A sensitivity analysis for nonrandomly missing categorical by Baker S. G. PDF

By Baker S. G.

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8 and 4 megabytes, respectively. For these experiments, we first turned on the sensor and had it capture, compress, and stream data. The experiment then turned the network card on and off for the times shown in Figure 9(a). The “on” times are indicated by a value of 1 in the graph, while the “off ” state is shown as a value of 0. As ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 1, No. 2, May 2005. Panoptes: Scalable Low-Power Video Sensor Networking Technologies • 165 Fig.

2003] uses both local and global perceptual features to annotate images. Finally, Zhang et al. suggest the use of “semantic feature vector” to model images and incorporate the semantic classification into the relevance feedback for image retrieval [He et al. 2002; Wenyin et al. 2001; Wu et al. 2002]. All these approaches assume that C, P , and L are fixed. The ability to improve C, P , and L is where our proposed CDE approach differs from these static ones. The core of CDE is its ability to assess classification confidence.

It should be noted that the compression times using the IPP are dependent on the actual video content. In comparing the two platforms, it appears that the Stargate platform is able to outperform the Bitsy platform using the Intel Performance Primitives but cannot outperform it using the software compression algorithm. We believe that this is due to the fact that (i) the Xscale device has a faster processor and can take advantage of it when the working set is relatively small and (ii) the memory accesses in the Stargate seem to be a little slower than on the Bitsy.

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A sensitivity analysis for nonrandomly missing categorical data arising from a national health disab by Baker S. G.

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