B. de Boer

The interaction between the mitochondrial ATPase (F1) and the ATPase inhibitor

1. The naturally occurring mitochondrial ATPase inhibitor inhibits the mitochondrial ATPase (F1) non-competitively. 2. The interaction between inhibitor and inhibitor-depleted F1 or submitochondrial particles is diminished when the ratio of ATP/ADP is low or when energy is generated by substrate oxidation. 3. The dissociation of the inhibitor from coupled Mg-ATP particles is promoted when substrates are being oxidized. This results in the appearance of a large uncoupler-stimulated ATPase activity. Activation of the uncoupler-stimulated ATPase activity is also achieved by incubation of the particles with ADP. 4. The ATPase activity of Mg-ATP particles is determined by the turnover capacity of F1. When endogenous inhibitor is removed, energy dissipation becomes the rate-limiting step. This energy dissipation can be activated by an uncoupler. 5. Evidence is presented for the existence of a non-inhibited intermediate F1-inhibitor complex.

Active exploration and keypoint clustering for object recognition

Object recognition is a challenging problem for artificial systems. This is especially true for objects that are placed in cluttered and uncontrolled environments. To challenge this problem, we discuss an active approach to object recognition. Instead of passively observing objects, we use a robot to actively explore the objects. This enables the system to learn objects from different viewpoints and to actively select viewpoints for optimal recognition. Active vision furthermore simplifies the segmentation of the object from its background. As the basis for object recognition we use the Scale Invariant Feature Transform (SIFT). SIFT has been a successful method for image representation. However, a known drawback of SIFT is that the computational complexity of the algorithm increases with the number of keypoints. We discuss a growing-when-required (GWR) network for efficient clustering of the key- points. The results show successful learning of 3D objects in real-world environments. The active approach is successful in separating the object from its cluttered background, and the active selection of viewpoint further increases the performance. Moreover, the GWR-network strongly reduces the number of keypoints.