We present a versatile scheduler for automated telescope observations and operations. The main objective is to optimize telescope use, while taking alerts (e.g., Gamma-Ray Bursts), weather conditions, and mechanical failures into account. Based on our previous experiment, we propose a two steps approach. First, a daily module develops plan schemes during the day that offer several possible scenarii for a night and provide alternatives to handle problems. Secondly, a nightly module uses a reactive technique --driven by events from different sensors-- to select at any moment the "best" block of observations to launch from the current plan scheme. In addition to a classical scheduling problem under resource constraints, we also want to provide dynamic reconfiguration facilities. The proposed approach is general enough to be applied to any other type of telescope, provided that reactivity is important.
Our objective is to offer clinicians wider access to evolving medical image processing (MIP) techniques, crucial to improve assessment and quantification of physiological processes, but difficult to handle for non-specialists in MIP. Based on artificial intelligence techniques, our approach consists in the development of a knowledge-based program supervision system, automating the management of MIP libraries. It comprises a library of programs, a knowledge base capturing the expertise about programs and data and a supervision engine. It selects, organizes and executes the appropriate MIP programs given a goal to achieve and a data set, with dynamic feedback based on the results obtained. It also advises users in the development of new procedures chaining MIP programs.. We have experimented the approach for an application of factor analysis of medical image sequences as a means of predicting the response of osteosarcoma to chemotherapy, with both MRI and NM dynamic image sequences. As a result our program supervision system frees clinical end-users from performing tasks outside their competence, permitting them to concentrate on clinical issues. Therefore our approach enables a better exploitation of possibilities offered by MIP and higher quality results, both in terms of robustness and reliability.
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