT) module and an atention mechanism. This enhancement aims to harness WaveNet's prowess in extracting meaningful features and its innate self-atention mechanisms to elevate the quality of EMR data by reducing noise. In our comprehensive experimental investigations, we conducted a comparative analysis, piting classical filtering techniques, including median and Gaussian filters, against neural network algorithms such as LSTM, GRU, and TCN. The outcomes undeniably underscore the superior performance of our proposed method in denoising electromagnetic radiation data. This advancement not only bolsters the quality of the data but also provides substantial support for the broader endeavor of EMR monitoring and assessment. It is noteworthy that both traditional methods and advanced neural network techniques have demonstrated their effectiveness in handling EMR data denoising. However, our proposed approach, while building upon these foundations, modestly presents itself as a notable enhancement. It is this humble enhancement that we believe contributes to the ongoing pursuit of refining EMR monitoring and assessment. In summation, our research humbly advances the field of noise reduction in EMR data through a novel approach, highlighting the incremental improvement brought about by our enhanced WaveNet model when juxtaposed with classical filtering and neural network algorithms. This contribution serves as a vital step toward enhancing the accuracy and dependability of electromagnetic radiation data collection and analysis.