8-Bit Quantizer for Chaotic Generator With Reduced Hardware Complexity

This article describes how nowadays, data is widely transmitted over the internet in the real time. Wherever the transmission or storage is required, security is needed. High speed processing hardware machine with reduced complexity are used for the security of the data, that are transmitted in real time. The information which is to be secure are encoded by pseudorandom key. Chaotic numbers are used in place of a pseudorandom key. The generated chaotic values are analogous in nature, these analog values are digitized to generate encryption key like 8-bit, 16-bit, 32-bit. To generate an 8-bit key, an 8-bit quantizer is required. The design of 8-bit quantizer requires 256 levels which needs lot of complex hardware to implement. In this article, an 8-bit quantizer is designed with reduced complexity, where hardware requirement is reduced by more than 12 times. Without compromising the randomness of the sequence generated. To increase the randomness and confusion timed hop random selection is used. The randomness of the sequence generated by the chaotic generators is analyzed by NIST test suite, to test for its randomness.

An Improved Particle Swarm Optimization Approach for Solving Machine Loading Problem in Flexible Manufacturing System

Machine loading problem in flexible manufacturing system is considered as a vital pre-release decision. Loading problem is concerned with assignment of necessary operations of the selected jobs to various machines in an optimal manner to minimize system unbalance under technological constraints of limited tool slots and operation time. Such a problem is combinatorial in nature and found to be NP-hard; thus, finding the exact solutions is computationally intractable and becomes impractical as the problem size increases. To alleviate above limitations, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to solve the machine loading problem. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast toward global optimum and hence reduce computational effort further. Twenty benchmark problems available in open literature have been solved using the proposed heuristic. Comparison between the results obtained by the proposed heuristic and the existing methods show that the results obtained are encouraging at significantly less computational effort.

EMERGENCE OF RANDOMNESS FROM CHAOS

In systems theory and science, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems [Aziz-Alaoui & Bertelle, 2009]. In this paper, we use the emergent property of the ultra weak multidimensional coupling of p 1-dimensional dynamical chaotic systems which leads from chaos to randomness. Generation of random or pseudorandom numbers, nowadays, is a key feature of industrial mathematics. Pseudorandom or chaotic numbers are used in many areas of contemporary technology such as modern communication systems and engineering applications. More and more European or US patents using discrete mappings for this purpose are obtained by researchers of discrete dynamical systems [Petersen & Sorensen, 2007; Ruggiero et al., 2006]. Efficient Chaotic Pseudo Random Number Generators (CPRNG) have been recently introduced. They use the ultra weak multidimensional coupling of p 1-dimensional dynamical systems which preserve the chaotic properties of the continuous models in numerical experiments. Together with chaotic sampling and mixing processes, ultra weak coupling leads to families of (CPRNG) which are noteworthy [Hénaff et al., 2009a, 2009b, 2009c, 2010]. In this paper we improve again these families using a double threshold chaotic sampling instead of a single one. We analyze numerically the properties of these new families and underline their very high qualities and usefulness as CPRNG when very long series are computed. Moreover, a determining property of such improved CPRNG is the high number of parameters used and the high sensitivity to the parameters value which allows choosing it as cipher-keys. It is why we call these families multiparameter chaotic pseudo-random number generators (M-p CPRNG).