9,629 research outputs found
Multiresolution analysis as an approach for tool path planning in NC machining
Wavelets permit multiresolution analysis of curves and surfaces. A complex curve can be decomposed using wavelet theory into lower resolution curves. The low-resolution (coarse) curves are similar to rough-cuts and high-resolution (fine) curves to finish-cuts in numerical controlled (NC) machining.;In this project, we investigate the applicability of multiresolution analysis using B-spline wavelets to NC machining of contoured 2D objects. High-resolution curves are used close to the object boundary similar to conventional offsetting, while lower resolution curves, straight lines and circular arcs are used farther away from the object boundary.;Experimental results indicate that wavelet-based multiresolution tool path planning improves machining efficiency. Tool path length is reduced, sharp corners are smoothed out thereby reducing uncut areas and larger tools can be selected for rough-cuts
Speeding up Simplification of Polygonal Curves using Nested Approximations
We develop a multiresolution approach to the problem of polygonal curve
approximation. We show theoretically and experimentally that, if the
simplification algorithm A used between any two successive levels of resolution
satisfies some conditions, the multiresolution algorithm MR will have a
complexity lower than the complexity of A. In particular, we show that if A has
a O(N2/K) complexity (the complexity of a reduced search dynamic solution
approach), where N and K are respectively the initial and the final number of
segments, the complexity of MR is in O(N).We experimentally compare the
outcomes of MR with those of the optimal "full search" dynamic programming
solution and of classical merge and split approaches. The experimental
evaluations confirm the theoretical derivations and show that the proposed
approach evaluated on 2D coastal maps either shows a lower complexity or
provides polygonal approximations closer to the initial curves.Comment: 12 pages + figure
Multiresolution Diffusion Entropy Analysis of time series: an application to births to teenagers in Texas
The multiresolution diffusion entropy analysis is used to evaluate the
stochastic information left in a time series after systematic removal of
certain non-stationarities. This method allows us to establish whether the
identified patterns are sufficient to capture all relevant information
contained in a time series. If they do not, the method suggests the need for
further interpretation to explain the residual memory in the signal. We apply
the multiresolution diffusion entropy analysis to the daily count of births to
teens in Texas from 1964 through 2000 because it is a typical example of a
non-stationary time series, having an anomalous trend, an annual variation, as
well as short time fluctuations. The analysis is repeated for the three main
racial/ethnic groups in Texas (White, Hispanic and African American), as well
as, to married and unmarried teens during the years from 1994 to 2000 and we
study the differences that emerge among the groups.Comment: 14 pages, 3 figures, 1 table. In press on 'Chaos, Solitons &
Fractals
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