Filter Design

Filter design, encompassing the creation of algorithms and systems to selectively process signals, aims to optimize signal quality, reduce complexity, and enhance efficiency in various applications. Current research emphasizes adaptive and online filter design methods, particularly within neural networks (like RNNs) and Kalman filters, addressing challenges like noise reduction, sample rate adjustment, and handling evolving data streams in graph-based systems. These advancements are crucial for improving performance in diverse fields, including audio processing, telecommunications, machine learning, and robotics, by enabling more robust and efficient signal processing.