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Content with rapid, moderate, and slow motion is frequently mixed together in real video sequences. Until now, no fast block-matching algorithm (FBMA), including the well-known three-step search (TSS), the block-based gradient descent search (BBGDS), and the diamond search (DS), can efficiently remove the temporal redundancy of sequences with wide range motion content. This paper proposes an adaptive FBMA, called A-TDB, to solve this problem. Based on the characteristics of a proposed predicted profit list, the A-TDB can adaptively switch search patterns among the TSS, DS, and BBGDS, according to the motion content. Experimental results reveal that the A-TDB successfully adopts the search patterns to remove the temporal redundancy of sequences with slow, moderate and rapid motion content.

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The debris flows caused by typhoon Morakot in August 2009 killed more than 600 people and caused US$ 500 million in damages to agriculture and forestry of Taiwan. Many lives can be saved if we can estimate the occurrence of debris flows and issue timely warnings to inhabitants. Previous literatures [1] have shown that rainfall is highly correlated with debris flows. Thus, the first step to debris flow monitoring and warning is to collect high-resolution, real-time precipitation in the debris-flow-prone areas. In this paper we discuss the considerations of designing a low-cost WSN-based rain gauge grid, which provides highresolution mapping of precipitation. Preliminary experimental results are presented.