Narrow Results

Fixed Field Alternating Gradient (FFAG) elements are usually designed in a circular shape, but straight sections can also be imagined. A demonstration using the scaling condition is first shown. Combining different scaling FFAG sections is then discussed and opens new possibilities for scaling FFAGs. As an example, an application with a new lattice proposal for the PRISM project is finally presented in this paper.

The back-off procedure is one of the media access control technologies in 802.11P communication protocol. It plays an important role in avoiding message collisions and allocating channel resources. Formal methods are effective approaches for studying the performances of communication systems. In this paper, we establish a discrete time model for the back-off procedure. We use Markov Decision Processes (MDPs) to model the non-deterministic and probabilistic behaviors of the procedure, and use the probabilistic computation tree logic (PCTL) language to express different properties, which ensure that the discrete time model performs their basic functionality. Based on the model and PCTL specifications, we study the effect of contention window length on the number of senders in the neighborhood of given receivers, and that on the station’s expected cost required by the back-off procedure to successfully send packets. The variation of the window length may increase or decrease the maximum probability of correct transmissions within a time contention unit. We propose to use PRISM model checker to describe our proposed back-off procedure for IEEE802.11P protocol in vehicle network, and define different probability properties formulas to automatically verify the model and derive numerical results. The obtained results are helpful for justifying the values of the time contention unit.

Classification rules (if-then rules) are an intuitive way of performing classification, and they are a popular alternative to decision trees. Covering algorithms are one of the most well-studied methods for inducing classification rules from training sets. This chapter discusses basic covering algorithms and illustrates them with simple examples that capture the essence of the problems. In addition, some of the current applications of covering algorithms are reviewed.