Browsing by Subject "Robot learning"

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Open Access
Piecewise smooth signal denoising via principal curve projections
(IEEE, 2008-10) Özertem, Umut; Erdoğmuş, D.; Arıkan, Orhan
One common problem in signal denoising is that if the signal has a blocky, in other words a piecewise-smooth structure, the denoised signal may suffer from oversmoothed discontinuities or exhibit artifacts very similar to Gibbs phenomenon. In the literature, total variation methods and some modifications on the signal reconstructions based on wavelet coefficients are proposed to overcome these problems. We take a novel approach by introducing principal curve projections as an artifact-free signal denoising filter alternative. The proposed approach leads to a nonparametric denoising algorithm that does not lead to Gibbs effect or so-called staircase type unnatural artifacts in the denoised signal. ©2008 IEEE.
Open Access
Voting as validation in robot programming
(Sage Publications Ltd., 1999-04) Utete, S. W.; Barshan, B.; Ayrulu, B.
This paper investigates the use of voting as a conflict-resolution technique for data analysis in robot programming. Voting represents an information-abstraction technique. It is argued that in some cases a voting approach is inherent in the nature of the data being analyzed: where multiple, independent sources of information must be reconciled to give a group decision that reflects a single outcome rather than a consensus average. This study considers an example of target classification using sonar sensors. Physical models of reflections from target primitives that are typical of the indoor environment of a mobile robot are used. Dispersed sensors take decisions on target type, which must then be fused to give the single group classification of the presence or absence and type of a target. Dempster-Shafer evidential reasoning is used to assign a level of belief to each sensor decision. The decisions are then fused by two means. Using Dempster's rule of combination, conflicts are resolved through a group measure expressing dissonance in the sensor views. This evidential approach is contrasted with the resolution of sensor conflict through voting. It is demonstrated that abstraction of the level of belief through voting proves useful in resolving the straightforward conflicts that arise in the classification problem. Conflicts arise where the discriminant data value, an echo amplitude, is most sensitive to noise. Fusion helps to overcome this vulnerability: in Dempster-Shafer reasoning, through the modeling of nonparametric uncertainty and combination of belief values; and in voting, by emphasizing the majority view. The paper gives theoretical and experimental evidence for the use of voting for data abstraction and conflict resolution in areas such as classification, where a strong argument can be made for techniques that emphasize a single outcome rather than an estimated value. Methods for making the vote more strategic are also investigated. The paper addresses the reduction of dimension of sets of decision points or decision makers. Through a consideration of combination/order, queuing criteria for more strategic fusion are identified.