Document Type



1. NATURAL SCIENCES, Applied mathematics, Computer Sciences, Optics

Publication Details

Journal of Applied Computer Science and Mathematics, Issue 1, (Vol. 17) / 2023

Journal page:


This paper presents a novel computational process used for determining matching coefficients for optical spectra either in real time or from a database. This simple algorithmic method is capable of outputting a set of coefficients relating to comparative matching datapoints and to a weighted similarity ratio between spectra whilst still maintaining high computational efficiency due to the minimization of floating-point calculations. The process works as effectively on signal data during real time processing or on images of spectra which may be stored in a database. A subset of the algorithm has already been used very effectively in an embedded mobile field device to determine the presence or absence of special security fluorescent emission peaks present in printing inks used in anti-counterfeiting labels. The process involves the division of test spectrum(s) signal/image into a grid of user definable blocks while applying the same grid to the test image or images. Each block is then checked to see if the signal/image passes through the block, if the signal/image passes through a block then the value of this grid block is set to 1 in a grid array. The same check is then applied to the reference signal/image(s).While it is possible to process a signal comparison without the need for data storage, useful for some embedded processes, it may be preferable to store the resultant data in simple data arrays. These arrays can then undergo simple arithmetical processing to quickly calculate a set of values representing the number of matching data points, the variation in non-matching data points and a divergence value. This check can be carried out between the test image or images and a larger number of reference images simultaneously by use of additional counters. In addition, when used with large databases of spectra this novel technique may be used to rapidly reduce a large test set to a more relevant subset which can be further analyzed using more conventional methods or by visual inspection. This grid square analysis method works as efficiently on images as it can for signal data and it is believed by the authors that the method will have applications in other image processing applications where fast and efficient comparative analysis between images is required.


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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