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New developments in fast image processing and data acquisition for STM
Author(s) -
Béjar M. A.,
GómezRodríguez J. M.,
GómezHerrero J.,
Baró A.
Publication year - 1988
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.1988.tb01429.x
Subject(s) - computer science , microcomputer , software , pixel , data acquisition , deconvolution , image processing , graphics , smoothing , computer graphics (images) , sample (material) , ibm pc compatible , computer vision , artificial intelligence , image (mathematics) , algorithm , telecommunications , chip , chemistry , chromatography , programming language , operating system
SUMMARY We have developed new easy‐to‐use but powerful software in order to obtain the best scan rates in data acquisition and image processing for a PC‐AT microcomputer. The programs have been completely written in the C‐language, which made it possible to combine low‐level characteristics (quick access to memory, interrupt control, etc.) without losing the advantages of a high‐level language. The data acquisition software was intended to be sufficiently flexible to acquire either topographical or spectroscopic data with no configuration changes. In topographical ( z=f ( x,y )) and spectroscopic ( I=f ( V, z )) measurements, we can obtain up to 15,000 samples/s rates, with 12‐bit resolution in a ±10V range. Spectroscopic measurements are done by acquiring I ( V ) with the feedback off at several z values (up to 256) until I reaches a preset value; afterwards, z is reset automatically to the initial voltage to avoid tip‐sample contact. The image processing was designed to get the best performances in time and quality with a simple system such as a PC‐AT equipped with an IBM Professional Graphics Display (640times480 pixels and 256 simultaneous colours). It was structured in three parts: plane subtraction, image display (3‐D shaded surfaces, 3‐D surfaces with colour scales or 2‐D contour maps), and filtering (smoothing filters or FFT convolution‐deconvolution techniques).