13th International Conference on Membrane Computing - MTA Sztaki

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S. Verlan, J. Quiros We consider a chain of rules evolving in smax mode and computation based on algorithms detailed in Section 3. According to these assumptions, the block has to perform following steps: Algorithm 2 1. Split the chain into k parts as it is described in section 4.1. 2. For each part. (a) Compute NBV ariants(Π, C, smax). For this purpose algorithm detailed in 3 and 4.1 is used. (b) Obtain the value of n indicating which combination will be chosen (n - th element). Hence his domain is from 0 to NBV ariants(Π, C, smax) − 1. i. Generate a random number rn, where 0 ≤ rn ≤ ⌈lg 2 (NBV ariants(Π, C, smax))⌉ ii. If rn < NBV ariants(Π, C, smax) then n = rn. Otherwise, n = rn + NBV ariants(Π, C, smax). (c) Compute V ariant(n, Π, smax), according to algorithm 1. The computation of NBV ariants(Π, C, smax) uses a subset of operations needed to compute V ariant(n, Π, smax), moreover these operations can be done in parallel with the generation of the random number n, necessary to compute V ariant(n, Π, smax). Hence, this stage can be performed in 2 clock cycles by dividing operations in two sets, called right and left propagation respectively. This block contains one sub-block per rule, which implements operations required in order to obtain number of applications of its rule associated. Interconnections between components are based on design keys and propagation concepts: a sub-block is only connected to blocks located on its right and left. As it is showed by Fig. 2, left propagation is the first to be executed. In this sub-stage, steps 2.a and 2.b.i of algorithm 2 are computed from the last rule to the first one. Right propagation, which is compound by steps 2.b.ii and 2.c, is executed in opposite way in the next clock cycle. One advantage of this approach is that it is not necessary to divide, implicitly, the chain of rules in k parts, deleting a step of the algorithm which let us reduce the number of required cycles from three to two. This logic is implemented, explicitly, by signals prevIsDep and chainStateSignal. After this stage, all rules have a random multiplicity assigned. Application Stage Once system has chosen which rules will be applied (and how many times), appLogic block computes how many objects will be generated and consumed by rules application. Like calcNx, it is an arithmetical block. 444
RIGHT PROPAGATION LEFT PROPAGATION Right propagation Left propagation Fast hardware implementations of P systems Nr from calcNx NEW VALUES OF AUTOMATON NEW CHAINSTATESIGNAL NEW RANDOMMASK NEW RANDOMNUMBER COMB_IN AUTSTATE_IN PREVISDEP sub-block VALUES OF AUTOMATON CHAINSTATESIGNAL RANDOMMASK RANDOMNUMBER COMBINATIONS_OUT NEXTAUTSTATE ISDEP nr to appLogic NR =! 1 NR == 1 INIT CHAINSTATESIGNAL INIT RANDOMMASK UPDATE CHAINSTATESIGNAL UPDATE RANDOMMASK (IF IT IS NECESSARY) UPDATE VALUES OF AUTOMATON ACCORDING TO CHAINSTATESIGNAL AND VALUES GENERATED BY PREVIOS BLOCK. NR != 1 NR == 1 PREVISDEP == 1 PREVISDEP != 1 SET NR = NR SET ISDEP = 0 SET ISDEP = 1 SET AUTSTATE = AUTSTATE_IN SET COMBINATIONS = COMBS_IN SET AUTSTATE = Q0 INIT COMBINATIONS ACCORDING TO RANDOM NUMBER ACCORDING TO AUTSTATE AND COMBINATION SET COMBINATIONS_OUT ACCORDING TO AUTSTATE AND COMBINATION SET NEXTAUTSTATE ACCORDING TO AUTSTATE AND COMBINATION SET NR Fig. 2. Details of sub-blocks which compound assignRule block. Flow of information between sub-blocks in left and right propagation is showed at the top of the figure. Below it, algorithm is detailed using UML notation. Updating Stage The block which saves and updates current configuration is called ObjReg. It contains a register per object which saves multiplicity of the associated object for 445
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Erzsébet Csuhaj-Varjú Marian Gheo
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Editors Erzsébet Csuhaj-Varjú Dep
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Preface In addition to the texts or
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Contents M.A. Martínez-del-Amor, I
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(Tissue) P systems with decaying ob
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Sorin Istrail, Solomon Marcus of pr
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Sorin Istrail, Solomon Marcus stati
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Sorin Istrail, Solomon Marcus his f
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Turing’s three pioneering initiat
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Turing’s three pioneering initiat
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MP systems for systems biology tere
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Yu. Rogozhin this obstacle and to g
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Yu. Rogozhin 10. J. Cocke, M. Minsk
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M. Stannett The question arises, wh
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Regular Contributions
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T. Ahmed, G. DeLancy, A. Paun almos
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T. Ahmed, G. DeLancy, A. Paun purpo
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T. Ahmed, G. DeLancy, A. Paun Fig.
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T. Ahmed, G. DeLancy, A. Paun gener
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T. Ahmed, G. DeLancy, A. Paun Table
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Asynchronuous and maximally paralle
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A. Alhazov, Yu. Rogozhin With coope
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A. Alhazov, Yu. Rogozhin 2.3 P Syst
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A. Alhazov, Yu. Rogozhin Such a sys
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A. Alhazov, Yu. Rogozhin applied, f
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B. Aman, G. Ciobanu formalisms is e
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B. Aman, G. Ciobanu membranes appea
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B. Aman, G. Ciobanu • [ ] recep r
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B. Aman, G. Ciobanu Definition 3. A
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B. Aman, G. Ciobanu { w i , for all
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B. Aman, G. Ciobanu The four transi
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B. Aman, G. Ciobanu A state space i
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B. Aman, G. Ciobanu to reiterate th
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On structures and behaviors of spik
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L. Cienciala, L. Ciencialová, M. P
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H. ElGindy, R. Nicolescu, H. Wu pat
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H. ElGindy, R. Nicolescu, H. Wu (b)
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H. ElGindy, R. Nicolescu, H. Wu Pse
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H. ElGindy, R. Nicolescu, H. Wu to
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H. ElGindy, R. Nicolescu, H. Wu ter
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H. ElGindy, R. Nicolescu, H. Wu 4.
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H. ElGindy, R. Nicolescu, H. Wu Tab
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A formal framework for P systems wi
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A new approach for solving SAT by P
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Maintenance of chronobiological inf
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4 3.5 3 2.5 2 1.5 1 0.5 0 0 50 100
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Using a kernel P system to solve th
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Spiking neural P systems with funct
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L.F. Macías-Ramos, M.J. Pérez-Jim
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Membranes with local environments A
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Membranes with local environments T
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Membranes with local environments -
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Membranes with local environments -
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Membranes with local environments I
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On efficient algorithms for SAT dis
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On efficient algorithms for SAT 2 S
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On efficient algorithms for SAT 2.4
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On efficient algorithms for SAT inc
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On efficient algorithms for SAT Not
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On efficient algorithms for SAT the
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On efficient algorithms for SAT 32.
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A. Obtu̷lowicz - its underlying tr
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A. Obtu̷lowicz 2−ramification
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An analysis of correlative and quan
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Sublinear-space P systems with acti
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Modelling ecological systems with t
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Page 434 and 435: S. Verlan, J. Quiros more relevance
Page 436 and 437: S. Verlan, J. Quiros For a regular
Page 438 and 439: S. Verlan, J. Quiros digital FPGA c
Page 440 and 441: S. Verlan, J. Quiros where k j = N
Page 442 and 443: S. Verlan, J. Quiros Now let us con
Page 446 and 447: S. Verlan, J. Quiros the present co
Page 448 and 449: S. Verlan, J. Quiros Table 2 gives
Page 450 and 451: S. Verlan, J. Quiros Using similar
Page 452 and 453: S. Verlan, J. Quiros 5. M. Maliţa,
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Page 457 and 458: Simplifying Event-B models of P sys
Page 461: Author Index Adorna H., 143 Agrigor
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13th International Conference on Membrane Computing - MTA Sztaki

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