Field desorption mass spectra often contain few peaks, the largest of which are [MI: and [M+H]' ions. Because of this lack of fragmentation accurate mass measurement is of primary concern in field desorption mass spectrometry. Although total ion yield per quantity of sample may rival electron impact
Computing Stochastic Completion Fields in Linear-Time Using a Resolution Pyramid
β Scribed by Lance Williams; John Zweck; Tairan Wang; Karvel Thornber
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 153 KB
- Volume
- 76
- Category
- Article
- ISSN
- 1077-3142
No coin nor oath required. For personal study only.
β¦ Synopsis
We describe a linear-time algorithm for computing the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane. Our algorithm is a resolution-pyramid-based method for solving a partial differential equation (PDE) characterizing a distribution of short, smooth completion shapes. The PDE consists of a set of independent advection equations in (x, y) coupled in the ΞΈ dimension by the diffusion equation. A previously described algorithm used a first-order, explicit finite difference scheme implemented on a rectangular grid. This algorithm required O(n 3 m) time for a grid of size n Γ n with m discrete orientations. Unfortunately, systematic error in solving the advection equations produced unwanted anisotropic smoothing in the (x, y) dimension. This resulted in visible artifacts in the completion fields. The amount of error and its dependence on ΞΈ have been previously characterized. We observe that by careful addition of extra spatial smoothing, the error can be made totally isotropic. The combined effect of this error and of intrinsic smoothness due to diffusion in the ΞΈ dimension is that the solution becomes smoother with increasing time, i.e., the high spatial frequencies drop out. By increasing βx and βt on a regular schedule, and using a secondorder, implicit scheme for the diffusion term, it is possible to solve the modified PDE in O(n 2 m) time, i.e., time linear in the problem size. Using current hardware and for problems of typical size, this means that a solution which previously took 1 h to compute can now be computed in about 2 min.
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