It is known that anodically-oxidized niobium (Nb) films make various colors. The driving force of coloration is considered to be thin film interference due to the Nb2O5 layer on the Nb film. In the case of the coloration due to thin film interference, there is a possibility that we can give a rewritable nature to Nb films by controlling the thickness of Nb2O5 layer. From the industrial point of view, it is preferable to give a rewritable nature to the color changeable Nb film since it may enable the Nb film to be used as new type of rewritable media such as color electronic paper. Therefore, the focus of this present work is to explore the possibility of these color Nb films as rewritable media. Our experimental data using a goniophotometer and a spectrophotometer suggest that our Nb films can be used as rewritable media by controlling preparation conditions.
The features of OCTA (Open Computational Tool for Advanced material technology) are explained. OCTA has been developed as a simulation engine platform and several types of simulation engines especially for polymeric materials have been implemented on OCTA. The self-consistent field (SCF) theory of polymers is also explained. This theory is implemented in one of OCTA' s simulation engine named SUSHI (simulation utilities for soft and hard interfaces). Deformations of a core shell type droplet on a wall are demonstrated with SUSHI. The wetting phenomena of the droplet on the wall are confirmed.
Robotic Scrub Nurse (RSN) is a developing research and a challenging issue in the field of medical robotics. Audio processing, wearing-sensor and computer vision are applied to control the robot that could support surgeons during surgeries as an alternative of human nurse. On the other hand, surgeons wish that RSN should be able to decide the correct action by recognizing surgical situations autonomously just like skilled human nurses. In general, surgical situations can be discriminated by surgeons' hand actions during surgeries. This article introduces and explains the authors' computer vision based method for recognizing surgical hand actions of surgeons from video sequences.