Optimizing maximum velocity of fish robot using Hill Climbing Algorithm and Genetic Algorithm

Vo, Tuong Quan; Lee, Byung Ryong; Kim, Hyoung Seok; Cho, Hyo Seung

doi:10.1109/icarcv.2008.4795585

Cited by 1 publication

(2 citation statements)

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“…Hill climbing algorithm is capable of giving better results than other algorithms when search time is limited [64,65].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Energy Detection in Cognitive Radio Networks

Pavithra¹

2014

IJCTT

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............................................................................................................................ ....................................................................................................................... LIST OF TABLESThe emergence of wireless services demands an efficient use of the radio spectrum due to its scarcity. This problem can be addressed by using Cognitive radio (CR) technology that uses the spectrum in an opportunistic manner. Spectral reuse is one application of cognitive radio that permits secondary users/networks to use the licensed spectrum of the primary users when they are not active. Different types of spectrum sensing methods will be explained. The signal model for CR networks under AWGN channel as well as CR networks experiencing Rayleigh, Nakagami, Lognormal, Gamma, Nakagami-Lognormal composite and Nakagami-Gamma composite fading will be discussed. The achievable average probability of detection is presented for different types of channels. Simulation results will be presented for the detection performance of CR networks under different channels for a single SU. After that, suggested methods for improving the detection performance such as cooperative spectrum sensing, spatial correlation and heuristic methods will be investigated.

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“…Hill climbing algorithm is capable of giving better results than other algorithms when search time is limited [64,65].…”

Section: Introductionmentioning

confidence: 99%

“…The change determines if it improves the value of f(x). The process ends if there is no change found in to improve the value of f(x) and x becomes locally optimized [64,65].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Detection in Cognitive Radio Networks

Pavithra¹

2014

IJCTT

View full text Add to dashboard Cite

show abstract

Optimizing maximum velocity of fish robot using Hill Climbing Algorithm and Genetic Algorithm

Cited by 1 publication

References 6 publications

Optimization of Energy Detection in Cognitive Radio Networks

Optimization of Energy Detection in Cognitive Radio Networks

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