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Micromechanically exfoliating graphene on Si/SiO 2 substrates is commonplace for graphene researchers, but locating actual graphene flakes on these substrates is a high-effort and tiresome task. The main purpose of this work was to establish a completely automated procedure to identify those graphene flakes with as little human interaction as possible, improving on the limitations of current methods. Furthermore, automatic electrical characterization of the identified flakes was performed. The proposed micro-robotic automation sequence consists of three main steps. To start, a sample surface plane is calculated, based on multiple foci points across the substrate. Secondly, flakes on the substrate are identified in the hue, saturation, and value (HSV) color space, with an implementation to fit the measurement probe, used to avoid undersized samples and adjust the flake orientation. Finally, electrical characterization is performed based on four point probe measurements with the Van der Pauw method. Results of the successfully implemented automation sequence are presented together with flake electrical properties and validation.

We explore how to track people and furniture based on a high-resolution pressure-sensitive floor. Gravity pushes people and objects against the floor, causing them to leave imprints of pressure distributions across the surface. While the sensor is limited to sensing direct contact with the surface, we can sometimes conclude what takes place above the surface, such as users' poses or collisions with virtual objects. We demonstrate how to extend the range of this approach by sensing through passive furniture that propagates pressure to the floor. To explore our approach, we have created an 8 m 2 back-projected floor prototype, termed GravitySpace, a set of passive touch-sensitive furniture, as well as algorithms for identifying users, furniture, and poses. Pressure-based sensing on the floor offers four potential benefits over camerabased solutions: (1) it provides consistent coverage of rooms wall-to-wall, (2) is less susceptible to occlusion between users, (3) allows for the use of simpler recognition algorithms, and (4) intrudes less on users' privacy.

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