In this paper, information theoretic methodology for system modeling is applied to investigate the probability density function of the busy period in M/G/1 vacation models operating under the N-, T and D-policies. The information about the density function is limited to a few mean value constraints (usually the first moments). By using the maximum entropy methodology one obtains the least biase...

Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. This paper presents an atlas-based approach for automatic segmentation of femurs in x-ray images. A robust global alignment...

A connection between the maximum a posteriori (MAP) estimation and the variational formulation based on the minimization of a given variational integral subject to some noise constraints is established in this paper. A MAP estimator which uses a Markov or a maximum entropy random field model for the prior distribution can be viewed as a minimizer of a variational problem. Inspired by the maximu...

train energy saving problem investigates how to control train's velocity such that the quantity of energy consumption is minimized and some system constraints are satis ed. on the assumption that the train's weights on different links are estimated by fuzzy variables when making the train scheduling strategy, we study the fuzzy train energy saving problem. first, we propose a fuzzy energy ...

We investigate three classes of constraints in a thermoelastic body: (i) a deformationtemperature constraint, (ii) a deformation-entropy constraint, and (iii) a deformationenergy constraint. These constraints are obtained as limits of unconstrained thermoelastic materials and we show that constraints (ii) and (iii) are equivalent. By using a limiting procedure, we show that for the constraint (...

Abstract: In this paper, a general theory of signals characterized by probabilistic constraints is developed. As in previous work [10], the theoretical development employs Lagrange multipliers to implement the constraints and the maximum entropy principle to generate the most likely probability distribution function consistent with the constraints. The method of computing the probability distri...

Combinatorial Optimization is an important area of computer science that has many theoretical and practical applications. In this thesis, we present important contributions to several different areas of combinatorial optimization, including nogood learning, symmetry breaking, dominance, relaxations and parallelization. We develop a new nogood learning technique based on constraint projection th...