A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

Yuan, Jianying; Guo, Dequan; Zhang, Gexiang; Paul, Prithwineel; Zhu, Ming; Yang, Qiang

doi:10.3390/molecules24071235

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…Finally, the techniques discussed in this work are currently being generalized in order to allow efficient hardware implementations of membrane computing models [38], such as cell-like P systems [39,40,41], bacterial P systems [42] and spiking neural P systems [43,44,45,46,47,48,49], used for processing images [50], controlling mobile robots [51], robot path-planning [52,53], image processing [54] and modeling complex systems [55].…”

Section: Discussionmentioning

confidence: 99%

Reaction Systems and Synchronous Digital Circuits

et al. 2019

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A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause–effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design.

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Section: Discussionmentioning

confidence: 99%

Reaction Systems and Synchronous Digital Circuits

et al. 2019

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“…Image edge detection is a fundamental task in image processing and computer vision. Yuan et al [55] applied the enzymatic numerical P system (ENPS) to solve image edge detection problems. ENPS was a cell-like P system with a nested membrane structure consisting of four membranes.…”

Section: Bio-inspired Researchmentioning

confidence: 99%

Molecular Computing and Bioinformatics

Liang

Zhu

et al. 2019

Molecules

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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

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“…For the theoretical research of membrane computing, three types of P systems have been extended to obtain several universal computational models [ 3 , 4 , 5 , 6 , 7 ], and the computational complexity of extended P systems has been explored [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. For the application research of membrane computing, existing studies have realized the integration of membrane computing with algorithms for applications in robot control [ 21 , 22 ], data modeling, and optimization [ 23 , 24 , 25 , 26 ], algorithms for solving NP problems [ 27 , 28 , 29 ], clustering algorithms [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], and image processing [ 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes

Liu

Zhao

2022

Entropy

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Spiking neural P systems (SN P systems for short) realize the high abstraction and simulation of the working mechanism of the human brain, and adopts spikes for information encoding and processing, which are regarded as one of the third-generation neural network models. In the nervous system, the conduction of excitation depends on the presence of membrane potential (also known as the transmembrane potential difference), and the conduction of excitation on neurons is the conduction of action potentials. On the basis of the SN P systems with polarizations, in which the neuron-associated polarization is the trigger condition of the rule, the concept of neuronal membrane potential is introduced into systems. The obtained variant of the SN P system features charge accumulation and computation within neurons in quantity, as well as transmission between neurons. In addition, there are inhibitory synapses between neurons that inhibit excitatory transmission, and as such, synapses cause postsynaptic neurons to generate inhibitory postsynaptic potentials. Therefore, to make the model better fit the biological facts, inhibitory rules and anti-spikes are also adopted to obtain the spiking neural P systems with membrane potentials, inhibitory rules, and anti-spikes (referred to as the MPAIRSN P systems). The Turing universality of the MPAIRSN P systems as number generating and accepting devices is demonstrated. On the basis of the above working mechanism of the system, a small universal MPAIRSN P system with 95 neurons for computing functions is designed. The comparisons with other SN P models conclude that fewer neurons are required by the MPAIRSN P systems to realize universality.

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A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

Cited by 12 publications

References 56 publications

Reaction Systems and Synchronous Digital Circuits

Reaction Systems and Synchronous Digital Circuits

Molecular Computing and Bioinformatics

Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes

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