Performance limits of graphene-ribbon field-effect transistors

The performance of field effect transistors based on an single graphene ribbon with a constriction and a single back gate are studied with the help of atomistic models. It is shown how this scheme, unlike that of traditional carbon-nanotube-based transistors, reduces the importance of the specifics of the chemical bonding to the metallic electrodes in favor of the carbon-based part of device. The ultimate performance limits are here studied for various constriction and metal-ribbon contact models. In particular, we show that, even for poorly contacting metals, properly tailored constrictions can give promising values for both the on conductance and the subthreshold swing.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.