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All of the block matching algorithms were implemented using Visual C++ on a PC with a 2.66 GHz Intel® Core™ 2 Duo CPU.
The matching algorithms were developed by David W. Jacobs, an associate professor of computer science at the University of Maryland, College Park.
For quantification of strain and twisting deformation, 3-D distortion-free vessel straightening and landmark matching algorithms were developed to compute the relative translation and rotation of distal landmarks with respect to a proximal landmark.
Finally sensor fault matching algorithms were proposed to minimize fault unobservability and cost for the whole system, under the constraints of detectability and limited resources, thus achieving optimum sensor placement.
The entity detection and the medication matching algorithms were always trained on the data that was never part of the test set in each run of the experiments.
Matching algorithms were based on identifying variables including child's first name, last name, date of birth, sex, and mother's first name, last name, and date of birth.
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One useful application of pattern matching algorithms is in the identification of major histocompatibility complex (MHC) ligands and T-cell epitopes.
A projection matching algorithm was applied for the determination of the center and orientation of raw particles in iterative refinement.
Therefore, a Bio-Hashed minutiae template based on a chip matching algorithm is proposed.
To acquire this information, the correlation matching algorithm is applied in this paper.
The brute-force fingerprint matching algorithm was mentioned in various texts as a simple algorithm that had moderate success in matching fingerprints.
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