Institute of Electrical and Electronics Engineers1 Q-bit refers to the smallest unit of information in quantum computing. In green computing, it generally refers to energy-oriented communication links 2 Q-bit individual refers to a set of Q-bits representing a possible solution of a problem in quantum computing. Here, it is an energy-oriented forwarding path 3 Rotation angle refers to the magnitude of solution convergence towards optimal solution in quantum computing. Here, it is the energy difference between paths
Home automation system, is designed to assist and provide support in order to fulfil the needs of elderly and disabled people at home. It has been designed for mobile phones having android platform,to automate Bluetooth interfaced microcontroller which controls home appliances like lights, fans. It presents the automated approach of controlling the devices in a household that could ease the task of using the traditional method of the switch. The most famous and efficient technology for short range wireless communication-Bluetooth,is used here to automate the system has been around for more than a decade. In this project, a voice controlled wireless smart home system has been presented for elderly and disabled people. The proposed system consists of two components namely (1)voice recognition system, (2) wireless system. Android application has been used for voice recognition system. On the other hand, Bluetooth wireless modules have been used to implement the wireless system.
DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.
Background:
Limited energy capacity of battery operated Wireless Sensor Networks
(WSNs) is the prime impediment in the ubiquity of WSNs as the network lifetime depends on the
available energy at the nodes. Prolonging the network lifetime is the principal issue in WSNs and
the challenge lies in devising a strategy for judicious use of available energy resources. Routing has
been one of the most commonly used strategies for minimizing and balancing the energy consumption
of nodes in a WSN.
Methods:
Routing in large networks has been proved to be NP-Hard and therefore meta heuristic
techniques have been applied for handling this problem. Quantum-inspired algorithms are relatively
new meta heuristic techniques which have been shown performing better than their traditional counter-
parts. Therefore, Quantum inspired ant Based Energy balanced Routing (QBER) algorithm has
been proposed in this paper for addressing the problem of energy balanced routing in WSNs.
Results:
Simulation results confirm that the proposed QBER algorithm performs comparatively better
than other quantum inspired routing algorithms for WSNs.
Conclusion:
DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.
DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.
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