Toward Guaranteed Illumination Models for Non-Convex Objects

Illumination variation remains a central challenge in object detection and recognition. Existing analyses of illumination variation typically pertain to convex, Lambertian objects, and guarantee quality of approximation in an average case sense. We show that it is possible to build V(vertex)-description convex cone models with worst-case performance guarantees, for non-convex Lambertian objects. Namely, a natural verification test based on the angle to the constructed cone guarantees to accept any image which is sufficiently well-approximated by an image of the object under some admissible lighting condition, and guarantees to reject any image that does not have a sufficiently good approximation. The cone models are generated by sampling point illuminations with sufficient density, which follows from a new perturbation bound for point images in the Lambertian model. As the number of point images required for guaranteed verification may be large, we introduce a new formulation for cone preserving dimensionality reduction, which leverages tools from sparse and low-rank decomposition to reduce the complexity, while controlling the approximation error with respect to the original cone

Prioritising Emergency Bridgeworks Assessment under Military Consideration using an Enhanced Fuzzy Weighted Average Approach

Prioritising emergency bridgeworks assessment has been a key to winning battles in combat circumstances because of soldier safety, attack or defence tactics, and logistic supply ability. However, an imprecise or vague satisfaction level of importance of criteria may also affect the prioritising evaluation of bridgeworks under military consideration. In this paper, the fuzzy set theory is employed to treat this aspect. With linguistic variables, fuzzy numbers and an enhanced fuzzy weighted average approach will be used. The proposed approach is used to investigate an example to illustrate its applications in emergency bridgeworks assessment. The approach is shown to be useful and effective. In order to make computing and ranking results easier and to increase recruiting productivity, a computer-based decision support system has been developed, which may help the commander make decisions more efficiently.Defence Science Journal, 2010, 60(4), pp.451-461, DOI:http://dx.doi.org/10.14429/dsj.60.48

Deconvolution Problems for Structured Sparse Signal

This dissertation studies deconvolution problems of how structured sparse signals appear in nature, science and engineering. We discuss about the intrinsic solution to the problem of short-and-sparse deconvolution, how these solutions structured the optimization problem, and how do we design an efficient and practical algorithm base on aforementioned analytical findings. To fully utilized the information of structured sparse signals efficiently, we also propose a sensing method while the sampling acquisition is expansive, and study its sample limit and algorithms for signal recovery with limited samples

Survival Prediction of Initial Blood pH for Nontraumatic Out-of-hospital Cardiac Arrest Patients in the Emergency Department

SummaryBackgroundMost nontraumatic out-of-hospital cardiac arrest (NTOHCA) patients who fail in prehospital resuscitation receive continued cardiopulmonary resuscitation in the emergency department (ED). Initial blood pH, which can be assessed rapidly in the ED, was examined to see whether it is a strong survival predictor for these patients.MethodsA 1-year retrospective study included consecutive 225 NTOHCA patients at a medical center in northern Taiwan who presented through the emergency medical services system. On arrival at the ED, these patients received continued cardiopulmonary resuscitation, and their initial blood pH data were assessed.ResultsThe pH value was positively correlated with variables such as return of spontaneous circulation, witnessed arrest, short prehospital time (≤20 minutes), and survival. The best cut-off value of initial blood pH, revealed by the receiver operating characteristic curve, was 7.068. The lowest pH value of the survivors was 6.856. The results of logistic regression model analysis shows that the odds ratios of survival was 10.0 (95% confidence interval [CI], 2.1–47.7) for patients with initial blood pH ≥ 7.068, 5.3 (95% CI, 1.48–18.9) for those with nonasystole rhythm, 4.0 (95% CI, 1.1–14.8) for those with prehospital time ≤20 minutes, and 9.1 (95% CI, 2.3–35.2) for those without NaHCO3 administration during resuscitation, respectively.ConclusionA cut-off value of an initial blood pH of 7.068 can serve as a predictor for survival among NTOHCA patients. In addition, patients whose initial blood pH is lower than 6.85 in the ED may not survive until hospital discharge