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To effectively assist autonomous robots in better planning and controlling their motion trajectories, enabling them to efficiently navigate to target positions and avoid obstacles and colli- sions. a new tracking algorithm was proposed using membrane computing method. Firstly, utili zing membrane computing to construct cell membrane structures with multiple step sizes increa ses the opportunity for autonomous robots to find feasible paths. Secondly, based on the operating rules of the aforementioned membrane, the workflow of the fast connection stage was designed to find feasible autonomous robot trajectory paths, so that the autonomous robot could avoid obsta cles and collisions, providing support for subsequent tracking. Finally, based on the multi-step parallel membrane structure a feasible autonomous robot trajectory path was found and a sliding mode variable structure function was used to design the control rate. The position error was ad- justed by the control rate to achieve autonomous robot trajectory tracking. The experimental re- sults show that the robot tracking is almost identical to the actual motion trajectory, with small errors(The errors on the X and Y axes are both less than 3 mm), and it has strong robustness. The convergence speed is below 1. 1 s,and it can adapt to uncertain situations. The smoothness of the tracking trajectory can be maintained at over 96%, with high stability.good tracking perform- ance, and a minimum tracking response time of 5 s. which is practical. [ABSTRACT FROM AUTHOR]

The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membrane (HNSNM) systems with novel repartition protocols. Through the introduction of hypergraphs, the HNSNM systems can characterize the high-order relationships among neurons and extend the traditional neuron structure to high-dimensional nonlinear spaces. The HNSNM systems also abstract two biological mechanisms of synapse creation and pruning, and use plasticity rules with repartition protocols to achieve planar, hierarchical and spatial communications among neurons in hypergraph neuron structures. Through imitating register machines, the Turing universality of the HNSNM systems is proved by using them as number generating and accepting devices. A universal HNSNM system consisting of 41 neurons is constructed to compute arbitrary functions. By solving NP-complete problems using the subset sum problem as an example, the computational efficiency and effectiveness of HNSNM systems are verified. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved. [ABSTRACT FROM AUTHOR]

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) offers clients to access services in a variety of situations under the management of a separate cloud service provider. The CPU, network, and storage are each given specific and distinct responsibilities through resource scheduling. Users must wait for resources to become available while tasks are being provided in a cloud environment, which causes longer wait times, and a lower quality of service for cloud users. To overcome these challenges, this paper proposes a novel MEmbrane Computing based RE source Scheduling in Cloud (MERECLOUD) approach for effective resource scheduling with the advent of membrane computing and evolutionary techniques to enhance resource allocation. Initially, the user provides the task to the network to check the resource availability in the cloud environment and the received tasks are prioritized by using Membrane Spiking Neural Network (MSNN). The Firefly Optimization Algorithm (FOA) is implemented to allocate the resources in cloud based on dynamic prediction policy. Finally, the allocated task will be assigned to the virtual machine which enhances the Quality of Service (QoS) and improves availability and reliability with lower error rates. The performance of the proposed MERE-CLOUD approach is evaluated based on throughput, fitness function and task execution parameters on different cloud resources and achieves an accuracy of 94.72% which is highly reliable for task prioritization. [ABSTRACT FROM AUTHOR]

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate. [ABSTRACT FROM AUTHOR]

Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection. [ABSTRACT FROM AUTHOR]

Spiking neural P systems (SN P systems) abstract the structure and function of neurons and nervous systems. By adopting some biological observations or mathematical considerations in SN P systems, many variants have been proposed. In this work, through combining two interesting variants, SN P systems with multiple channels (SNP-MC systems) and SN P systems with autapses (SNP-AU systems), we propose SN P systems with multiple channels and autapses (SNP-MCA systems) and research their universality. SNP-MCA systems are proved to be universal in the generation of numbers. For computing functions, a universal SNP-MCA system with 25 neurons is constructed, which requires fewer neurons than the two variants.

As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in recent years. This work introduces the phenomenon of lateral inhibition in biological nervous systems into SN P systems, and proposes SN P systems with lateral inhibition (LISN P systems). LISN P systems add the property of synaptic length to portray the lateral distance between neurons, and adopt a new form of rules, lateral interaction rules, to describe the reception of spikes by postsynaptic neurons with different lateral distances from the presynaptic neuron. Specifically, an excited neuron produces lateral inhibition on surrounding postsynaptic neurons. Postsynaptic neurons close to the excited neuron, i.e., neurons with small lateral distances, are more susceptible to lateral inhibition and either receive a fewer number of spikes generated by the excited neuron or fail to receive spikes. As the lateral distance increases, the lateral inhibition weakens, and the number of spikes received by postsynaptic neurons increases. Based on the above mechanism, four specific LISN P systems are designed for generating arbitrary odd numbers, arbitrary even numbers, arbitrary natural numbers and arithmetic series, respectively, as examples. By designing working modules, LISN P systems provide equivalence in computational power to the universal register machines in both generating and accepting modes. This verifies the computational completeness of LISN P systems. A universal LISN P system using merely 65 neurons is devised for function computation. According to comparisons among several systems, universal LISN P systems require fewer computational resources. • Spiking neural P systems with lateral inhibition (LISN P systems) are proposed. • A novel type of rules, lateral interaction rules, is introduced in LISN P systems. • Four specific LISN P systems are designed for generating different sets of numbers. • The Turing universality of LISN P systems is proved. • A small LISN P system using 65 neurons is designed for computing functions. [ABSTRACT FROM AUTHOR]

In the framework of membrane computing, several interesting results concerning frontiers of efficiency between the complexity classes P and NP have been found by using different ingredients. One of the main characteristics of cell-like membrane systems is their rooted tree-like structure, where a natural parent-children membrane relationship exists, and objects can travel through the membranes. Separation rules are used as a method to obtain an exponential workspace in terms of membranes in polynomial time. Inspired by cell meiosis, objects from the original membrane are distributed between the two new membranes. In this work, P systems with evolutional symport/antiport rules and separation rules are used to give a solution to SAT, a well known NP-complete problem. One of the advantages of this solution is the use of the environment as a passive agent. [ABSTRACT FROM AUTHOR]

Rice pests and diseases have a significant impact on the quality and yield of rice, and even have a certain impact on and cause a loss in the national agricultural industry and economy. The timely and accurate detection of pests and diseases is the basic premise of formulating effective rice pest control and prevention programs. However, the complexity and diversity of pests and diseases and the high similarity between some pests and diseases make the detection and classification task of pests and diseases extremely difficult without detection tools. The existing target detection algorithms can barely complete the task of detecting pests and diseases, but the detection effect is not ideal. In the actual situation of rice disease and insect pest detection, the detection algorithm is required to have fast speed, high accuracy, and good performance for small target detection, and so this paper improved the popular yolov5 algorithm to achieve an ideal detection performance suitable for rice disease and insect pest detection. This paper briefly introduces the current status and influence of rice pests and diseases and several target detection algorithms based on deep learning. Based on the yolov5 algorithm, the RepVGG network structure is introduced, 3*3 convolution is combined with ReLU, a training time model with multi-branch topology is adopted, and the inference time is reduced through layer merging. To improve algorithm detection speed, the SK attention mechanism is introduced to improve the receptive field of the convolution kernel to obtain more information and improve accuracy. In addition, Adaptive NMS is replaced by Adaptive NMS, the dynamic suppression strategy is adopted, and scores for learning density are set, which greatly improves the problems of missing detection and the false detection of small targets. Finally, the improved algorithm model is combined with membrane calculation to further improve the accuracy and speed of the algorithm. According to the experimental results, the accuracy of the improved algorithm is increased by about 2.7 percentage points, and the mAP is increased by 4.3 percentage points, up to 94.4%. The speed is improved by about 2.8 percentage points, and indicators such as recall rate and AP are improved. [ABSTRACT FROM AUTHOR]

The number system is the representation method of numbers, and number system conversion is the most basic function of a computing system. The decimal system is the most commonly used number system; however, it may not necessarily be the most suitable number system in a computing system. This paper investigates the conversion methods between different number systems and designs corresponding cells, such as P systems, to implement them. The P systems we designed include Π10_2 and Π2_10 to implement conversion between decimal and binary, Π10_m and Πm_10 to implement conversion between decimal and m-base. The operation process of each P system was explained through examples, and their feasibility and effectiveness were verified through simulation software, UPSimulator. [ABSTRACT FROM AUTHOR]

LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with LSTM-SNP model. The ALS model can better capture the sentiment features in the text to compute the correlation between context and aspect words. To validate the effectiveness of the ALS model for aspect-level sentiment analysis, comparison experiments with 17 baseline models are conducted on three real-life data sets. The experimental results demonstrate that the ALS model has a simpler structure and can achieve better performance compared to these baseline models. [ABSTRACT FROM AUTHOR]

Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability. [ABSTRACT FROM AUTHOR]

As a novel biological computing device, the Spiking Neural P system (SNPS) has powerful computing potential. The application of SNPS in the field of arithmetic operation has been a hot research topic in recent years. Researchers have proposed methods and systems for implementing basic arithmetic operations using SNPS. This paper studies four basic arithmetic operations, improves the parallelization of addition and multiplication methods, and designs more effective natural number addition and multiplication SNPS, as well as SNPS for subtraction and for division of natural numbers based on multiple subtractions. The effectiveness of the proposed SNPS is verified by example. Compared with the same kind of SNPS, for the addition operation the number of neurons used in our system is reduced by 50% and the time overhead is reduced by 33%, while for the multiplication operation the number of neurons is reduced by 40%. [ABSTRACT FROM AUTHOR]

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an important field of membrane computing application research in recent years. We discuss the application of locally homogeneous spiking neural P systems in arithmetic operations. The purpose is to design a spiking neural P system with fewer neurons and rule types to perform arithmetic operations. We designed the addition and subtraction of a locally homogeneous spiking neural P system without weight and delay. They include two input neurons to achieve any two binary number subtraction, one input neuron to achieve any two binary number addition and subtraction, and one input neuron to achieve any n binary number addition and subtraction. This is an attempt to apply the locally homogeneous spiking neural P system in arithmetic operations. Compared with the current excellent spiking neural P system performing arithmetic operations, our designed locally homogeneous spiking neural P system is more concise. The system we designed reduces the number of neurons required for n number addition operations by k − 6 and reduces the number of rule types by 5k − 14. [ABSTRACT FROM AUTHOR]

Spiking neural P systems (SNP systems), as computational models abstracted by the biological nervous system, have been a major research topic in biological computing. In conventional SNP systems, the rules in a neuron remain unchanged during the computation. In the biological nervous system, however, the biochemical reactions in a neuron are also influenced by factors such as the substances contained in it. Based on this motivation, this paper proposes SNP systems with rules dynamic generation and removal (RDGRSNP systems). In RDGRSNP systems, the application of rules leads to changes of the substances in neurons, which leads to changes of the rules in neurons. The Turing universality of RDGRSNP systems is demonstrated as a number-generating device and a number-accepting device, respectively. Finally, a small universal RDGRSNP system for function computation using 68 neurons is given. It is demonstrated that the variant we proposed requires fewer neurons by comparing it with five variants of SNP systems. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor networks. Minimum vertex cover (MVC) is an important problem in large-scale graph research. This paper proposes a solver Q-MeaMetaVC based on the MVC framework PEAF and the membrane evolution algorithm framework MEAF. First, the graph is reduced and divided into two types of connected components (bipartite graph and non-bipartite graph) to reduce the scale of the problem. Second, different membrane structures are designed for different types of connected components to better represent the connected component features and facilitate solutions. Third, a membrane evolution algorithm (MEA), which includes fusion, division, cytolysis, and selection operators, is designed to solve the connected components. Then, Q-MeaMetaVC is compared with the best MVC solver in recent years on the test set, and good experimental results that are obtained verify the feasibility and effectiveness of Q-MeaMetaVC in solving the MVC of large-scale graphs. [ABSTRACT FROM AUTHOR]

Evolution-communication spiking neural P systems with energy request rules (ECSNP-ER systems) are proposed and developed as a new variant of evolution-communication spiking neural P systems. In ECSNP-ER systems, in addition to spike-evolution rules and spike-communication rules, neurons also have energy request rules. Energy request rules are used to obtain energy from the environment needed for spike evolution and communication in neurons. The definition, structure and operations of ECSNP-ER systems are presented in detail. ECSNP-ER systems are proved to have the same computing capabilities as Turing machines by using them as number generating/accepting devices and function computing devices. Working non-deterministically, ECSNP-ER systems are used to solve NP-complete problems, using the SAT problem as an example, in linear time. [ABSTRACT FROM AUTHOR]

Considering a class  R  comprising recognizer membrane systems with the capability of providing polynomial-time and uniform solutions for NP-complete problems (referred to as a "presumably efficient" class), the corresponding polynomial-time complexity class PMC R  encompasses both the NP and  co - NP  classes. Specifically, when  R  represents the class of recognizer presumably efficient cell-like P systems that incorporate object evolution rules, communication rules, and dissolution rules, PMC R  includes both the DP and  co - DP  classes. Here, DP signifies the class of languages that can be expressed as the difference between any two languages in NP (it is worth noting that NP ⊆ DP and  co - NP ⊆ co - DP ). As DP-complete problems are believed to be more complex than NP-complete problems, they serve as promising candidates for studying the P vs. NP problem. This outcome has previously been established within the realm of recognizer P systems with active membranes. In this paper, we extend this result to encompass any class  R  of presumably efficient recognizer tissue-like membrane systems by presenting a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems. [ABSTRACT FROM AUTHOR]

This paper investigates an FPGA architecture whose primary function is to accelerate parallel computations involved in the rapid-exploring random tree (RRT) algorithm. The RRT algorithm is inherently serial, while in each computing step there are many computations that can be executed simultaneously. Nevertheless, how to carry out these parallel computations on an FPGA so that a high degree of acceleration can be realized is the key issue. Membrane computing is a parallel computing paradigm inspired from the structures and functions of eukaryotic cells. As a newly proposed membrane computing model, the generalized numerical P system (GNPS) is intrinsically parallel; so, it is a good candidate for modeling parallel computations in the RRT algorithm. Open problems for the FPGA implementation of the RRT algorithm and GNPS include: (1) whether it possible to model the RRT with GNPS; (2) if yes, how to design such an FPGA architecture to achieve a better speedup; and (3) instead of implementing GNPSs with a fixed-point-number format, how to devise a GNPS FPGA architecture working with a floating-point-number format. In this paper, we modeled the RRT with a GNPS at first, showing that it is feasible to model the RRT with a GNPS. An FPGA architecture was fabricated according to the GNPS-modeled RRT. In this architecture, computations, which can be executed in parallel, are accommodated in different inner membranes of the GNPS. These membranes are designed as Verilog modules in the register transfer level model. All the computations within a membrane are triggered by the same clock impulse to implement parallel computing. The proposed architecture is validated by implementing it on the Xilinx VC707 FPGA evaluation board. Compared with the software simulation of the GNPS-modeled RRT, the FPGA architecture achieves a speedup of a 10 4 order of magnitude. Although this speedup is obtained on a small map, it reveals that this architecture promises to accelerate the RRT algorithm to a higher level compared with the previously reported architectures. [ABSTRACT FROM AUTHOR]

Interactive image segmentation is a method for precisely segmenting of the object from background using information entered by the user. However, most interactive segmentation techniques are sensitive to the location and the number of seed points. To obtain a satisfactory result, the user should repeat the segmentation process over and over, and also based on employed technique, it may work well in some limited conditions and applications. To overcome these limitations and enhance the robustness of interactive image segmentation algorithm, this paper proposes a parallel fusion model using the majority voting technique, which not only is more reliable than existing methods, but also requires less user interaction. To this end, at first the input image is segmented by several segmentation methods independently. Then the obtained results are combined using majority voting technique to extract final segmentation result. To reduce the computational overhead of the proposed scheme, a spiking neural-like P system model for parallel implementation of majority voting technique is also proposed. The proposed model has been evaluated and compared with state-of-the-art methods using different metrics, and the obtained results show its efficiency compared to other methods. [ABSTRACT FROM AUTHOR]

To guarantee their locomotion, biped robots need to walk stably. The latter is achieved by a high performance in joint control. This article addresses this issue by proposing a novel human-simulated fuzzy (HF) membrane control system of the joint angles. The proposed control system, human-simulated fuzzy membrane controller (HFMC), contains several key elements. The first is an HF algorithm based on human-simulated intelligent control (HSIC). This HF algorithm incorporates elements of both multi-mode proportional-derivative (PD) and fuzzy control, aiming at solving the chattering problem of multi-mode switching while improving control accuracy. The second is a membrane architecture that makes use of the natural parallelisation potential of membrane computing to improve the real-time performance of the controller. The proposed HFMC is utilised as the joint controller for a biped robot. Numerical tests in a simulation are carried out with the planar and slope walking of a five-link biped robot, and the effectiveness of the HFMC is verified by comparing and evaluating the results of the designed HFMC, HSIC and PD. Experimental results demonstrate that the proposed HFMC not only retains the advantages of traditional PD control but also improves control accuracy, real-time performance and stability. [ABSTRACT FROM AUTHOR]

The focused crawler downloads web pages related to the given topic from the Internet. In many research studies, most of focused crawler predict the priority values of unvisited hyperlinks by integrating the topic similarities based on the text similarity model and equivalent weighted factors based on the manual method. However, in these focused crawlers, there are flaws in the text similarity models, and weighted factors are arbitrarily determined for calculating priorities of unvisited URLs. To solve these problems, this paper proposes a semantic and intelligent focused crawler based on the Semantic Vector Space Model (SVSM) and the Membrane Computing Optimization Algorithm (MCOA). Firstly, the SVSM method is used to calculate topic similarities between texts and the given topic. Secondly, the MCOA method is used to optimize four weighted factors based on the evolution rules and the communication rule. Finally, this proposed focused crawler predicts the priority of each unvisited hyperlink by integrating the topic similarities of four texts and the optimal four weighted factors. The experiment results indicate that the proposed SVSM-MCOA Crawler improve the evaluation indicators compared with the other four focused crawlers. In conclusion, the proposed SVSM and MCOA method promotes the focused crawler to have semantic understanding and intelligent learning ability. [ABSTRACT FROM AUTHOR]

In the framework of Membrane Computing, several efficient solutions to computationally hard problems have been given. To find new borderlines between families of P systems that can solve them and the ones that cannot is an important task to tackle the P versus NP problem. Adding syntactic and/or semantic ingredients can mean passing from non-efficiency to presumed efficiency. Here, we try to get narrow frontiers, setting the stage to adapt efficient solutions from a family of P systems to another one. In order to do that, a solution to the SAT problem is given by means of a family of tissue P systems with evolutional symport/antiport rules and cell separation with the restriction that both the left-hand side and the right-hand side of the rules have at most two objects; that is, with recognizer P systems from TSEC (2 , 2) . This result improves a previous one, when 3 objects could be used in the left-hand side of the evolutional communication rules [ABSTRACT FROM AUTHOR]

In this work we present a novel and proof of concept Spiking Neural P system (for short, SN P systems) simulator that runs on modern web browsers whilst using graphics processing units (for short, GPUs). By creating an SN P system that both utilizes the GPU and runs on modern web browsers, we allow a much more performant SN P simulator that would also be a lot more accessible for researchers to experiment with, and can be integrated into other tools or visualizations transparently without having to learn specific GPU knowledge or techniques. Using previous results on representing SN P system computations using linear algebra, we analyze and implement a computation simulation algorithm on web browsers that runs on the GPU. Since web browsers (at this time) do not have any capabilities for General Purpose computing on GPUs (for short, GPGPU), we exploit the Web Graphics Library (for short, WebGL) and create shaders to generate textures that correspond to computational results of our SN P simulation algorithm. To our knowledge, this is the first work on simulating SN P systems on browser GPUs. Here, we present two different implementations and algorithms as case studies to analyse and compare the performance of the simulations, with particular interest in speedup compared to CPU approaches. [ABSTRACT FROM AUTHOR]

It is known that dissolution rules are necessary in polarizationless P systems with active membranes to solve problems beyond AC 0 using reasonable tight uniformity conditions (Murphy and Woods, Fundam Inf Series 134:129–152, 2014). On the other hand, no solutions of such problems exist using only dissolution rules. In this paper, we show that the NL -complete reachability problem can be solved in polynomial time by polarizationless P systems with active membranes using only dissolution rules under a suitable uniformity condition. [ABSTRACT FROM AUTHOR]

The highly endangered giant panda (Ailuropoda melanoleuca) is the world's most widely recognised conservation icon. Population dynamics models can get on track to a healthy population for giant pandas, and assess its development over time. This paper proposes a new way to study population dynamics of giant pandas in captivity by means of membrane computing, a bio-inspired computational paradigm based on processing multisets of objects within a cell structure, following a series of evolution rules. This framework is used to model the intrinsically stochastic real-life evolution processes of giant pandas, based on pedigree data. This is the first attempt to collect and analyse so complete source, and to investigate population dynamics of giant pandas based on them. Pedigree data consisting of the number of giant panda individuals per age are provided by Chengdu Research Base of Giant Panda Breeding of China. A special simulator has been developed to assist in the design and formal verification of the mathematical model presented. Particularly, the model feasibility, effectiveness, soundness and robustness have been validated by the simulator, which also enables decision-making based on the simulation results of conducted virtual experiments. [ABSTRACT FROM AUTHOR]

A morphogenetic (M) system is an abstract computational model combining properties of membrane (P) systems, such as computing via abstract particles in separate compartments regulating their workflow, with algorithmic self-assembly generalizing original Wang tiles to arbitrary polytopes forming complex shapes in 2D/3D (or generally, dD) space. Even very simple morphogenetic systems can exhibit complex self-organizing behaviour and, at the abstract level, one can observe characteristic properties of morphogenetic phenomena such as controlled growth, self-reproduction, homeostasis and self-healing. Here we focus on computational aspects of the morphogenetic systems. After summarizing a series of results related to their computational universality (in the Turing sense) and computational complexity, we present two small universal M systems (one of them self-healing) and we also demonstrate how morphogenetic systems relate to the classes P and NP. [ABSTRACT FROM AUTHOR]

The model of Spiking Neural P systems (SNP systems) is a widespread computational model in the area of membrane computing. It has numerous applications, especially related to machine learning. Most of these applications require a custom variant of SNP systems, differing by the rule form and by semantics. The model of network of cells and the formal framework for SNP systems were developed to help the analysis of such custom models, to compare and relate them to each other and to other models of computing. The model specifies the data structure, the rules and the update procedure, while the formal framework concentrates on how the input, output and the choice of the update strategy are handled. Together, these concepts specify a concrete instance of a network of cells that strongly bisimulates the desired model, thus making easier the process of the creation of new models and the extension of existing ones. Since the formal framework is rather generic, it might be slightly complex to use it for concrete cases. This paper provides a tutorial that explains the model of networks of cells and the basic concepts used in the formal framework for SNP systems. It gives a series of examples for the analysis of existing models, their bisimulation and their extension by different features. [ABSTRACT FROM AUTHOR]

Human computation is a technology inspired by nature. The majority of its computational sources are physiologically motivated computations. The existing calculation, on the other hand, uses some conventional techniques to complete the work. In the cloud, IaaS is one of the most basic services, offering a wide range of functions to a large number of customers. Modelling a number of species using P systems with different membrane structure types to predict the number of individuals is a major advantage of the proposed work. Even though it provides large number services (or) features to the user always it faces many numerous obstacles in Infrastructure service in cloud network, such as Authentication and Authorization, Data leakage and monitoring, End to End encryption, there is a risk of lack of security. Traditional encryption approaches also employ efficient methods to build cloud network security. It does, however, have some flaws in terms of IaaS. The work proposed membrane infrastructure based on the quantum inspiration. It will used to overcome such challenges. It is possible to minimize current constraints by employing this strategy. The paper proposes to employ basic distributed computational methods in an open and natural setting to provide membrane systems as a suitable framework for cloud environments for providing unique security among cloud users. They employ a framework for defending against intruder attacks. It is possible to address key cloud computing security difficulties by integrating Quantum Computing between the membrane environments with Cloud Computing technology. The proposed effectiveness and outcomes are monitored with the help of Internet of Things (IoT). This level introduces a revolutionary method to cloud security by incorporating quantum protocols into a membrane environment. The proposed SEDFA is the better method with all types of datasets and the Communication Cost by 5%, Encoding Time (ET) by 2 s, and Decoding Time (DT) by 0.5 s.

Membrane systems are a class of bio-inspired computing models,inspired by the structure and function of cells,tissue,organ and bio-systems.Spiking neural P systems with rules on synapses(SNPRS) are a type of membrane systems,inspired by the way that neurons communicate information.In SNPRS,each neuron is a basic unit for storing information,and each synapse is a medium for integrating and transmitting information.The whole system processes information in the distributed and parallel way.In this paper,we review the definition and related notions of SNPRS.Then,we introduce a few variants of SNPRS,and give a comparison among the variants of SNPRS.Furthermore,we provide results on the computation power of SNPRS(and their variants) working in different modes and on the application of the systems,such as solving NP-hard problems,implementing arithmetic operations,and breaking RSA.Additionally,some open problems are provided to suggest directions for further theore-tical as well as applicable research on SNPRS.

This paper deals with a synthesis method of membrane computer systems inspired from the biological cells structure. The functional concept of the computer system is based on the internal structure, chemical transformations and the way of interaction between living cells. Thus, the computer system is composed of a set of autonomous, homogeneous or heterogeneous computing cells, which communicate with each other in synchronous or asynchronous mode. The algorithmic complexity of the solved problem depends on the knowledge gained, the rules of data processing and the computer system topology. It is proposed the sequence of operations for the synthesis of membrane computer systems with implementation in reconfigurable FPGA architectures which includes: analysis of behavioural models of living organisms and their interpretation in artificial intelligence models; structure and functional description of the computing cell; cell modelling and performance evaluation using Petri nets; Van diagram of the membrane computer system topology; formal description of the membrane computer system topology by applying the JSON formatting language; validation, functional modelling and performance evaluation of the membrane computer system topology using Petri nets; Hardware Description Language (HDL) code of the cell from which the principle electrical diagram and the time diagrams are obtained; implementation of the computer system in the FPGA circuit.

Numerical spiking neural (NSN) P systems are a class of distributed parallel neural computing devices, where the values of numerical variables are used to encode the information, the programs that process the variables are expressed by continuous production functions. In this work, numerical spiking neural P systems with functions on synapses (NSNFS P systems), as a variant of numerical spike neural P systems, are proposed. In NSNFS P systems, continuous production functions are considered at synapses of neurons and used to transmit information between two neurons connected by a synapse. The computation power of NSNFS P systems as a kind of digital generating devices and digital accepting devices is investigated respectively, when the continuous production functions on each synapse are linear and only one numerical variable is involved in each neuron. The results show that numerical spiking neural P systems with production functions on synapses are still universal. • A novel Turing complete paradigm of spiking neural P systems with synapses as computational units. • The Turing completeness of the proposed systems is proved as number generating devices. • The Turing completeness of the proposed systems is proved as number accepting devices. [ABSTRACT FROM AUTHOR]