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The indexing of spatio-temporal data is important for retrieval by spatio-temporal queries. The previous techniques on spatio-temporal indexing miss the semantics of the application since they are usually based on traditional indexing structures that has little to no semantic information incorporated. In those systems, the semantic queries were executed by using the low-level index structures. In this paper, we introduce a novel indexing method for spatiotemporal data: semantic sequence state graph (S 3 G). S 3 G maintains the properties of events-objectslocations for efficient spatio-temporal queries. In S 3 G, the spatial information is maintained in states whereas semantic events that result in temporal ordering link the states. S 3 G supports our SMART (semantic modeling and retrieval) system.

The objective of an image projection is to obtain high resolution images from low resolution inputs. The generation of low resolution images can be modeled as a combination of smoothing and down sampling operations of the natural scenes by low quality sensors. Super resolution is an inverse problem of this generation process, so it is under-determined due to the information loss. One criteria of solving this inverse problem is minimizing the reconstruction error as well as computation time. There are so many methods are proposed on this concepts where as all methods are aimed to enhance either accuracy or to reduce computation time. In this paper a new hybrid system was proposed for image projection. This paper considers the main factors that effect the image projection, speed and accuracy. It incorporates the bi cubic interpolation for efficient image projection, an adaptive bilateral filtration for edge preservation and also an improved OMP (lOMP) algorithm for less computation time. The simulation result shown in above section illustrates the effectiveness of the proposed hybrid approach in various regards.