1,721,072 research outputs found
An RNS architecture for quasi-chaotic oscillators
No full textWideband chaotic carrier is a promising solution for wideband communication, since it overcomes the disadvantages of both narrowband and spread-spectrum communication. It is particularly suited to realize information encryption for secure communication. Chaotic signals can be generated by using discrete-time non-linear dynamical circuits, since they can exhibit a quasi-chaotic ( QC) behavior. A particular implementation of QC digital filters can be based on finite precision arithmetic and, in particular, on residue number system (RNS) circuits, which possess very attractive features with regard to their VLSI implementation. In the present paper, we propose an RNS architecture that can be used in connection with secure communication. Each RNS channel consists of a QC oscillator, having its coefficients belonging to a Galois field defined by a prime modulus. In particular, the QC behavior is ensured by well-known properties of primitive polynomials in this field, which generate the characteristic feedback of the oscillator. We demonstrate in the paper that the proposed RNS architecture yields a cost-effective VLSI implementation, which favorably compares with respect to other secure communication approaches proposed in the technical literature. We obtain encouraging results both in terms of confidentiality of the encrypted information and of throughput rate for real-time applications. Moreover, we propose an extended architecture suited to the protection of the secure communication system against transmission errors, by using the self-correcting ability of Redundant RNS (RRNS)
Neural networks with quantum architecture and quantum learning
No full textA method is proposed for solving the two key problems facing quantum neural networks: introduction of nonlinearity in the neuron operation and efficient use of quantum superposition in the learning algorithm. The former is indirectly solved by using suitable Boolean functions. The latter is based on the use of a suitable nonlinear quantum circuit. The resulting learning procedure does not apply any optimization method. The optimal neural network is obtained by applying an exhaustive search among all the possible solutions. The exhaustive search is carried out by the proposed quantum circuit composed of both linear and nonlinear components. © 2009 John Wiley & Sons, Ltd
The quantum approach leading from evolutionary to exhaustive optimization
No full textWhat bio-inspired algorithms mimic are natural mechanisms governing the macroscopic world for optimizing actual performances that are of vital importance. Neural and neurofuzzy networks, genetic, swarm-intelligence and other evolutionary algorithms are well-known results of this imitation. A completely different situation characterizes the microscopic world governed by quantum mechanics. All the possible solutions exist simultaneously in superposition and the problem is to extract the optimal one. In this case, basic mechanisms of quantum mechanics, i.e., superposition and entanglement, are necessary to mimic nature. Following the latter approach, in this paper a quantum architecture was proposed for determining the maximum/minimum in a set of positive integers which is a basic problem related to optimization. The proposed architecture is based on a suitable nonlinear quantum operator and it solves the said problem by an exhaustive search. This was illustrated in detail in the case of a typical NP-complete problem. © 2012 Asian Network for Scientific Information
RNS quasi-chaotic generators
No full textRNS digital filters are particularly suited to the realisation of quasi-chaotic generators. A very large number of architectures can be devised by relying on different moduli and poles. The proposed scheme is constituted by a cascade of first-order filters having poles coincident with first-order primitives
Neurofuzzy Networks With Nonlinear Quantum Learning
No full textNonlinear quantum processing allows the solution of an optimization problem by the exhaustive search on all its possible solutions. Hence, it can replace advantageously the algorithms for learning from a training set. In order to pursue this possibility in the case of neurofuzzy networks, we propose in this paper to tailor their architectures to the requirements of quantum processing. In particular, superposition is introduced to pursue parallelism and entanglement to associate the network performance with each solution present in the superposition. Two aspects of the proposed method are considered in detail: the binary structure of membership functions and fuzzy reasoning and the use of a particular nonlinear quantum algorithm for extracting the optimal neurofuzzy network by exhaustive search
RNS quasi-chaotic generator for self-correcting secure communication
No full textA novel quasi-chaotic generator is proposed. Its architecture is tailored to a particular secure communication system, which is structured for self-correcting transmission errors. It is composed of a suitable set of RNS digital filters having primitive poles
RC distributed circuits for vibration damping in piezo-electromechanical beams
No full textA RC distributed circuit is proposed in the paper to control vibrations in piezo-electro-mechanical systems. The circuit is designed on the basis of a distributed electro-mechanical model obtained, in particular, for Euler beams. The aim is to achieve mechanical damping while relaxing the constraints on the electrical design. With respect to previous RL approaches, the proposed RC circuit performs better and avoids the use of floating inductors, whose implementation is complex and critical in the stabilization sense. Conversely, even if negative capacitors are necessary, their active realization is simpler and the whole stability of the system can be directly controlled based on explicit constraints derived in the paper
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