A RELATIONSHIP BETWEEN COGNITIVE INFORMATION PROCESSING IN LEARNING THEORY AND MACHINE LEARNING TECHNIQUES IN COGNITIVE RADIOS

The relationship between cognitivism as learning theory in education and machine learning is characterized in this survey paper. The cognitivism describes how learning occurs through internal processing of information and thus leads to understanding and retention. Cognitive information processing plays an active role to understand and process information that learner receives and relates it to already known and stored within learner’s memory. Thus, the cognitive approach defines learning as a change in knowledge which is stored in learner’s memory, and not a change in learner’s behaviour. In regard with importance of various learning problems to designing cognitive communications systems the two main classification categories of learning techniques are explained. Furthermore, the cognitive radio learning algorithms that have been proposed are described. Finally, the similarities and differences among the principles of learning theories and machine learning are discussed

Algorithms for Extended Galois Field Generation and Calculation

The paper aims to suggest algorithms for Extended Galois Field generation and calculation. The algorithm analysis shows that the proposed algorithm for finding primitive polynomial is faster than traditional polynomial search and when table operations in GF(pm) are used the algorithms are faster than traditional polynomial addition and subtraction

N-adic Summation-Shrinking Generator. Basic properties and empirical evidences.

The need of software-flexible stream ciphers has led to several alternative proposals in the last few years. One of them is a new Pseudo Random Number Generator (PRNG), named N-adic Summation-Shrinking (NSumSG), which architecture is described in this paper. It uses N-1 parallel working slave summation generators and one N-adic summation generator, controlling the nonlinearity in the generator. The implementation, some properties and statistical tests of NSumSG are given. The results from statistical analysis show that the sequence generated by NSumSG is uniform, scalable, uncompressible, whit large period; consistent and unpredictable. This gives the reason consider the NSumSG as suitable for a particular cryptographic application

International Conference on Computer Systems and Technologies- CompSysTech ’ 2005 An Algorithm for Fast Software Encryption

Abstract: An algorithm for fast software encryption is proposed in this paper. It is based on the architecture of new pseudo random number generator (PRNG), named Self−Shrinking p–adic Generator (SSPG). In the paper first, the basic SSPG architecture and algorithm are recalled. Then, the software implementation in Visual C++ environment is presented. Finally, the results of some images and texts, encrypted with SSPG sequence, are discussed. The proposed algorithm is suitable for fast software encryption because it allows generating uniform, scalable and unpredictable pseudo random sequences with large period

N-adic Summation-Shrinking Generator. Basic properties and empirical evidence, http://eprint.iacr.org

The need of software-flexible stream ciphers has led to several alternative proposals in the last few years. One of them is a new Pseudo Random Number Generator (PRNG), named N-adic Summation-Shrinking (NSumSG), which architecture is described in this paper. It uses N-1 parallel working slave summation generators and one N-adic summation generator, controlling the nonlinearity in the generator. The implementation, some properties and statistical tests of NSumSG are given. The results from statistical analysis show that the sequence generated by NSumSG is uniform, scalable, uncompressible, whit large period; consistent and unpredictable. This gives the reason consider the NSumSG as suitable for a particular cryptographic application