A P-Lingua Programming Environment for Membrane Computing

A new programming language for membrane computing, PLingua, is developed in this paper. This language is not designed for a speci c simulator software. On the contrary, its purpose is to o er a general syntactic framework that could de ne a uni ed standard for membrane computing, covering a broad variety of models. At the present stage, P-Lingua can only handle P systems with active membranes, although the authors intend to extend it to other models in the near future. P-Lingua allows to write programs in a friendly way, as its syntax is very close to standard scienti c notation, and parameterized expressions can be used as shorthand for sets of rules. There is a built-in compiler that parses these human-style programs and generates XML documents that can be given as input to simulation tools, di erent plugins can be designed to produce speci c adequate outputs for existing simulators. Furthermore, we present in this paper an integrated development environment that plays the role of interface where P-lingua programs can be written and compiled. We also present a simulator for the class of recognizer P systems with active membranes, and we illustrate it by following the writing, compiling and simulating processes with a family of P systems solving the SAT problem.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

Well-Tempered P Systems: Towards a Membrane Computing Environment for Music Composition

A proposal of designing a membrane computing environment for music composition is outlined

On a Contribution of Membrane Computing to a Cultural Synthesis of Computer Science, Mathematics, and Biological Sciences

Some topic contribution of membrane computing to a cultural synthesis of computer science, mathematics and biological sciences is presented

Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

Depth-First Search with P Systems

The usual way to find a solution for an NP complete problem in Membrane Computing is by brute force algorithms. These solutions work from a theoretical point of view but they are implementable only for small instances of the problem. In this paper we provide a family of P systems which brings techniques from Artificial Intelligence into Membrane Computing and apply them to solve the N-queens problem.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-0420

Test generation from P systems using model checking

This paper presents some testing approaches based on model checking and using different testing criteria. First, test sets are built from different Kripke structure representations. Second, various rule coverage criteria for transitional, non-deterministic, cell-like P systems, are considered in order to generate adequate test sets. Rule based coverage criteria (simple rule coverage, context-dependent rule coverage and variants) are defined and, for each criterion, a set of LTL (Linear Temporal Logic) formulas is provided. A codification of a P system as a Kripke structure and the sets of LTL properties are used in test generation: for each criterion, test cases are obtained from the counterexamples of the associated LTL formulas, which are automatically generated from the Kripke structure codification of the P system. The method is illustrated with an implementation using a specific model checker, NuSMV. (C) 2010 Elsevier Inc. All rights reserved

Spiking Neural P Systems with Functional Astrocytes

Spiking Neural P Systems (SN P Systems, for short) is a developing field within the universe of P Systems. New variants arise constantly as the study of their properties, such as computational completeness and computational efficiency, grows. Variants frequently incorporate new ingredients into the original model inspired by real neurophysiological structure of the brain. A singular element present within that structure is the astrocyte. Astrocytes, also known collectively as astroglia, are characteristic star-shaped glial cells in the brain and spinal cord. In this paper, a new variant of Spiking Neural P Systems incorporating astrocytes is introduced. These astrocytes are modelled as computing devices capable of performing function computation in a single computation step. In order to experimentally study the action of Spiking Neural P Systems with astrocytes, it is necessary to develop software providing the required simulation tools. Within this trend, P– Lingua offers a standard language for the definition of P Systems. Part of the same software project, pLinguaCore library provides particular implementations of parsers and simulators for the models specified in P–Lingua. Along with the new SN P System variant with astrocytes, an extension of the P–Lingua language allowing definition of these systems is presented in this paper, as well as an upgrade of pLinguaCore, including a parser and a simulator that supports the aforementioned variant.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Using A Kernel P System to Solve The 3-Col Problem

The newly introduced Kernel P systems offer an unitary and elegant way of integrating established features of existing P system variants with new elements with potential value for formal modelling. This paper presents a case study illustrating the expressive power and efficiency of kernel P systems on the 3-Col problem. The use of model checking (in particular of Spin) for formal verification of kernel P systems is also discussed and illustrated in this case.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08–TIC–0420

Implementing P Systems Parallelism by Means of GPUs

Software development for Membrane Computing is growing up yielding new applications. Nowadays, the efficiency of P systems simulators have become a critical point when working with instances of large size. The newest generation of GPUs (Graphics Processing Units) provide a massively parallel framework to compute general purpose computations. We present GPUs as an alternative to obtain better performance in the simulation of P systems and we illustrate it by giving a solution to the N-Queens problem as an example.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía P08–TIC-0420

Automatic 2d image segmentation using tissue-like p system

This paper uses P-Lingua, a standard programming language that is designed specifically for P systems, to automatically simulate the region-based segmentation of 2D images. P-Lingua, which is based on membrane computing, links to Java Netbeans using the PLinguaCore4 Java library to automatically codify the pixels of the input image as long as automatically draw the output segmented image. Many methods have been suggested previously and used for artificial image segmentation, but to the best of our knowledge, none of those techniques were automatic, where the image was codified manually and the visualization of the output image was done manually in the tissue simulator which takes time and effort, especially when dealing with large images in the system. Two types of pixel adjacency have been utilized in this paper, namely; 4-adjacency and 8-adjacency. The jacquard index method has been used to measure the accuracy of the segmentation. The results of the proposed method demonstrated that beside its ability to automatically segmenting 2D images with arbitrary sizes, it is more efficient and faster than the tissue simulator tool, since the latter needs the input image to be codified manually pixel by pixel which makes it impractical for real-world applications