Sampling Rate

Sampling rate, the frequency at which data is collected, is a critical parameter influencing the accuracy and efficiency of various signal processing applications. Current research focuses on developing methods to optimize sampling rates, including adaptive sampling techniques that adjust rates based on data content or needs, and the use of deep learning models like diffusion models, generative adversarial networks (GANs), and recurrent neural networks (RNNs) to handle varying or low sampling rates effectively. These advancements are improving the performance of diverse applications, from audio processing and image reconstruction to energy disaggregation and structural health monitoring, by enabling more efficient data acquisition and processing.