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This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 11th International Conference on Computing, Communication and Sensor Network (CCSN) held in September 2022 at Utkal University in Bhubaneswar, Odisha, India. The conference covered various interdisciplinary areas such as computing, communication, sensor networks, and circuit designs. The papers included in this special issue have undergone a rigorous review process and were selected for publication. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

This paper addresses a problem of global optimization in a non-Archimedean fuzzy metric space context without fuzzy P-property. Specifically, it concerns the determination of the fuzzy distance between two subsets of a non-Archimedean fuzzy metric space. Our approach to solving this problem is to find an optimal approximate solution to a fixed point equation. This approach has been well studied within a category of problems called proximity point problems. We explore some new types of (ψ − ϕ) -weak proximal contractions and investigate the existence of the unique best proximity point for such kinds of mappings. Subsequently, some fixed point results for corresponding contractions are proved, and some illustrative examples are presented to support the validity of the main results. Moreover, an interesting application in computer science, particularly in the domain of words has been provided. Our work is a fuzzy generalization of the proximity point problem by means of fuzzy fixed point method. [ABSTRACT FROM AUTHOR]

The importance of measuring the complexity of shapes can be seen by the wide range of its application such as computer vision, robotics, cognitive studies, eye tracking, and psychology. However, it is very challenging to define an accurate and precise metric to measure the complexity of the shapes. In this paper, we explore different notions of shape complexity, drawing from established work in mathematics, computer science, and computer vision. We integrate results from user studies with quantitative analyses to identify three measures that capture important axes of shape complexity, out of a list of almost 300 measures previously considered in the literature. We then explore the connection between specific measures and the types of complexity that each one can elucidate. Finally, we contribute a dataset of both abstract and meaningful shapes with designated complexity levels both to support our findings and to share with other researchers. [ABSTRACT FROM AUTHOR]

Threshold graphs have numerous applications in computer science and psychology. The eccentricity matrix ɛ (G) of G is defined as ɛ (G) i j = d (v i , v j) if d (v i , v j) = m i n { e (v i) , e (v j) } , 0 otherwise. In this paper, we give some properties of the eccentricity eigenvalues of threshold graphs and determine threshold graphs with distinct eccentricity eigenvalues in [ − 2 , 0 ]. [ABSTRACT FROM AUTHOR]

A convex polytope is the convex hull of a finite set of points in the Euclidean space ℝ n . By preserving the adjacency-incidence relation between vertices of a polytope, its structural graph is constructed. A graph is called Hamilton-connected if there exists at least one Hamiltonian path between any of its two vertices. The detour index is defined to be the sum of the lengths of longest distances, i.e., detours between vertices in a graph. Hamiltonian and Hamilton-connected graphs have diverse applications in computer science and electrical engineering, whereas the detour index has important applications in chemistry. Checking whether a graph is Hamilton-connected and computing the detour index of an arbitrary graph are both NP-complete problems. In this paper, we study these problems simultaneously for certain families of convex polytopes. We construct two infinite families of Hamilton-connected convex polytopes. Hamilton-connectivity is shown by constructing Hamiltonian paths between any pair of vertices. We then use the Hamilton-connectivity to compute the detour index of these families. A family of non-Hamilton-connected convex polytopes has also been constructed to show that not all convex polytope families are Hamilton-connected. [ABSTRACT FROM AUTHOR]

Graph tries are a new and interesting data structure proposed by Jacquet in 2014. They generalize the classical trie data structure which has found many applications in computer science and is one of the most popular data structure on words. For his generalization, Jacquet considered the size (or space requirement) and derived an asymptotic expansion for the mean and the variance when graph tries are built from n independently chosen random labelings of a rooted M -ary tree. Moreover, he conjectured a central limit theorem for the (suitably normalized) size as the number of labelings tends to infinity. In this paper, we verify this conjecture with the method of moments. [ABSTRACT FROM AUTHOR]

Solomon Marcus (1925–2016) was one of the founders of the Romanian theoretical computer science. His pioneering contributions to automata and formal language theories, mathematical linguistics and natural computing have been widely recognised internationally. In this paper we briefly present his publications in theoretical computer science and related areas, which consist in almost ninety papers. Finally we present a selection of ten Marcus books in these areas. [ABSTRACT FROM AUTHOR]

Kruskal's theorem on trees is a classical result of combinatorics with important applications in computer science. The minimal bad sequence argument of Nash-Williams yields a proof that is very elegant but not as elementary as possible, by previous results from mathematical logic. In the present paper, we formulate a uniform Kruskal theorem, which extends the original theorem from trees to general recursive data types. Our main result shows that the minimal bad sequence argument does yield the most elementary proof of this uniform Kruskal theorem. So in the uniform case, the elegant proof coincides with the most elementary one. In more precise and technical terms, we work in the framework of reverse mathematics, where we show that the uniform Kruskal theorem is equivalent to Π 1 1 -comprehension, over RCA 0 extended by the chain-antichain principle. As a by-product of our investigation, we obtain uniform proofs of several Kruskal-type independence results. [ABSTRACT FROM AUTHOR]