Towards a broad-coverage graphemic analysis of large historical corpora

This paper presents a method which we are developing to explore graphemic variation in large historical corpora of German. Historical corpora provide an amount of data at the level of graphemics which cannot be handled exhaustively using common methods of manual evaluation. To deal with this challenge, we apply methods from computational linguistics to pave the way for a broad-coverage graph(em)ic analysis of large historical corpora. In this paper, we show how our approach can be applied to the Reference Corpus of Middle High German. Illustrating our method and linguistic analysis, we present findings from our investigations into diatopic and/or diachronic variation as documented in 13th and 14th century charters (Urkunden) from the corpus.

Decomposition of Target Coverage Graph in Wireless Sensor Network

In order to reduce the algorithm complexity of the target coverage problem in Wireless Sensor Network, three kinds of approaches are designed to decrease the network scale, that is, one is deleting the redundant sensor nodes, another is removing the redundant targets, and the third is decomposing the target coverage graph into multiple independent sub graphs. This paper proves that the three approaches are correct, and presents the Construct Independent Sub Graph Algorithm( CISGA) to divide target coverage graph. Experiment results show that CISGA can reduce 30% of network scale and greatly reduce the algorithm complexity of target coverage problem.