Investigating gate-level evolutionary development of combinational multipliers using enhanced cellular automata-based model

Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

Three-dimensional physics-based cellular automata model for granular shear flow

Assessing cellular automata model behaviour using a sensitivity analysis approach

As the VLSI technology grows up, optimization is required to reduce the complexity, area and power of the digital circuits. In order to optimize the hardware requirement of digital combinational circuits, evolutionary techniques need to be enforced at various levels such as gate level and device level. This paper presents an efficient approach for the optimal design of a combinational logic circuit with a reduced gate count in MATLAB platform. The evolutionary optimization technique used is Particle Swarm Optimization with Aging Leader and Challenger (ALC-PSO). The results obtained after optimization of Full Subtractor circuit using ALC-PSO technique are shown to have a less number of gates compared to human design method. Later on that optimized circuit has been analysed by DSCH3.5 and Microwind3.1 VLSI CAD Tool. The results shown in this paper reflects that technology scaling decreases the area, delay and power consumption which are some of the major requirements of today's VLSI design.

The paper presents new results concerning application of cellular automata (CA) to secret key cryptography extending results presented in Tomassini and Perrenoud (2000), and Tomassini and Sipper (2000). One dimensional, nonuniform CA is considered as a generator of pseudorandom number sequences (PNS) used in cryptography with the secret key. The quality of PNS highly depends on a set of applied CA rules. To find such rules nonuniform CA with two types of rules is considered. The search of rules is performed with use of an evolutionary technique called cellular programming. As the result of the collective behavior of the discovered set of CA rules very high quality PNS are generated. The quality of PNS outperforms the quality of known one dimensional CA-based PNS generators used in secret key cryptography. The extended set of CA rules which was found makes the cryptography system much more resistant on breaking a cryptography key.

Cellular Automata (CA) is an evolutionary computing technique that makes discrete idealizations of differential equations and represents the physical system at the mesoscopic scale. A novel CA approach for predicting the temporal and spatial variations of chlorine in Water Distribution Systems (WDSs) is presented in this paper. Random Walk Particle Tracking (RWPT), a stochastic Lagrangian technique, is used to represent the advection and dispersion processes. A one-dimensional CA-based reactive-transport model for chlorine, named as RWPT_CA model, incorporating advective-dispersive transport mechanism is developed and demonstrated. The significance of the cell dimension in the model algorithm is ascertained, and a deterministic approach is formulated for its selection. An indirect numerical solution technique is developed to improve the computational efficiency of the CA algorithm and to minimize the restrictions in the process of discretization of mass into equivalent particles. The numerical accuracy of the proposed RWPT_CA model is verified by applying it on to a benchmark problem. The RWPT_CA model provided excellent representations of the chlorine concentration profiles for low to medium range dispersion in WDSs. The model testing on a benchmark problem from the literature, well tested by researchers, revealed its effectiveness to derive the chlorine concentration patterns under dynamic hydraulic conditions. The dispersion mechanism was found significant in controlling the temporospatial distribution of chlorine at the nodes farther from the source nodes. The models which consider only advective transport mechanism were found over-predicting the chlorine concentrations, and thereby, establishing untrue representations of the quality of the delivered water.

In cases where the cellular automata (CA) is a direct mapping of a physical, biological or social process, the transition rules may be intuitive. The converse problem of going from observed global behaviour to transition rules is largely intractable. For this reason heuristic search methods, notably evolutionary computation, have been used to deduce rules. In general the rule space to search is vast and evolutionary techniques have been only weakly successful. In earlier work we have shown show that by invoking the structure of rule space, it is possible to dramatically reduce the search space size and thus improve search speed and accuracy. We conjecture that restricting the search space is a more powerful strategy than increasing the algorithm complexity by techniques such as coevolution. We extend the formalism to cover rules of greater complexity and power. The density classification problem for one-dimensional two-state cellular automata has long been of interest to researchers in evolutionary computation. The approach described generates high quality rules and has the potential to achieve the best possible results for this problem.

Materials which form the surface and subcutaneous layers of an extrudate experience large deformations when they traverse the die land. This, when added to the inhomogeneity caused by the dead metal zone, leads to considerable modifications to the deformation parameters when compared to the remainder of the extrusion. The distribution of structure is therefore greatly inhomogeneous. Reference to both empirical and physical models of the recrystallisation process indicates that nucleation and growth will differ at these locations in those aluminium alloys that are usually solution treated and aged subsequent to the deformation process. Since static recrystallisation has a significant influence on many of the properties of the extrudate, it is therefore essential to provide the methodology to predict these variations. In the work presented, a physical model, for AA2024, based on dislocation density, subgrain size and misorientation is modified and integrated into the commercial finite element method (FEM) code, FORGE, to study the microstructure changes. Axi-symmetrical and shape extrusion are presented as examples. The evolution of the substructure influencing static recrystallisation is studied. The predicted results show an agreement with the experimental measurement. The distribution of equivalent strain, temperature compensated strain rate and temperatures are also presented to aid interpretation. Importantly the properties of hard alloys improve as the temperature of the extrusion is raised. This phenomenon is discussed and theoretically justified. This paper also presents some innovative work where the physically based models, and the Cellular Automata (CA) method, are combined to simulate the static recrystallisation process. The FEM is adopted to provide the initial morphology and state variables for the structure models, such as the equivalent strain, the temperature and the equivalent strain rate. The subgrain size, and dislocation densities are calculated from physically based models and are transferred to CA models to construct the data required to define the initial state for recrystallisation. Simulation results are compared with experimental measurements. It is demonstrated that CA integrated with the physically based models is effective in predicting the structural changes by selecting a suitable neighbourhood and reasonable transition rules.

This work investigates elementary cellular automata comparing their dynamic evolution on two topologies: the usual regular lattice and a small-world network constructed as a directed graph. The number of iterations for any initial perturbation to propagate over all cells of the automaton corresponds to how quickly an initial configuration reaches a high entropy state, and the temporal rate of propagation of the perturbation can be evaluated from a chaotic set of elementary cellular automata rules using an entropy measure. Results indicate that the average entropy can be nearly tripled in the small-world network topology, suggesting much faster applications (e.g. in Cryptography) from alterations in the topological arrangement of usual cellular automata regular lattices.

A basic framework to characterize the behavior of two-dimensional (2-D) cellular automata (CA) has been proposed. The performance of the regular structure of the 2-D CA has been evaluated for pseudo-random pattern generation. The potential increase in the local neighborhood structure for 2-D CA has led to better randomness of the generated patterns as compared to LFSR and 1-D CA. The quality of the random patterns generated with 2-D CA based built-in-self-test (BIST) structure has been evaluated by comparing the fault coverage on several benchmark circuits. Also a method of synthesizing 2-D CAs to generate patterns of specified length has been reported. The patterns generated can serve as a very good source of random two-dimensional sequences and also variable length parallel pattern generation having virtually nil correlation among the bit patterns.

When two bodies slide against each other, one part of the dissipated energy causes a topography change. Tribological research on brake bads shows a rich dynamic of the boundary layer: plateau‐like structures of a typical length scale grow with time due to agglomerating wear particles and collapse spontaneously at a stability limit [4], [1]. This time‐dependent behaviour can be modeled with cellular automata, which consider local resolution of temperature, wear particle density and frictional power [4]. Beside this the instationary normal pressure distribution and the distinction between areas with and without contact is expected to have a significant influence [3]. This paper derives a fast scheme to estimate the time‐variant pressure distribution of a deterministic and dynamic topography by a cellular automaton. The approach is discussed in the light of computational performance and the solution's characteristics. (© 2009 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

BIST structures for on-chip generation of random, exhaustive and deterministic test patterns have been discussed in the paper. Iso-morphism between maximal length LFSR and exhaustive CA has been established and the pseudorandomness properties of CAs in terms of well-suited autocorrelation functions have been investigated. Owing to the regularity in interconnection structure, additive group CA (cellular automata) offers advantages over LFSR. A method of covering deterministic test set by a counter-based scheme and its optimisation based on AT2 complexity has been discussed.

The cellular genetic algorithm (CGA) is growing which combines GAs with cellular automata. The individuals are distributed in a toridal grid or presudo landscape and their genetic operator is restricted to within neighborhood. In this paper, we present a cellular genetic algorithm with evolutional rule (CGAE) which more closely mimics the process of evolvement in ecology by cellular automata. It provides a mechanism for maintaining flexible population size and self- adaptive control on migration .We investigate the performance and behavior of the algorithm on complex problem like problems having high epistasis and multimodality. According to numerical optimization problems, CGAE is compared with other CGA. In the paper, the experiment result indicated that CGAE can improve convergent speed and maintain diversity of population. It shows that this novel algorithm has application prospects.

Simulation of Grain Growth during Solidification of Twin-Roll Continuously Cast Pure Aluminum with a Modified CA Model

Parallel architectures and algorithms may offer a solution to the system bottleneck arising from the need to encrypt a very large amount of data without compromising security. In this respect the use of cellular automata with their parallel, simple, regular and modular structure is very promising. We extend the blockcipher algorithm, based on the iterations of so called “toggle” cellular automata rules to two dimensions.The advantages are higher complexity of the crypt-analytical attacks and substantial increase in the speed of the algorithm. Due to its massive parallelism and interconnectivity, the algorithm is very suitable for opto-electronic implementation.