One Dimensional

One-dimensional (1D) signal processing focuses on analyzing and manipulating data represented as sequences, a fundamental task across numerous scientific and engineering domains. Current research emphasizes developing efficient algorithms for tasks like signal classification, denoising, and parameter estimation, employing techniques ranging from linear scale-space methods and optimal transport-based transforms to convolutional neural networks (CNNs) and transformer architectures. These advancements are improving the accuracy and speed of applications in diverse fields, including medical diagnostics (e.g., ECG analysis, Parkinson's detection), image and video processing, and radio frequency spectrum sensing. The development of robust and computationally efficient 1D signal processing methods continues to be a significant area of investigation.