10,741 research outputs found
Exploring compression techniques for ROOT IO
ROOT provides an flexible format used throughout the HEP community. The
number of use cases - from an archival data format to end-stage analysis - has
required a number of tradeoffs to be exposed to the user. For example, a high
"compression level" in the traditional DEFLATE algorithm will result in a
smaller file (saving disk space) at the cost of slower decompression (costing
CPU time when read). At the scale of the LHC experiment, poor design choices
can result in terabytes of wasted space or wasted CPU time. We explore and
attempt to quantify some of these tradeoffs. Specifically, we explore: the use
of alternate compressing algorithms to optimize for read performance; an
alternate method of compressing individual events to allow efficient random
access; and a new approach to whole-file compression. Quantitative results are
given, as well as guidance on how to make compression decisions for different
use cases.Comment: Proceedings for 22nd International Conference on Computing in High
Energy and Nuclear Physics (CHEP 2016
Human Motion Capture Data Tailored Transform Coding
Human motion capture (mocap) is a widely used technique for digitalizing
human movements. With growing usage, compressing mocap data has received
increasing attention, since compact data size enables efficient storage and
transmission. Our analysis shows that mocap data have some unique
characteristics that distinguish themselves from images and videos. Therefore,
directly borrowing image or video compression techniques, such as discrete
cosine transform, does not work well. In this paper, we propose a novel
mocap-tailored transform coding algorithm that takes advantage of these
features. Our algorithm segments the input mocap sequences into clips, which
are represented in 2D matrices. Then it computes a set of data-dependent
orthogonal bases to transform the matrices to frequency domain, in which the
transform coefficients have significantly less dependency. Finally, the
compression is obtained by entropy coding of the quantized coefficients and the
bases. Our method has low computational cost and can be easily extended to
compress mocap databases. It also requires neither training nor complicated
parameter setting. Experimental results demonstrate that the proposed scheme
significantly outperforms state-of-the-art algorithms in terms of compression
performance and speed
Path planning for active tensegrity structures
This paper presents a path planning method for actuated tensegrity structures with quasi-static motion. The valid configurations for such structures lay on an equilibrium manifold, which is implicitly defined by a set of kinematic and static constraints. The exploration of this manifold is difficult with standard methods due to the lack of a global parameterization. Thus, this paper proposes the use of techniques with roots in differential geometry to define an atlas, i.e., a set of coordinated local parameterizations of the equilibrium manifold. This atlas is exploited to define a rapidly-exploring random tree, which efficiently finds valid paths between configurations. However, these paths are typically long and jerky and, therefore, this paper also introduces a procedure to reduce their control effort. A variety of test cases are presented to empirically evaluate the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.Peer ReviewedPostprint (author's final draft
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