Abstract: Voting algorithms are extensively used to make
decisions in fault tolerant systems where each redundant module
gives inconsistent outputs. Popular voting algorithms include
majority voting, weighted voting, and inexact majority voters. Each
of these techniques suffers from scenarios where agreements do not
exist for the given voter inputs. This has been successfully overcome
in literature using fuzzy theory. Our previous work concentrated on a
neuro-fuzzy algorithm where training using the neuro system
substantially improved the prediction result of the voting system.
Weight training of Neural Network is sub-optimal. This study
proposes to optimize the weights of the Neural Network using
Artificial Bee Colony algorithm. Experimental results show the
proposed system improves the decision making of the voting
algorithms.
Abstract: This paper impart the design and testing of
Nanotechnology based sequential circuits using multiplexer
conservative QCA (MX-CQCA) logic gates, which is easily testable
using only two vectors. This method has great prospective in the
design of sequential circuits based on reversible conservative logic
gates and also smashes the sequential circuits implemented in
traditional gates in terms of testability. Reversible circuits are similar
to usual logic circuits except that they are built from reversible gates.
Designs of multiplexer conservative QCA logic based two vectors
testable double edge triggered (DET) sequential circuits in VHDL
language are also accessible here; it will also diminish intricacy in
testing side. Also other types of sequential circuits such as D, SR, JK
latches are designed using this MX-CQCA logic gate. The objective
behind the proposed design methodologies is to amalgamate
arithmetic and logic functional units optimizing key metrics such as
garbage outputs, delay, area and power. The projected MX-CQCA
gate outshines other reversible gates in terms of the intricacy, delay.