A visual servoing path-planning strategy for cameras obeying the unified model

Part of 2010 IEEE Multi-Conference on Systems and ControlRecently, a unified camera model has been introduced in visual control systems in order to describe through a unique mathematical model conventional perspective cameras, fisheye cameras, and catadioptric systems. In this paper, a path-planning strategy for visual servoing is proposed for any camera obeying this unified model. The proposed strategy is based on the projection onto a virtual plane of the available image projections. This has two benefits. First, it allows one to perform camera pose estimation and 3D object reconstruction by using methods for conventional camera that are not valid for other cameras. Second, it allows one to perform image pathplanning for multi-constraint satisfaction by using a simplified but equivalent projection model, that in this paper is addressed by introducing polynomial parametrizations of the rotation and translation. The planned image trajectory is hence tracked by using an IBVS controller. The proposed strategy is validated through simulations with image noise and calibration errors typical of real experiments. It is worth remarking that visual servoing path-planning for non conventional perspective cameras has not been proposed yet in the literature. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD), Yokohama, Japan, 8-10 September 2010. In Proceedings of CACSD, 2010, p. 1795-180

Analysis on the critical factors of over-time and over-pay problems for government engineering project construction

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Classification of gluteal muscle contracture in children and outcome of different treatments

<p>Abstract</p> <p>Background</p> <p>Gluteal muscle contracture (GMC) is a clinical syndrome due to multiple etiologies in which hip movements may be severely limited. The aim of this study was to propose a detailed classification of GMC and evaluate the statistical association between outcomes of different management and patient conditions.</p> <p>Methods</p> <p>One hundred fifty-eight patients, who were treated between January 1995 and December 2004, were reviewed at a mean duration of follow-up of 4.8 years. Statistical analyses were performed using X²and Fisher's exact tests.</p> <p>Results</p> <p>Non-operative management (NOM), as a primary treatment, was effective in 19 of 49 patients (38.8%), while operative management was effective in all 129 patients, with an excellence rating of 83.7% (108/129). The outcome of NOM in level I patients was significantly higher than in level II and III patients (<it>P </it>< 0.05). The results of NOM and operative management in the child group were better than the adolescent group (<it>P </it>< 0.05). Complications in level III were more than in level II.</p> <p>Conclusion</p> <p>NOM was more effective in level I patients than in level II and III patients. Operative management was effective in patients at all levels, with no statistical differences between levels or types. We recommend NOM as primary treatment for level I patients and operative management for level II and III patients. Either NOM or operative management should be carried out as early as possible.</p

Variations of Particle Size Distribution, Black Carbon, and Brown Carbon during a Severe Winter Pollution Event over Xi'an, China

Real-time particulate matter (PM) size distributions, 4-hour time resolution, PM2.5, carbonaceous materials, and their optical properties were measured during a severe pollution event in Xi&#39;an, China High PM2.5 /PM10 ratios were observed on both pollution (0.83) and non-pollution (0.73) days, emphasizing the abundance of fine particles during sampling days. The particle number (PN) first peaked with a wide size range (30-100 nm) before morning rush hours (approximately 01:00-05:00) on pollution and non-pollution days, demonstrating that PN was governed by the accumulation of freshly emitted diesel particles and characterized by distinct aerosol condensation growth. By contrast, the second peak time and size range differed between pollution and non-pollution days because of different formation mechanisms The light-absorbing coefficients of both black carbon (BC, b(abs-880nm,BC)) and brown carbon (BrC, b(abs-370nm, BrC)) were high on pollution days and decreased to approximately half of those values on non-pollution days, indicating that the degree of light absorption is reduced by rain. The diurnal variation in b(abs-880nm, BC) pollution peaked with traffic on January 1 and 2. By contrast, it remained in relatively stable and high ranges (120-160 Mm(-1)) in the second period (January 3-5) without traffic peaks, illustrating that the dominant sources changed even during the same pollution period. High values of both b(abs-370nm, BrC) and b(abs-880nm,) (BC )coincided in the afternoon and evening due to emissions from primary sources, and abundant aqueous secondary organic carbon, respectively. A highly variable mass absorption coefficient of BrC also indicated the variety of fuel combustion sources of primary BrC in Xi&#39;an

Composition of gut microbiota in infants in China and global comparison

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The Sound of Topology in the AdS/CFT Correspondence

Using the gauge/gravity correspondence, we study the properties of 2-point correlation functions of finite-temperature strongly coupled gauge field theories, defined on a curved space of general spatial topology with a dual black hole description. We derive approximate asymptotic expressions for the correlation functions and their poles, supported by exact numerical calculations, and study their dependence on the dimension of spacetime and the spatial topology. The asymptotic structure of the correlation functions depends on the relation between the spatial curvature and the temperature, and is noticeable when they are of the same order. In the case of a hyperbolic topology, a specific temperature is identified for which exact analytical solutions exist for all types of perturbations. The asymptotic structure of the correlation functions poles is found to behave in a non-smooth manner when approaching this temperature.Comment: 65 pages, LaTeX, 21 figures, 1 table; fixed a small error in subsection 3.

Conditional Acceptability for Random Variables

Acceptable random variables introduced by Giuliano Antonini et al. (J. Math. Anal. Appl. 338:1188-1203, 2008) form a class of dependent random variables that contains negatively dependent random variables as a particular case. The concept of acceptability has been studied by authors under various versions of the definition, such as extended acceptability or wide acceptability. In this paper, we combine the concept of acceptability with the concept of conditioning, which has been the subject of current research activity. For conditionally acceptable random variables, we provide a number of probability inequalities that can be used to obtain asymptotic results

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

Room temperature ethyl formate fuel cells for consumer electronics

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