This paper presents the main simulation and experimental results from studies of the robustness of a proposed new control strategy for the under-actuated robot RABBIT. The disturbances studied include modifications of the characteristics of the foot/ground interaction and the application of external forces to the trunk of the robot. These two kinds of disturbance correspond to those most frequently encountered by biped robots during walking in an unknown environment.

In order to alleviate the negative effect of curse of dimensionality, band selection is a crucial step for hyperspectral image (HSI) processing. In this letter, we propose a novel unsupervised band selection approach to reduce the dimensionality for hyperspectral imagery. In order to obtain the most representative bands, the correlation matrix computed from the original HSI is used to describe the correlation characteristics among bands, while the block-diagonal structure is measured to segment all bands into a series of subspace. After applying the spectral clustering algorithm, the optimal combination of band is finally selected. To verify the effectiveness and superiority of the proposed band selection method, experiments have been conducted on three widely used real-world hyperspectral data. The results have shown that the proposed method outperforms other methods in HSI classification application.

In this work we present an intelligent approach to detection and extraction of salient objects. The described system is inspired by early processing stages of human visual system and is based on our previous work on the field of visual saliency. Building on our preceding system, which worked with a fixed visual attention scale, we develop a machine learning approach using an artificial neural network and genetic algorithm, estimating automatically the visual attention scale for each input image individually. The whole approach has low complexity and can be run in speed close to real-time on contemporary processors. Quantitative evaluation results of the described approach with visual attention scale estimation are compared to results obtained with a fixed scale and to results of two other existing salient object detection techniques. The system is a part of our work on an intelligent machine vision system, using visual saliency for unsupervised learning of objects.