Neutron imaging and tomography with MCPs

A neutron imaging detector based on neutron-sensitive microchannel plates (MCPs) was constructed and tested at beamlines of thermal and cold neutrons. The MCPs are made of a glass mixture containing B-10 and natural Gd, which makes the bulk of the MCP an efficient neutron converter. Contrary to the neutron sensitive scintillator screens normally used in neutron imaging, spatial resolution is not traded off with detection efficiency. While the best neutron imaging scintillators have a detection efficiency around a percent, a detection efficiency of around 50% for thermal neutrons and 70% for cold neutrons has been demonstrated with these MCPs earlier. Our tests show a performance similar to conventional neutron imaging detectors, apart from the orders of magnitude better sensitivity. We demonstrate a spatial resolution better than 150 um. The sensitivity of this detector allows fast tomography and neutron video recording, and will make smaller reactor sites and even portable sources suitable for neutron imaging.Comment: Submitted to the proceedings of the 19th International Workshop on Radiation Imaging Detectors (iWoRiD) 2-6 July 2017, Krakow, Polan

NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation

In various applications, such as robotic navigation and remote visual assistance, expanding the field of view (FOV) of the camera proves beneficial for enhancing environmental perception. Unlike image outpainting techniques aimed solely at generating aesthetically pleasing visuals, these applications demand an extended view that faithfully represents the scene. To achieve this, we formulate a new problem of faithful FOV extrapolation that utilizes a set of pre-captured images as prior knowledge of the scene. To address this problem, we present a simple yet effective solution called NeRF-Enhanced Outpainting (NEO) that uses extended-FOV images generated through NeRF to train a scene-specific image outpainting model. To assess the performance of NEO, we conduct comprehensive evaluations on three photorealistic datasets and one real-world dataset. Extensive experiments on the benchmark datasets showcase the robustness and potential of our method in addressing this challenge. We believe our work lays a strong foundation for future exploration within the research community

Understanding the System Fit Challenge at the Initial Post-Adoption Stage: The Roles of Emotions in Users\u27 Adaptation Behaviors

Users’ adaptation behaviors are vital to the success of the system if there is a poor fit between task and technology at the initial post-adoption stage. However, prior studies have mixed results on how users adapt to the fit challenge. We draw on coping theory and appraisal theory of emotion to develop an encounter-emotion-coping framework to reconcile the mixed results by exploring the links between fit, emotions, individual adaptation and task-technology adaptation behaviors. The paths were tested through a survey of 283 nurses. Results suggest that emotions felt by users in the initial stage explain the relationship between fit and the two adaptation behaviors. This study (1) extends our understanding of the consequences of fit issue, (2) unveils the roles of different emotions in eliciting users’ adaptation behaviors, and (3) differentiates individual adaptation from task-technology adaptation in terms of their emotional antecedents. Implications for practice are discussed