Wavelet Transform

The wavelet transform is a mathematical tool that decomposes signals into different frequency components, enabling efficient analysis and processing across various scales. Current research focuses on integrating wavelet transforms with deep learning architectures, such as convolutional neural networks and transformers, to improve performance in diverse applications like image denoising, medical image analysis, and machine condition monitoring. This combination leverages the wavelet's ability to extract relevant features from complex data, enhancing the accuracy and efficiency of machine learning models. The resulting advancements have significant implications for numerous fields, improving diagnostic accuracy in medicine, enhancing signal processing in engineering, and enabling more robust and efficient data analysis across scientific disciplines.