Jumping jack

Abstract: Robots have one motor at each joint, but animals have bi-articular muscle driving two joints simultaneously. The bi-articular muscle characterizes animals and is predicted to have special functions. To clarify these functions, a simple human model was emploved. The model was a Jumping Jack without the guide rail. Gastrocnemius, the bi-articular muscle in the lower leg, was modeled simply by wire. The clarified bi-articular muscle functions will contribute to development of simple controlled robots. Model experiments and computer simulations were executed with and without the wire. Ground reaction force and jump movement were analyzed both in the model experiments and computer simulations. In addition, energy transformation was analyzed in computer simulations. The wire simulating the bi-articular muscle (1) controlled the direction of the ground reaction force, (2) inhibited transformation into rotational energy from the potential energy of the spring, and (3) stabilized airborne movement.

Abstract: Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for nonmonotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.