A texture representation should corroborate various functions of a texture. In this paper, we present a novel approach that incorporates texture features for retrieval in an examplar-based texture compaction and synthesis algorithm. The original texture is compacted and compressed in the encoder to obtain a thumbnail texture, which the decoder then synthesizes to obtain a perceptually high quality texture. We propose using a probabilistic framework based on the generalized EM algorithm to analyze the solutions of the approach. Our experiment results show that a high quality synthesized texture can be generated in the decoder from a compressed thumbnail texture. The number of bits in the compressed thumbnail is 400 times lower than that in the original texture and 50 times lower than that needed to compress the original texture using JPEG2000. We also show that, in terms of retrieval and synthesization, our compressed and compacted textures perform better than compressed cropped textures and compressed compacted textures derived by the patchwork algorithm.

Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.