A factor oracle is a data structure for weak factor recognition. It is an automaton built on a string p of length m that is acyclic, recognizes at least all factors of p, has m+1 states which are all final, and has m to 2m−1 transitions. In this paper, we give two alternative algorithms for its construction and prove the constructed automata to be equivalent to the automata constructed by the a...

Biró, Hujter, and Tuza introduced the concept of H-graphs (1992), intersection graphs of connected subgraphs of a subdivision of a graph H. They naturally generalize many important classes of graphs, e.g., interval graphs and circular-arc graphs. Our paper is the first study of the recognition and dominating set problems of this large collection of intersection classes of graphs. We negatively ...

The question of tractable classes of constraint satisfaction problems (CSPs) has been studied for a long time, and is now a very active research domain. However, studies of tractable classes are typically very theoretical. They usually introduce classes of instances together with polynomial time algorithms for recognizing and solving them, and the algorithms can be used only for the new class. ...

We attack the problem of recognizing real, planar objects from two-dimensional, intensity images taken from arbitrary viewpoints using genetic algorithms. More speciically, we use genetic algorithms to search for a geometric mapping that brings subsets of points comprising the model and subsets of points comprising the scene into alignment. The genetic algorithm searches the image space and we ...

Recognizing user activities using body-worn, miniaturized sensor nodes enables wearable computers to act contextaware. This paper describes how online activity recognition algorithms can be run on the SensorButton, our miniaturized wireless sensor platform. We present how the activity recognition algorithms have been optimized to be run online on our sensor platform, and how the execution can b...

In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumin...

Human-Computer-Interface HCI) has become an emerging area of research among the scientific community. The uses of machine learning algorithms are dominating the subject of data mining, to achieve the optimized result in various areas. One such area is related with emotional state classification using bio-electrical signals. The aim of the paper is to investigate the efficacy, efficiency and com...

The paper considers the syntactic model of recognizing structures UML-diagrams, defines main methods and algorithms for working with images basic figures texts, proposes its own algorithm analysis recognition based on method flexible comparison graphs. proposed can be used any platform. As a result, technology primary design information, namely – UML diagrams, was developed. allows to reduce ti...

This paper reviews related work and state-of-the-art publications for recognizing motor symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to inform on how well traditional machine learning algorithms can handle this task. In particular, four PD related motor symptoms are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia) and their deta...