Showing papers on "Centroid published in 1970"

A small on-line computer system for high-resolution mass spectrometers

Abstract: A system is described in which a medium-sized double-focusing mass spectrometer is coupled on-line to a small (4K memory words) computer. The system can produce complete elemental composition data within two minutes after the completion of the mass spectral scan. The inherent speed of the processor and memory sub-systems is effectively retained through the utilization of a low-cost random-access bulk-storage device and other high-speed peripherals. An attempt is made to define both the theoretical and practical considerations necessary in utilizing a small digital computer as an integral part of a chemical experiment, specifically as applied to high-resolution mass spectrometry. Important aspects include the interaction of resolution and errors of mass measurement, the contribution of noise to observed errors, the effect of peak shape on such errors, the techniques of analog and digital signal processing, the criteria of efficient system design and the fundamental validity of applying a mathematical model, such as the centroid, to a basically statistical situation. The centroid method of calculating peak centers is shown to be fundamentally correct and the resultant error due to statistics diminishes in absolute value as the peak width decreases, that is, as the resolution increases, errors being approximately one-half as large at R = 10,000 as those at R = 2200. The overall mass measuring accuracy has been investigated at resolutions up to R = 10,000 using several different organic compounds and has been shown to be about 12 ppm for a single scan. Multiple scan averaging reduces this error by approximately the square root of the numbers of scans.

Mass measurement system for mass spectrometers

Abstract: In a scanning mass spectrometer, electrical output signals including a series of time-related peaks representing an ion mass spectrum of an unknown sample material are produced along with a series of reference peaks derived from a reference material. The time at which a spectrum peak occurs is taken to be the time of occurrence of the centroid (or center of gravity) of the peak. The time of occurrence of the peak centroid in relation to the time of occurrence of the end of the peak is determined and that information is presented in digital form. The occurrence time of the peak centroid is then subtracted from the occurrence time of the peak to provide the peak centroid occurrence on the scan. Thus, the time positions of the peak centroids due to an unknown sample may be readily identified with respect to the time positions of the centroids of peaks due to the reference material.

Modeling 3D Quantization Error For MotionEstimation

Abstract: Spatial anisotropic uncertainty of feature points must be taken into account to improve the precision in visual navigation. This paper includes two parts, which discuss error modeling and motion estimation respectively. In the first part we model the 3D reconstruction uncertainty in binocular stereo system as normal distribution and compute its propagation in stereo pair. Assume the uncertainty of image feature pixels geing normal distributed on the image plane, the reconstructed 3D error is analytically derived based on some error evaluation schemes. The closed-form solution of the 3D uncertainty is obtained for parallel camera setup. The second part of this paper proposes a novel iterative motion estimation algorithm that involves the anisotropic 3D uncertainty. We present a modified centroid coincidence theorem to divide the problem into two steps, rotation estimation and translation estimation. The translation estimation is straight-forward, and the latter can be obtained by a new iterative method as well. The LMS motion estimation criterion is linearized at 0th order and a motion estimation equation is proposed. The initial guess of the motion parameters is given by a SVD method. The iterative algorithm yields the optimal LMS motion estimation. Experimental data show that the iterative algorithm always converges under large and small point sets. It is much more robust and fast than previous quasi-Newton optimization based approaches. Transactions on Information and Communications Technologies vol 16, © 1996 WIT Press, www.witpress.com, ISSN 1743-3517