Signal Detection

Signal detection research focuses on reliably identifying meaningful signals amidst noise across diverse applications, from communication systems to medical imaging and gravitational wave astronomy. Current efforts leverage machine learning, particularly deep neural networks (DNNs) and convolutional neural networks (CNNs), along with iterative algorithms and novel architectures like continuous-time attention networks, to improve detection accuracy and efficiency, often addressing challenges posed by low signal-to-noise ratios and complex data structures. These advancements are crucial for enhancing the performance of various technologies and scientific instruments, enabling more sensitive and accurate analyses in fields ranging from healthcare to astrophysics. The development of robust and computationally efficient methods remains a key focus.