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Bio-inspired computation in telecommunications /

Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Yang, Xin-She (Editor ), Chien, Su Fong (Editor ), Ting, Tiew On (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Waltham, Massachusetts : Morgan Kaufmann, 2015.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 0 0 |a Bio-inspired computation in telecommunications /  |c edited by Xin-She Yang, Su Fong Chien, Tiew On Ting. 
264 1 |a Waltham, Massachusetts :  |b Morgan Kaufmann,  |c 2015. 
264 4 |c ©2015 
300 |a 1 online resource (349 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Front Cover; Bio-Inspired Computation in Telecommunications; Copyright ; Contents ; Preface ; List of Contributors ; Chapter 1: Bio-Inspired Computation and Optimization: An Overview; 1.1. Introduction; 1.2. Telecommunications and optimization; 1.3. Key challenges in optimization; 1.3.1. Infinite Monkey Theorem and Heuristicity; 1.3.2. Efficiency of an Algorithm; 1.3.3. How to Choose Algorithms; 1.3.4. Time Constraints; 1.4. Bio-inspired optimization algorithms; 1.4.1. SI-Based Algorithms; 1.4.1.1. Ant and bee algorithms; 1.4.1.2. Bat algorithm; 1.4.1.3. Particle swarm optimization. 
505 8 |a 1.4.1.4. Firefly algorithm1.4.1.5. Cuckoo search; 1.4.2. Non-SI-Based Algorithms; 1.4.2.1. Simulated annealing; 1.4.2.2. Genetic algorithms; 1.4.2.3. Differential evolution; 1.4.2.4. Harmony search; 1.4.3. Other Algorithms; 1.5. Artificial neural networks; 1.5.1. Basic Idea; 1.5.2. Neural Networks; 1.5.3. Back Propagation Algorithm; 1.6. Support vector machine; 1.6.1. Linear SVM; 1.6.2. Kernel Tricks and Nonlinear SVM; 1.7. Conclusions; References; Chapter 2: Bio-Inspired Approaches in Telecommunications; 2.1. Introduction; 2.2. Design problems in telecommunications; 2.3. Green communications. 
505 8 |a 2.3.1. Energy Consumption in Wireless Communications2.3.2. Metrics for Energy Efficiency; 2.3.3. Radio Resource Management; 2.3.4. Strategic Network Deployment; 2.4. Orthogonal frequency division multiplexing; 2.4.1. OFDM Systems; 2.4.2. Three-Step Procedure for Timing and Frequency Synchronization; 2.5. OFDMA model considering energy efficiency and quality-of-service; 2.5.1. Mathematical Formulation; 2.5.2. Results; 2.6. Conclusions; References; Chapter 3: Firefly Algorithm in Telecommunications; 3.1. Introduction; 3.2. Firefly algorithm; 3.2.1. Algorithm Complexity. 
505 8 |a 3.2.2. Variants of Firefly Algorithm3.3. Traffic Characterization; 3.3.1. Network Management Based on Flow Analysis and Traffic Characterization; 3.3.2. Firefly Harmonic Clustering Algorithm; 3.3.3. Results; 3.4. Applications in wireless cooperative networks; 3.4.1. Related Work; 3.4.2. System Model and Problem Statement; 3.4.2.1. Energy and spectral efficiencies; 3.4.2.2. Problem statement; 3.4.3. Dinkelbach Method; 3.4.4. Firefly Algorithm; 3.4.5. Simulations and Numerical Results; 3.5. Concluding remarks; 3.5.1. FA in Traffic Characterization; 3.5.2. FA in Cooperative Networks; References. 
505 8 |a Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation4.1. Introduction; 4.2. Intrusion detection systems; 4.2.1. IDS Components; 4.2.2. Research Areas and Challenges in Intrusion Detection; 4.3. The method: evolutionary computation; 4.4. Evolutionary computation applications on intrusion detection; 4.4.1. Foundations; 4.4.2. Data Collection; 4.4.3. Detection Techniques and Response; 4.4.3.1. Intrusion detection on conventional networks; 4.4.3.2. Intrusion detection on wireless and resource-constrained networks; 4.4.4. IDS Architecture; 4.4.5. IDS Security. 
520 |a Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students. 
542 |f Copyright and#169: Elsevier Science and Technology  |g 2015 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Telecommunication. 
650 0 |a Natural computation. 
650 2 |a Telecommunications 
650 6 |a Télécommunications. 
650 6 |a Calcul naturel. 
650 7 |a telecommunications.  |2 aat 
650 7 |a TECHNOLOGY & ENGINEERING / Mechanical.  |2 bisacsh 
650 7 |a Natural computation.  |2 fast  |0 (OCoLC)fst01745866 
650 7 |a Telecommunication.  |2 fast  |0 (OCoLC)fst01145830 
700 1 |a Yang, Xin-She,  |e editor. 
700 1 |a Chien, Su Fong,  |e editor. 
700 1 |a Ting, Tiew On,  |e editor. 
776 0 8 |i Print version:  |t Bio-inspired computation in telecommunications.  |d Waltham, Massachusetts : Morgan Kaufmann, ©2015  |h xvii, 330 pages  |z 9780128015384 
856 4 0 |u https://learning.oreilly.com/library/view/~/9780128015384/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
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