This paper presents a single camera vehicle detection technique for forward collision warning systems suitable to be integrated in embedded platforms. It combines the robustness of detectors based on classification methods with an innovative perspective multi-scale procedure to scan the images that dramatically reduces the computational cost associated with robust detectors. In our experiments ...

This paper presents computational experience with a rather straight forward implementation of an edge search algorithm for obtaining the globally optimal solution for linear programs with an additional reverse convex constraint. The paper’s purpose is to provide a collection of problems, with known optimal solutions, and performance information for an edge search implementation so that research...

This paper introduces an effective memetic algorithm for the linear ordering problem with cumulative costs. The proposed algorithm combines an order-based recombination operator with an improved forward-backward local search procedure and employs a solution quality based replacement criterion for pool updating. Extensive experiments on 118 well-known benchmark instances show that the proposed a...

One of the most successful methods for planning in large partially observable stochastic domains is depth-limited forward search from the current belief state together with a utility estimation. However, when the environment is continuous and the number of possible actions is practically infinite, then abstractions have to be made before any forward search planning can be performed. The paper p...

In this paper, we outline a comprehensive approach to composed analogies based on the theory of conceptual spaces. Our algorithmic model understands analogy as search procedure and builds upon idea that analogical similarity depends phenomena called ‘dimensional salience.’ We distinguish between category-based, property-based, event-based, part-whole analogies, propose computationally-oriented ...

The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes th...