An improved spectral efficiency of WiMAX using 802.16G based technology

Divya, S.; Kumar, H. Ananda; Vishalakshi, A.

doi:10.1109/icaccs.2015.7324098

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…The players of the opposite pair try to prevent him from doing it [15][16]. In bridge, special focus in game representation is on the fact that players cooperate in pairs, thus sharing potentials of their hands [17].…”

Section: Fig 2: Bridge Tablementioning

confidence: 99%

“…The success of a contract often lies in the hand making the opening lead. Hence even knowing the location of all cards may sometimes be not sufficient to indicate cards that will take tricks [17]. A card that belongs to the suit has been chosen to have the highest value in a particular game, since a trump can be any of the cards belonging to any one of the players in the pair.…”

Section: No-trump and Trump-suitmentioning

confidence: 99%

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Artificial Neural Network Architectures for Solving the Contract Bridge

Dharmalingam

2020

International Journal of Advanced Information and Communication Technology

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Contract Bridge is an intelligent game, which enhances the creativity with multiple skills and quest to acquire the intricacies of the game, because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined, since the outcome at any intermediate stage is purely based on the decision made on the immediate preceding stage. One among the architectures of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double Dummy Bridge Problem (DDBP) implemented with the neural network paradigm. This study mainly focuses on Cascade-Correlation Neural Network (CCNN) and Elman Neural Network (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (R-prop) Algorithm and Work Point Count System.

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Section: Fig 2: Bridge Tablementioning

confidence: 99%

Section: No-trump and Trump-suitmentioning

confidence: 99%

Artificial Neural Network Architectures for Solving the Contract Bridge

Dharmalingam

2020

International Journal of Advanced Information and Communication Technology

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“…Lakshminarayanan et al [10] studied the effect of FSW welding parameters on the tensile strength of square butt joints made out of AA 7039 aluminum and A384 alloys using Taguchi parametric design approach. K.Anganan et al [11] investigated the mechanical property in AA7075-T6 and A384.0-T6 aluminum alloys of 6.35mm thickness plate in 600 to 1000rpm, feed rate of 30 to 50mm/min and 6 to 10KN of axial load. In this study the conclusions was at the maximum tensile strength of 277.37MPa can be obtained at the tool rotational speed of 920rpm,welding speed of 46mm/min and the axial load of 9.2KN is the optimum welding parameters.…”

Section: Fig 1 : Fsw Processmentioning

confidence: 99%

Experimental Analysis on Mechanical Properties of Friction Stir Welding in Dissimilar Aluminum Alloys

Mr¹,

Mr²,

Mr³

et al. 2020

International Journal of Advanced Information and Communication Technology

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Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in industries for joining aluminum, magnesium, zinc and copper alloys. The FSW process parameters such as tool, rotational speed, welding speed, axial force, etc play major role in deciding the weld quality. A mathematical modeling was developed based on experiments to predict the tensile strength of dissimilar FSW aluminum alloys. The maximum tensile strength of 210 MPa can be obtained at the tool rotational speed of 1100 rpm, welding speed of 35mm/min and an axial load of 7 kN is the Optimum welding parameters.

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“…The assumption is that C, d0 and γ are the same for all hops. After calculating the Signal Noise Ratio for the channel between data sensors and the first level of relaying sensors, the SNR of channel between two nodes that are i hops away from each other is [20].…”

Section: Energy Calculationmentioning

confidence: 99%

Enhancing Energy in WBAN through Cognitive Radio Networks

Suriya

Surya

Veeralakshmi

2020

International Journal of Advanced Information and Communication Technology

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Wireless Body Area Networks (WBAN) provides to measure various physiological and biological data and monitor human body functionalities. In this study, a cognitive radio based coordinator node is designed for Wireless Body Area Networks. Cognitive Radio is capable of connecting various wireless access points with perception and adaptation features. A simulation model has been developed for implementing cognitive radio enabled body area network for analyzing remote health monitoring system for humans. The prominent parameters such as user speed, access point delay, and connection cost are taken into account when selecting the wireless access point. A supervised machine learning technique called Particle Swarm Optimization (PSO) is adapted in the proposed model for improving Energy efficiency and reducing transmission delay in CR enabled WBAN. Simulation is performed using MATLAB and results indicate that the use of proposed PSO for WBAN performs efficiently compared to traditional methods based on MAC protocols.

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An improved spectral efficiency of WiMAX using 802.16G based technology

Cited by 14 publications

References 2 publications

Artificial Neural Network Architectures for Solving the Contract Bridge

Artificial Neural Network Architectures for Solving the Contract Bridge

Experimental Analysis on Mechanical Properties of Friction Stir Welding in Dissimilar Aluminum Alloys

Enhancing Energy in WBAN through Cognitive Radio Networks

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