We trace the beginning of symbolic dynamics—the study of the shift dynamical system—as it arose from the use of coding to study recurrence and transitivity of geodesics. It is our assertion that neither Hadamard’s 1898 paper, nor the Morse-Hedlund papers of 1938 and 1940, which are normally cited as the first instances of symbolic dynamics, truly present the abstract point of view associated wi...

Symbolic Trajectory Evaluation (STE) is a formal verification technique for hardware. The current STE semantics is not faithful to the proving power of existing STE tools, which obscures the STE theory unnecessarily. In this paper, we present a new closure semantics for STE which does match the proving power of STE model-checkers, and makes STE easier to understand.

Knowledge-Based Artificial Neural Networks (KBANNs) offer a means for combining symbolic and connectionist approaches into a hybrid methodology capable of dealing with small datasets and requires shorter training time when compared to conventional artificial neural networks (ANNs). These approaches were developed mainly for binary-valued domain problems and when applied to real-valued data, man...

researches support that color is like a force that affects on nervous system and this effect is not less than music and sometimes it may be more than that. poets throughout history have shown great interest in color, but this interest varies from time to time. as at pre-islamic era, color was used in its original meaning in most situations and this view has slowly gone toward using color in sym...

In protein sequences, often two sequences that share similar substrings have similar functional properties. Learning of the characteristics and properties of an unknown protein is much easier if its likely functional properties can be predicted by finding the substrings already known from other protein sequences. The sequence pattern search algorithm proposed in this paper searches for similar ...

The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely investigated and reported in the literatu...

This paper presents symbolic time series analysis (STSA) of multi-dimensional measurement data for pattern identification in dynamical systems. The proposed methodology is built upon concepts derived from Information Theory and Automata Theory. The objective is not merely to classify the time series patterns but also to identify the variations therein. To achieve this goal, a symbol alphabet is...

This letter introduces the frequency structure matrix as a new representation of color filter arrays (CFAs). The matrix records the frequency components of CFA filtered images and their positions in the spectrum. The matrix can be conveniently obtained by applying the symbolic DFT to the CFA pattern. With this new representation, it is easy to analyze the characteristics of CFAs and to formulat...

In this paper, a novel method of representing symbolic images in a symbolic image database (SID) invariant to image transformations, useful for exact match retrieval is presented. The proposed model is based on Triangular Spatial Relationship (TSR) [Guru, D.S., Nagabhushan, P., 2001. Triangular spatial relationship: A new approach for spatial knowledge representation, Pattern Recognition Lett. ...