Temporal feature analysis method describing for automata models of computation systems

The paper reported the temporal feature analysis method describing for automata models of computation systems. The method is based on from source algorithm to modified algorithm expanding makes it possible to take characteristics of work process related to execution time measurement. The paper considers the transition method from source algorithm representation to modification a final state machine with additional states providing registration of temporal features. The paper demonstrates method usage example on computer system authorization algorithm, approves an algorithm complexity for features registration O(n) for n-states automata algorithm. The method can be used for a large class algorithm, but is recommended to apply it to an algorithm, separated on procedure thus mean of commands amount should be more than 500 for more time measurement accuracy.

Generating networks of genetic processors

The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.

On Boolean Automata Networks (de)Composition1

Boolean automata networks (BANs) are a generalisation of Boolean cellular automata. In such, any theorem describing the way BANs compute information is a strong tool that can be applied to a wide range of models of computation. In this paper we explore a way of working with BANs which involves adding external inputs to the base model (via modules), and more importantly, a way to link networks together using the above mentioned inputs (via wirings). Our aim is to develop a powerful formalism for BAN (de)composition. We formulate three results: the first one shows that our modules/wirings definition is complete; the second one uses modules/wirings to prove simulation results amongst BANs; the final one expresses the complexity of the relation between modularity and the dynamics of modules.

SIGACT News Complexity Theory Column 109

Warmest thanks to Alexander Knop, Shachar Lovett, Sam McGuire, and Weiqiang Yuan for this issue's guest column, \Models of computation between decision trees and communication." (Their article came in early, and I wrote back thanking them for being three days early, mentioning that that happens surprisingly rarely. Then two days later, I said, \Wow!", as I realized that they actually had somehow|even during a remote-teaching, social-distancing time period|prepared and sent in their article a month and three days early.)

Specifications and Modeling

How can we describe the system which we would like to design, and how can we represent intermediate design information? Models and description techniques for initial specifications as well as for intermediate design information will be shown in this chapter. First of all, we will capture requirements for modeling techniques. Next, we will provide an overview of models of computation. This will be followed by a presentation of popular models of computations, in combination with examples of the corresponding languages. The presentation includes models for early design phases, automata-based models, data flow, Petri nets, discrete event models, von Neumann languages, and abstraction levels for hardware modeling. Finally, we will compare different models of computation and present exercises.

Analysis and Identification of Possible Automation Approaches for Embedded Systems Design Flows

Sophisticated and high performance embedded systems are present in an increasing number of application domains. In this context, formal-based design methods have been studied to make the development process robust and scalable. Models of computation (MoC) allows the modeling of an application at a high abstraction level by using a formal base. This enables analysis before the application moves to the implementation phase. Different tools and frameworks supporting MoCs have been developed. Some of them can simulate the models and also verify their functionality and feasibility before the next design steps. In view of this, we present a novel method for analysis and identification of possible automation approaches applicable to embedded systems design flow supported by formal models of computation. A comprehensive case study shows the potential and applicability of our method.