Robust filtering with randomly varying sensor delay: The finite-horizon case

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, we consider the robust filtering problem for discrete time-varying systems with delayed sensor measurement subject to norm-bounded parameter uncertainties. The delayed sensor measurement is assumed to be a linear function of a stochastic variable that satisfies the Bernoulli random binary distribution law. An upper bound for the actual covariance of the uncertain stochastic parameter system is derived and used for estimation variance constraints. Such an upper bound is then minimized over the filter parameters for all stochastic sensor delays and admissible deterministic uncertainties. It is shown that the desired filter can be obtained in terms of solutions to two discrete Riccati difference equations of a form suitable for recursive computation in online applications. An illustrative example is presented to show the applicability of the proposed method

The common nodulation genes of Astragalus sinicus rhizobia are conserved despite chromosomal diversity

The nodulation genes of Mesorhizobium sp. (Astragalus sinicus) strain 7653R were cloned by functional complementation of Sinorhizobium meliloti nod mutants. The common nod genes, nodD, nodA, and nodBC, were identified by heterologous hybridization and sequence analysis. The nodA gene was found to be separated from nodBC by approximately 22 kb and was divergently transcribed. The 2.0-kb nodDBC region was amplified by PCR from 24 rhizobial strains nodulating A. sinicus, which represented different chromosomal genotypes and geographic origins. No polymorphism was found in the size of PCR products, suggesting that the separation of nodA from nodBC is a common feature of A. sinicus rhizobia. Sequence analysis of the PCR-amplified nodA gene indicated that seven strains representing different 16S and 23S ribosomal DNA genotypes had identical nodA sequences. These data indicate that, whereas microsymbionts of A. sinicus exhibit chromosomal diversity, their nodulation genes are conserved, supporting the hypothesis of horizontal transfer of nod genes among diverse recipient bacteria

Multilabel region classification and semantic linking for colon segmentation in CT colonography

Accurate and automatic colon segmentation from CT images is a crucial step of many clinical applications in CT colonography, including computer-aided detection (CAD) of colon polyps, 3-D virtual flythrough of the colon, and prone/supine registration. However, the existence of adjacent air-filled organs such as the lung, stomach, and small intestine, and the collapse of the colon due to poor insufflation, render accurate segmentation of the colon a difficult problem. Extra-colonic components can be categorized into two types based on their 3-D connection to the colon: detached and attached extracolonic components (DEC and AEC, respectively). In this paper, we propose graph inference methods to remove extracolonic components to achieve a high quality segmentation. We first decompose each 3-D air-filled object into a set of 3-D regions. A classifier trained with region-level features can be used to identify the colon regions from noncolon regions. After removing obvious DEC, we remove the remaining DEC by modeling the global anatomic structure with an a priori topological constraint and solving a graph inference problem using semantic information provided by a multiclass classifier. Finally, we remove AEC by modeling regions within each 3-D object with a hierarchical conditional random field, solved by graph cut. Experimental results demonstrate that our method outperforms a purely discriminative learning method in detecting true colon regions, while decreasing extra-colonic components in challenging clinical data that includes collapsed cases

Intelligent Modeling and Verification (Editorial)

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