Image security system using recursive cellular automata substitution

This paper presents a novel image security system based on the replacement of the pixel values using recursive cellular automata (CA) substitution. This proposed image encryption method exhibits the properties of confusion and diffusion because of the characteristics of CA substitution are flexible. The salient features of the proposed image encryption method are its losslessness, symmetric private key encryption, very large number of secret keys, and key-dependent pixel value replacement. Simulation results obtained using some color and gray-level images clearly demonstrate the strong performance of the proposed image security system.     

Intelligent controllers as hierarchical stochastic automata

This paper introduces a design methodology for intelligent controllers, based on a hierarchical linguistic model of command translation by tasks-primitive tasks-primitive actions, and on a two-stage hierarchical learning stochastic automaton that models the translation interfaces of a three-level hierarchical intelligent controller. The methodology relies on the designer's a priori knowledge on how to implement by primitive actions the different primitive tasks which define the intelligent controller. A cost function applicable to any primitive task is introduced and used to learn on-line the optimal choices from the corresponding predesigned sets of primitive actions. The same concept applies to the optimal tasks for each command, whose choice is based on conflict sets of stochastic grammar productions. Optional designs can be compared using this performance measure. A particular design evolves towards the command translation (by tasks-primitive tasks-primitive actions) that minimizes the cost function.     

Finite stochastic automata with variable transition probabilities

The transition probabilities of the stochastic automata we introduce in this paper are dependent upon the number of times the current state has been passed by. All the possible ways an automaton can develop, are represented by a set of matrices, which is formally characterized. Based on this representation, a method to calculate some probabilities of these automata, is given.     

Analysis of stochastic automata algorithm for relaxation labelling

A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will always converge to a consistent labeling     

Convergence theorems for stochastic automata and learning systems

It is shown that every stochastic automaton generates a semigroup of continuous linear operators in order to give conditions for the convergence of certain operators connected with infinite input sequences. The main results of this note are conditions for the infinitesimal stability of stochastic automata. Since under suitable conditions the behaviour of a learning system can be represented by a stochastic automaton, these results apply to the asymptotic stability of learning.     

An iterative bounding method for stochastic automata networks

A method to bound stationary distributions of large Markov chains resulting from networks of stochastic automata is presented. It combines the concepts for bounding the stationary distribution using eigenvector polyhedra with the exploitation of the specific structure of Markov chains resulting from stochastic automata networks. The quality of the bounds depends on the coupling between automata. Three consecutive steps of the method are presented. In the first step bounds are computed using information about single automata in isolation. Bounds for single automata are refined in a second step by considering the environment of an automaton given by the other automata in the network. In a third step, bounds are further improved using a disaggregation step. By means of two small examples it is shown that the method yields tight bounds for loosely coupled automata and that the approach is extremely efficient compared to other bounding methods, let alone compared to an exact numerical analysis.     

Parameter optimization of stochastic automata operating in random environments

Two methods of determining the lower bounds of the rate of convergence of finite stochastic automata are presented. The rate of convergence, defined as the percentage decrease in the distance between the transient probability distribution and the equilibrium probability distribution in each step, is determined as a function of the probability transition matrix. Formulas for parameter optimization for a class of stochastic automata for fast convergence and maximum expediency are derived and illustrative examples of fourth-order systems are given.     

An iterative stochastic algorithm based on distributed learning automata for finding the stochastic shortest path in stochastic graphs

The Journal of Supercomputing - In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on...     

Motion estimation using learning automata

Block-matching algorithms (BMAs) are widely employed for motion estimation. BMAs divide input frames into several blocks and minimize an error function for each block to calculate motion vectors. Afterward, each motion vector is applicable for all of the pixels within the block. Since computing the error functions is resource intensive, many fast-search motion estimation algorithms have been suggested to reduce the computational cost. These fast algorithms provide a significant reduction in computation but often converge to a local minimum. A learning automaton is an adaptive decision-making unit that learns the optimal action through repeated interactions with its environment. Learning automata (LA) have been applied successfully to a wide range of applications including pattern recognition, dynamic channel assignment, and social network analysis. In this paper, we apply LA to motion estimation problem, which is one of the basic problems in computer vision. We compare the accuracy and performance of the suggested algorithms with other well-known BMAs. Interestingly, the obtained results indicate high efficiency and accuracy of the proposed methods. The results suggest that simplicity, efficiency, parallel nature, and accuracy of LA-based methods make them a good candidate to solve computer vision problems.     

String taxonomy using learning automata

A typical syntactic pattern recognition (PR) problem involves comparing a noisy string with every element of a dictionary, X. The problem of classification can be greatly simplified if the dictionary is partitioned into a set of subdictionaries. In this case, the classification can be hierarchical-the noisy string is first compared to a representative element of each subdictionary and the closest match within the subdictionary is subsequently located. Indeed, the entire problem of subdividing a set of string into subsets where each subset contains "similar" strings has been referred to as the "String Taxonomy Problem". To our knowledge there is no reported solution to this problem. In this paper we present a learning-automaton based solution to string taxonomy. The solution utilizes the Object Migrating Automaton the power of which in clustering objects and images has been reported. The power of the scheme for string taxonomy has been demonstrated using random string and garbled versions of string representations of fragments of macromolecules.     

History dependent stochastic automata: A formal view of evolutionary learning

We develop a theoretical framework for the study of evolutionary learning systems. the formalism we use is that of history dependent stochastic automata with suitable structure, as well as related structures. This formalism provides a natural setting in which to describe the learning of classification hierarchies, of control hierarchies and notions of selfreference, all of which are derived as consequences of the ability to learn by association.     

Learning behavior of stochastic automata in the last stage of learning

In this paper, a learning behavior of stochastic automata acting in an unknown random environment is considered. Especially, a learning behavior of stochastic automata in the last stage of learning is investigated. Using the theory of Stochastic Stability and Control [9], it is shown that there exists an upper bound of the probability with which stochastic automaton goes back to an unfavorable state within some finite time.     

Using stochastic learning automata for job scheduling in distributed processing systems

A stochastic model for decentralized control of job scheduling in distributed processing systems is presented. The basic decisionmaker of this model, replicated at each host, consists of a Stochastic Learning Automaton (SLA). Because of the environment under consideration, various practical enhancements to the basic SLA model are developed. It is shown by simulation that the overall performance of the system improves over the basic SLA model. Comparisons to some known analytical models and other algorithms are also made. These comparisons illustrate that the SLA based system performs much better than the M/M/1 and Fractional Assignment analytical models and is reasonably close to the best analytical bound. The range of effectiveness of the SLA algorithm with respect to several others is noted. What is significant about this work is that we have illustrated that it is possible to design a learning controller that is able to dynamically acquire relevant job scheduling information by a process of trial and error, and use that information to provide good performance. Other advantages of the algorithm are its low execution overhead, its potential reliability because it is decentralized, and its ability to recover gracefully from random events such as surges of new jobs entering the system. It is important to note that almost all other scheduling algorithms are tested only under equilibrium conditions.     

Isarithmic flow control using learning automata

Kibernetika i Sistemnyi Analiz, No. 6, pp. 9–16, November–December, 1991.