Sparse Sensor

Sparse sensor research focuses on reconstructing complete data from limited sensor measurements, aiming to improve efficiency and reduce costs in various applications. Current research emphasizes developing optimized sensor placement strategies using physics-based criteria or differentiable programming, coupled with machine learning models like neural networks (including implicit neural representations and graph neural networks) and advanced algorithms such as ADMM for data processing and reconstruction. This field is significant for its potential to enhance data acquisition in diverse areas, from robotics and traffic monitoring to medical imaging and environmental sensing, by enabling accurate estimations with fewer, less expensive sensors.