The main features, implemented and tested, include:Fully configurable camera  to adapt the pixel response at any experiment condition.Full streaming data acquisition architecture  continuous data acquisition without dead readout time. On-line image-based self-event trigger (Fast reject)and Region-of-Interest readout architecture  intelligent selection of the Region-of-Interest of the frame for reducing the amount of output data and, significantly increase the camera frame-rate.

High-speed X-ray imaging applications play a crucial role for non-destructive investigations of the dynamics in material science and biology. On-line data analysis is necessary for quality assurance and data-driven feedback, leading to a more efficent use of a beam time and increased data quality. In this article we present a smart camera platform with embedded Field Programmable Gate Array (FPGA) processing that is able to stream and process data continuously in real-time. The setup consists of a Complementary Metal-Oxide-Semiconductor (CMOS) sensor, an FPGA readout card, and a readout computer. It is seamlessly integrated in a new custom experiment control system called Concert that provides a more efficient way of operating a beamline by integrating device control, experiment process control, and data analysis. The potential of the embedded processing is demonstrated by implementing an image-based trigger. It records the temporal evolution of physical events with increased speed while maintaining the full field of view. The complete data acquisition system, with Concert and the smart camera platform was successfully integrated and used for fast X-ray imaging experiments at KIT's synchrotron radiation facility ANKA.Index Terms-CMOS image sensors, control systems, data processing, FPGAs, smart cameras. 0018-9499

Federated identity management (FIM) is an arrangement that can be made among multiple organisations that lets subscribers use the same identification data to obtain access to the secured resources of all organisations in the group. In many research communities there is an increasing interest in a common approach to FIM as there is obviously a large potential for synergies. FIM4R [1] provides a forum for communities to share challenges and ideas, and to shape the future of FIM for our researchers. Current participation covers high energy physics, life sciences and humanities, to mention but a few. In 2012 FIM4R converged on a common vision for FIM, enumerated a set of requirements and proposed a number of recommendationsfor ensuring a roadmap for the uptake of FIM [2]. In summer 2018, FIM4R published an updated version of this paper [3]. The High Energy Physics (HEP) Community has been heavily involved in creating both the original white paper and the new version, which documented the progress made in FIM for Research, in addition to the current challenges. This paper presents the conclusions of this second FIM4R white paper and a summary of the identified requirements and recommendations. We focus particularly on the direction being taken by the Worldwide LHC Computing Grid (WLCG), through the WLCG Authorisation Working Group, and the requirements gathered from the HEP Community.

With the increasing acceptance of multiple authentication mechanisms, federated infrastructures need to provide means of keeping consistency between multiple user identities. Although the current authentication and authorization infrastructures are designed to support multiple ways of authentication (SAML, OpenID Connect, X.509), they are missing unified protocols and interfaces to harmonize multiple user identities. This article introduces the concept of identity harmonization for federated cloud services. Our approach is based on the standardized System for Cross-domain Identity Management (SCIM) protocol. We add the support for account linking and per-service verification. Furthermore, the concept is put into context of currently existing federated infrastructures and is exemplified within a federated e-infrastructure currently developed in the course of the INDIGO-Datacloud project. The concept is evaluated in the INDIGO testbed in terms of deployability, scalability, provisioning and deprovisioning of user accounts, as well as maintenance and integration effort.

The main features, implemented and tested, include:Fully configurable camera  to adapt the pixel response at any experiment condition.Full streaming data acquisition architecture  continuous data acquisition without dead readout time. On-line image-based self-event trigger (Fast reject)and Region-of-Interest readout architecture  intelligent selection of the Region-of-Interest of the frame for reducing the amount of output data and, significantly increase the camera frame-rate.