Proc. SPIE. 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007
KEYWORDS: Control systems, Field programmable gate arrays, Calibration, Sensors, Signal detection, Electronic filtering, Human-machine interfaces, Analog electronics, Phase measurement, Signal processing
The paper describes the hardware and software architecture of a control and measurement system for electromagnetic field
stabilization inside the radio frequency electron gun, in FLASH experiment. A complete measurement path has been
presented, including I and Q detectors and FPGA based, low latency digital controller. Algorithms used to stabilize the
electromagnetic field have been presented as well as the software environment used to provide remote access to the control
device. An input signal calibration procedure has been described as a crucial element of measurement process.
Upcoming Low Level Radio Frequency (LLRF) systems for particle accelerators are facing manifold challenges. Special purpose machines like the X-Ray Free Electron Laser (XFEL) demand for field stabilities of 0.01% in amplitude and 0.01° in phase for the pulsed operation of superconducting, high-Q resonators. Due to the large number of parameters in a LLRF systems, automated procedures for parameter optimization will be essential. Apart from the field stability, manageability is an important topic. The International Linear Collider (ILC) will exceed the number of 20000 superconducting resonators and several hundred LLRF control loops. It is clear, that automation for this enormous amount of LLRF stations is inevitable. This document describes a systematic approach to implement several automation tasks using the technique of finite state machines (FSM). It is general enough to be applicable on various types of accelerators. A prototype of such an implementation is currently tested at the VUV-FEL at DESY, Hamburg. Some test results will be presented.
A shape model for full automatic segmentation and recognition of lateral lumbar spine radiographs has been developed. The shape model is able to learn the shape variations from a training dataset by a principal component analysis of the shape information. Furthermore, specific image features at each contour point are added into models of gray value profiles. These models were computed from a training dataset consisting of 25 manually segmented lumbar spines. The application of the model containing both shape and image information is optimized on unknown images using a simulated annealing search first to acquire a coarse localization of the model. Further on, the shape points are iteratively moved towards image structures matching the gray value models. During optimization the shape information of the model assures that the segmented object boundary stays plausible. The shape model was tested on 65 unknown images achieving a mean segmentation accuracy of 88% measured from the percental cover of the resulting and manually drawn contours.