Abstract
The standard operation refinement ordering is a kind of "meet of opposites": non-determinism reduction suggests "smaller" behaviour while increase of definition suggests "larger" behaviour. Groves' factorization of this ordering into two simpler relations, one per refinement concern, makes it more mathematically tractable but is far from fully exploited in the literature. We present a pointfree theory for this factorization which is more agile and calculational than the standard set-theoretic approach. In particular, we show that factorization leads to a simple proof of structural refinement for arbitrary parametric types and exploit factor instantiation across different subclasses of (relational) operation. The prospect of generalizing the factorization to coalgebraic refinement is discussed.

Abstract
Capsule endoscopy is a recent technology with a clear need for automatic tools that reduce the long exam annotation times of exams. We have previously developed a topographic segmentation method, which is now improved by using spatial and temporal position information. Two approaches are studied: using this information as a confidence measure for our previous segmentation method, and direct integrating of this data into the image classification process. These allow us not only to automatically know when we have obtained results with error magnitudes close to human errors, but also to reduce these automatic errors to much lower values. All the developed methods have been integrated in the CapView annotation software, currently used for clinical practice in hospitals responsible for over 250 capsule exams per year, and where we estimate that the two hour annotation times are reduced by around 15 minutes.

Abstract
We present a group membership protocol specially designed for next generation communication systems for real-time safety-critical applications such as FlexRay and FTT-CAN. The proposed protocol imposes an overhead of two bits per processor per communication cycle, when the system is in a quiescent state, and is able to tolerate benign failures of up to half of the group members between consecutive executions. Additionally, it removes a faulty processor within two communication cycles in the worst case and reintegrates a processor at the latest two communication cycles after it recovers. Compared with protocols developed for similar systems, it is as tolerant as the most robust protocol with a traffic overhead slightly higher than the most efficient protocol, which is much less robust. © 2006 IEEE.

Abstract
This paper presents a study of the application of artificial neural network (ANN) to the evaluation of the distance to the voltage collapse point. Voltage stability has been of the major concern in power system operation. To prevent these problems, technical staff evaluates frequently the distance of the operation state to the voltage collapse point. This distance normally is calculated with power flow equations. This classic technique is very slow for electric power systems with large dimension. In abnormal exploration situations it may introduce serious limitation in the voltage stability analysis process. So, the application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper also presents the method FSQV (Full sum dQ/dV) for the detection of the collapse point.

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Abstract