This article explains the idea of pattern systems that develop gradually. These systems involve symbolic communication that includes symbols, syntax, and layout rules. Some pattern systems change over time, like historical scripts. The scientific study of pattern systems is called pattern evolution research, and scriptinformatics is concerned with the modelling of the evolution of scripts. The symbol series consists of symbols from a pattern system, while the graph sequence is a symbol sequence applied with a specific technology. This article describes a method for examining tested pattern systems to confirm their classification, which focuses on more ancient features. The method’s effectiveness was tested on Rovash scripts and graph sequences. Multivariate analysis was carried out by using PAST4 software, employing principal coordinates analysis ordination and k-means clustering algorithms.

This paper presents a machine learning approach to explore the phenetic relations of historical scripts and their glyphs. Its first step is the identification of the observable topological transformations in the development of the glyphs, and with the use of these transformations, the method collects the possible cognate glyphs by minimizing the necessary topological transformations between the glyphs. In these investigations, the phonetic properties of the graphemes were consistently considered. The second step of our method is selecting similarity groups of possible cognate glyphs by minimizing the differences of their topological properties. The third step is multidimensional scaling and different cluster analyses based on the similarity groups of the glyphs of the historical scripts in order to explore the phenetic relationships between these scripts. The resulting phenetic structure of the scripts could be used for paleographical research, especially in deciphering ancient hard-to-read inscriptions.

A method for optimization of MOS model parameters is introduced. The method is based upon dynamically fitting the measured frequency response of a reconfigurable ring oscillator. The ring oscillator contains two added switching transistors and two loading capacitors. By controlling the switching transistors, the capacitive load of the inverters and thus the oscillation frequency can be varied. The simulated and the measured frequencies are compared and the derived average error is minimized. Sequential iterations using combined mathematical methods are applied to the procedure. Simplifications are introduced in order to shorten the computational time. The effectiveness of the method on improving the accuracy of simulation is demonstrated with numerical examples.>