computationalsensing

Computational sensing, sometimes written as computationalsensing, is an interdisciplinary field that merges sensing hardware with computation to extract information about the physical world. It treats sensor data as observations of an underlying phenomenon and uses priors, models, and learning-based inference to produce estimates, reconstructions, or decisions that go beyond raw measurements.

Core ideas include joint design of sensing and processing, adaptive or closed-loop sensing, and solving ill-posed inference problems with regularization and prior knowledge. Techniques range from model-based inverse problems and calibration to learning-based methods such as deep neural networks and probabilistic inference, including hybrid approaches that combine physics-based models with data-driven components.

Applications span medical imaging and sensing, computer vision, robotics and autonomous systems, environmental monitoring, and consumer electronics. Examples include super-resolution from limited data, denoising and deblurring, sparse sensing networks, and sensor fusion for robust state estimation.

Challenges include data efficiency, generalization across devices and environments, latency and energy constraints, privacy and security concerns, and the need for reliable uncertainty quantification. The field emphasizes hardware-software co-design and end-to-end pipelines, extending concepts from computational imaging to diverse sensing modalities.

Future directions include edge computing, multi-modal fusion, physics-informed models, and adaptive sensing that optimize information gain in real time.