Search for: All records

Current scientific experiments frequently involve control of specialized instruments (e.g., scanning electron mi- croscopes), image data collection from those instruments, and transfer of the data for processing at simulation centers. This process requires a “human-in-the-loop” to perform those tasks manually, which besides requiring a lot of effort and time, could lead to inconsistencies or errors. Thus, it is essential to have an automated system capable of performing remote instrumentation to intelligently control and collect data from the scientific instruments. In this paper, we propose a Remote Instrumentation Science Environment (RISE) for intelligent im- age analytics that provides the infrastructure to securely capture images, determine process parameters via machine learning, and provide experimental control actions via automation, under the premise of “human-on-the-loop”. The machine learning in RISE aids an iterative discovery process to assist researchers to tune instrument settings to improve the outcomes of experiments. Driven by two scientific use cases of image analytics pipelines, one in material science, and another in biomedical science, we show how RISE automation leverages a cutting-edge integration of cloud computing, on-premise HPC cluster, and a Python programming interface available on a microscope. Using web services, we implement RISE to perform automated image data collection/analysis guided by an intelligent agent to provide real-time feedback control of the microscope using the image analytics outputs. Our evaluation results show the benefits of RISE for researchers to obtain higher image analytics accuracy, save precious time in manually controlling the microscopes, while reducing errors in operating the instruments.