ATM Functions Adaptation Layer QOS Flow Control Signaling and Routing IP

Atm functions adaptation layer qos flow control

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ATM: Functions-Adaptation Layer-QOS-Flow Control- Signaling and Routing, IP: Routing and Forwarding- QOS- MPLS, SAN: ESCON- Fiber Channel- HIPPI UNIT IV 9 WDM NETWORK DESIGN WDM Network Elements: Line Terminal- Line Amplifier- OXC and its configuration, WDM Network Design: Cost Trade Offs – LTD and RWA Problem- Dimensioning Wave Length Routing Network- Statistical and Maximum Load Dimensioning Model UNIT V 9 ADVANCED OPTICAL NETWORKS Access Network: Overview- HFC- FTTC, Photonic Packet Switching: OTDM Synchronization – Buffering - Header processing- Burst Switching- NTT’s Optical ATM switches-AON-CORD, Long- Haul Networks- Long- Haul Network Case Study- Long- Haul Undersea Networks- Metro Networks- Metro Ring Case Study TOTAL : 45 periods 38
Beyond the syllabus 1. Elastic Optical Networking 2. TRANSPONDER (BVT) REFERENCES 1. Ramaswami R and Sivarajan K, Optical Networks: A Practical Perspective, Morgan Kaufmann, 2nd Edition, 2001. 2. Stern T.E and Bala K, Multiwavelength Optical Networks: A Layered Approach, Addison- Wesley. 3. Agrawal G.P, Fiber-Optic Communication Systems, John Wiley and Sons URL: en.wikipedia.org/wiki/ Optical _ networking networks .com/.../ Optical _ Networking %20Systems_96_Tre... 39
PENEC16 GENETIC ALGORITHM AND APPLICATION L T P C 3 0 0 3 AIM To study about Genetic algorithm in search ,Optimization and machine learning. OBJECTIVES Students will get an introduction about Genetic algorithm. Students will be provided with an up-to-date survey of developments in Genetic algorithm in search,Optimisation and machine learning Enable the students to know techniques involved to support Neural networks UNIT I 9 Fundamentals of genetic algorithm: A brief history of evolutionary computation biological terminology-search space -encoding, reproduction-elements of genetic algorithm-genetic modeling-comparison of GA and traditional search methods. UNIT II 9 Genetic technology: steady state algorithm - fitness scaling - inversion. Genetic programming - Genetic Algorithm in problem solving UNIT III 9 Genetic Algorithm in engineering and optimization-natural evolution –Simulated annealing and Tabu search .Genetic Algorithm in scientific models and theoretical foundations UNIT IV 9 Implementing a Genetic Algorithm – computer implementation - low level operator and knowledge based techniques in Genetic Algorithm. UNIT V 9 Applications of Genetic based machine learning-Genetic Algorithm and parallel processors, composite laminates, constraint optimization, multilevel optimization, real life problem-PSO based on GA-Ant colony Optimization. TOTAL : 45 periods 40
BEYOND THE SYLLABUS: Biological and artificial evolution, Evolutionary algorithms for TSPs, Multiobjective evolutionary algorithms REFERENCES 1 Melanie Mitchell, ’An introduction to Genetic Algorithm’, Prentice-Hall of India, New Delhi, Edition: 2004 2 David.E.Golberg, ’Genetic algorithms in search, optimization and machine learning’, Addision-Wesley-1999 3 S.Rajasekaran and G.A Vijayalakshmi Pai,’Neural Networks, Fuzzy logic and Genetic

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