2 research outputs found
Recommended from our members
Proceedings of the Workshop on Membrane Computing, WMC 2016.
yesThis Workshop on Membrane Computing, at the Conference of Unconventional
Computation and Natural Computation (UCNC), 12th July 2016, Manchester,
UK, is the second event of this type after the Workshop at UCNC 2015 in
Auckland, New Zealand*. Following the tradition of the 2015 Workshop the
Proceedings are published as technical report.
The Workshop consisted of one invited talk and six contributed presentations
(three full papers and three extended abstracts) covering a broad spectrum of
topics in Membrane Computing, from computational and complexity theory to
formal verification, simulation and applications in robotics. All these papers â
see below, but the last extended abstract, are included in this volume.
The invited talk given by Rudolf Freund, âP SystemsWorking in Set Modesâ,
presented a general overview on basic topics in the theory of Membrane Computing
as well as new developments and future research directions in this area.
Radu Nicolescu in âDistributed and Parallel Dynamic Programming Algorithms
Modelled on cP Systemsâ presented an interesting dynamic programming
algorithm in a distributed and parallel setting based on P systems enriched with
adequate data structure and programming concepts representation. Omar Belingheri,
Antonio E. Porreca and Claudio Zandron showed in âP Systems with
Hybrid Setsâ that P systems with negative multiplicities of objects are less powerful
than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov
presented in âExtended Spiking Neural P Systems with Statesâ new results regading
the newly introduced topic of spiking neural P systems where states are
considered.
âSelection Criteria for Statistical Model Checkerâ, by Mehmet E. Bakir and
Mike Stannett, presented some early experiments in selecting adequate statistical
model checkers for biological systems modelled with P systems. In âTowards
Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPUâ,
Raluca Lefticaru, Luis F. MacĂas-Ramos, IonuĆŁ M. Niculescu, LaurenĆŁiu MierlÄ
presented some of the advatages of implementing kernel P systems simulations in
FLAME. Andrei G. Florea and CÄtÄlin Buiu, in âAn Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots.
*http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc-
at-the-conference-on-unconventional-computation-natural-computation
Tissue-like p system for region-based and edge-based image segmentations
Membrane Computing (MC), a relatively recent branch of natural computing is an emerging field in molecular computing. MC aims at abstracting models, called membrane systems or P systems, which mimic the function and structure of a biological cell. Many studies have utilized MC in various applications such as image segmentation. Due to the high computational cost of conventional segmentation techniques, bio-inspired models including MC may be applicable to tackle this limitation. In this study, tissue-like P systems, a variant of MC, with sophisticated communication rules were developed to improve regionbased and edge-based segmentation algorithms for manual and automatic segmenting of 2D artificial and real images. Manual segmentation was applied for artificial images, whereas, the automatic segmentation was applied for artificial and real medical images. The manual segmentation of 2D artificial images was achieved using four, six and eight adjacency pixel connectivity relationships, whereas, the automatic segmentation of 2D artificial and real medical images were achieved using four and eight adjacency pixel connectivity relationships. Two methods were used to realize the automatic edge-based and region-based segmentations. The first method is for 2D artificial images using P-lingua linked to Java Netbeans using the P-linguaCore4 Java Library. The second method is for the 2D real and real medical images using C# linked to P-linguaCore4 Java library. The results of the second method demonstrated the ability of the system to automatically segment 2D real and real medical images with arbitrary sizes and different image formats. The experimental results statistically proved that the methods markedly outpaced the state-of-the-art methods of 2D real image segmentation using the same data set. Furthermore, the methods showed better segmentation accuracy and ability to deal with images of different sizes and types. Extra efficient results such as reducing the number of rules and computational steps were achieved for 2D hexagonal artificial images based on Tissue-like P systems. The main contributions of this study are automatic loading and codifying of the input image as well as automatic visualization of output images after segmentation. Furthermore, six and eight adjacency pixel connectivity relationships should be considered for reducing computational steps, number of rules used and processing time in molecular computing