Nonlinear Cellular Automata Research Articles

A Secure Cellular Automata Integrated Deep Learning Mechanism for Health Informatics

Health informatics has gained a greater focus as the data analytics role has become vital for the last two decades. Many machine learning-based models have evolved to process the huge data involved in this sector. Deep Learning (DL) augmented with Non-Linear Cellular Automata (NLCA) is becoming a powerful tool with great potential to process big data. This will help to develop a system that facilitates parallelization, rapid data storage, and computational power with improved security parameters. This paper provides a novel and robust mechanism with deep learning augmented with non-linear cellular automata with greater security, adaptability for health informatics. The proposed mechanism is adaptable and can address many open problems in medical informatics, bioinformatics, and medical imaging. The security parameters considered in this model are Confidentiality, authorization, and integrity. This method is evaluated for performance, and it reports an average accuracy of 89.32%. The parameters precision, sensitivity, and specificity are considered to measure to measure the accuracy of the model.

A DETERMINISTIC APPROACH TO THE SYNCHRONIZATION OF NONLINEAR CELLULAR AUTOMATA

In this work, we introduce a deterministic scheme of synchronization of nonlinear cellular automata with chaotic behavior, connected through a master–slave coupling. By using a definition of Boolean derivative, we utilize the linear approximation of the cellular automata rules to design a deterministic and simple coupling function that ensures synchronization. Our results show that it is possible to synchronize nonlinear chaotic cellular automata using a deterministic coupling function that does not introduce into the slave all the information about the state of the master.

Non-linear cellular automata based edge detector for optical character images

Design of parallel algorithms for edge detection is extremely important for image analysis and understanding. Cellular automata are the most common and simple models of parallel computation and over the last decade, numerous cellular automata techniques have already been proposed. This paper presents a novel method for edge detection of optical character images based on a variant of cellular automata, called non-linear cellular automata. The method consists of three stages and each stage is simple to understand and implement. A standard binarization technique is applied to a grayscale image and boundary conditions are added to the resultant image. Finally, non-linear cellular automata rules are designed and applied simultaneously to all pixels of the image. The suggested scheme has been validated on optical characters (handwritten as well as printed) of different languages. Furthermore, results are compared with standard edge detection techniques in terms of different performance parameters like entropy, kappa values, true positives, false negatives, etc. It is observed that the suggested scheme is superior to other standard schemes. Hence, the scheme has the potential application for character recognition.

비선형 셀룰라오토마타의 도달가능표

Non-linear cellular automata is difficult to analyze mathematically than linear cellular automata. So it is difficult to identify reachable states and attractors of nongroup non-linear cellular automata than nongroup linear cellular automata. In this paper, we propose a new reachable table to overcome these problems. We can see the next state for all the states of the non-linear cellular automata by the proposed reachable table. In addition, we can identify reachable states and attractors by the reachable table.

Simulating complex urban development using kernel-based non-linear cellular automata

Cellular automata (CA) have been increasingly used to simulate complex urban systems. Empirical data can be used to calibrate CA models so that realistic urban patterns can be generated. Traditionally, the calibration procedure employs linear regression methods, e.g., multicriteria evaluation. However, the evolution of urban systems often manifests the complexity of non-linear features, for which the linear transition rules are insufficient. This paper proposes to use the kernel-based learning techniques to acquire non-linear transition rules for CA. The kernel-based approach maps the original data vectors to an implicit high-dimensional feature space, through which complex non-linear problems are translated into simple linear problems. Compared with a widely used non-linear method, neural network, the kernel method is more mathematically “transparent” and therefore the results are easier to be analyzed. A case study of simulating the expansion of Guangzhou, a fast growing city in China, shows that the kernel-based CA even achieves a slightly higher accuracy than a neural-network-based CA.

Design of Nonlinear CA Based TPG Without Prohibited Pattern Set In Linear Time

This paper reports an efficient BIST solution for VLSI circuits. The solution is based on an on-chip Pseudo-Random Pattern Generator (PRPG) for the CUTs (Circuit Under Test) of a VLSI chip that may be accessed through a full or partial scan path. The test solution guarantees non-issuance of the test patterns declared prohibited to a CUT. An n-bit Test Pattern Generator (TPG), for any arbitrary value of n, has been designed in linear time around the nonlinear Cellular Automata (CA). Experimental results confirm the enhanced pseudo-random quality of the generated test patterns, avoiding prohibited patterns due to application of nonlinear CA.

Multifractals Defined by Nonlinear Cellular Automata

It is known that the space-time output patterns of additive cellular automata may be scaled to yield fractals. In this paper, nonadditive cellular automata rules are grouped into equivalence classes, each class defined in terms of a characteristic nonadditive rule. This rule defines a multifractal uniquely associated to each class. It is shown that these multifractals can be generated by a form of matrix substitution system, called concatenation substitution. This allows easy computation of fractal dimensions. It is likely that the multifractal spectra for each class does not possess the usual inverted U shape. A conserved quantity is found to be associated with the concatenation substitution system, and is shown to play a role similar to Langton's λ-parameter.