No abstract

No abstract

Unmanned aerial vehicles (UAVs) have many applications and quickly gain popularity with the availability of low-cost micro aerial vehicles (MAVs). Robotics is a popular interdisciplinary education target as it involves understanding and collaboration of several disciplines. Thus, UAVs can serve as an ideal study platform. However, as robotics requires technical background, skills and initial efforts, it is commonly applied in long-term courses. In this paper we successfully exploit the opposite case of robotics in short-term education for students without background, in form of a one-day contest on automatic visual UAV navigation. We provide an extensive survey, and show that existing material and tools do not fit the task and lack in technical aspects. We introduce a novel open-source programming library that comprises programs to guide learning by experience and allow rapid development. It makes contributions to marker-based tracking, with a novel nested-marker design and accurate calibration parameters estimated from 14 Parrot AR.Drone 2.0 front cameras. We show a detailed discussion of the contest results, which represents an extensive user study regarding robotics in education and the effectiveness of the library. The achievement of a steep learning curve for a complex subject has important implications in interdisciplinary design education, as it allows deep understanding of potentials and limitations to facilitate decision-making, unconventional problem solutions and novel applications.

We propose a method to evaluate the significance of historical entities (people, events, and so on.). Here, the significance of a historical entity means how it affected other historical entities. Our proposed method first calculates the tempo-spacial impact of historical entities. The impact of a historical entity varies according to time and location. Historical entities are collected from Wikipedia. We assume that a Wikipedia link between historical entities represents an impact propagation. That is, when an entity has a link to another entity, we regard the former is influenced by the latter. Historical entities in Wikipedia usually have the date and location of their occurrence. Our proposed iteration algorithm propagates such initial tempo-spacial information through links in the similar manner as PageRank, so the tempo-spacial impact scores of all the historical entities can be calculated. We assume that a historical entity is significant if it influences many other entities that are far from it temporally or geographically. We demonstrate a prototype system and show the results of experiments that prove the effectiveness of our method.