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First order logic lies at the core of many methods in mathematics, philosophy, linguistics, and computer science. Although important efforts have been made to extend first order logic to the task of handling uncertainty, existing solutions are sometimes limited by the way they model uncertainty, or simply by the complexity of the problem formulation. These approaches could be strengthened by adding more flexibility in assigning probabilities (e.g., through intervals) and a more rigorous method of assigning probability/uncertainty measures. In this paper we present the basic theory of Uncertain Logic Processing (ULP), a robust framework for modeling and inference when information is available in the form of first order logic formulas subject to uncertainty. Dempster–Shafer (DS) theory provides the substrate for uncertainty modeling in the proposed ULP formulation. ULP can be tuned to preserve consistency with classical logic, allowing it to incorporate typical inference rules and properties, while preserving the strength of DS theory for representing and manipulating uncertainty. [ABSTRACT FROM AUTHOR]

In this article, the authors propose a system which can identify and track animals. Identification and tracking of animals has got plenty of applications like, avoiding dangerous animal intrusion into residential areas, avoiding animal-vehicle collisions, and behavioral study of animals and so on. Previously, biologists studied videos to detect and identify animals, a time consuming and difficult task. This requires a fully automatic or computer-assisted system to identify and track animals by video. Initially, frames are extracted from the given video. Segmentation is done to the extracted frames using a maximum similarity-based region merging algorithm. Then, the mean shift-based algorithm is used to track the animals. Finally, the animals are classified using Gabor features and a KNN classifier. Experimentation has been conducted on a data set containing more than 150 videos with 15 different classes.

In linear prediction of speech, the 1-norm error minimization criterion has been shown to provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm minimization, 1-norm minimization does not guarantee the stability of the corresponding all-pole filter and can generate saturations when this is used to synthesize speech. In this paper, we introduce two new methods to obtain intrinsically stable predictors with the 1-norm minimization. The first method is based on constraining the roots of the predictor to lie within the unit circle by reducing the numerical range of the shift operator associated with the particular prediction problem considered. The second method uses the alternative Cauchy bound to impose a convex constraint on the predictor in the 1-norm error minimization. These methods are compared with two existing methods: the Burg method, based on the 1-norm minimization of the forward and backward prediction error, and the iteratively reweighted 2-norm minimization known to converge to the 1-norm minimization with an appropriate selection of weights. The evaluation gives proof of the effectiveness of the new methods, performing as well as unconstrained 1-norm based linear prediction for modeling and coding of speech.

• A patient underwent a left-sided hepatic lobectomy for primary hepatocellular carcinoma 13 years ago and remained symptom free. He then presented with spontaneous rupture of a large tumor in the right lobe of the liver. Although this tumor proved to be primary hepatocellular carcinoma, there were significant histological differences between the two lesions, suggesting that this was a second primary liver tumor. Bleeding from the tumor was controlled by selectively ligating the branches supplying the area of hemorrhage.(Arch Surg 1986;121:726-728)