shafi
2009-07-02 02:05:58 UTC
I hav an option to choose an elective subject amongst the three
1. Computer Networks
2.Embedded Systems
3.Artifical Neural Networks
I am including the syllabbus of each subject also with this
1. Computer Networks
Module 1
Introduction to computer networks – Types of Networks - Layered architecture- OSI reference model, TCP/IP reference model –Internet Protocol Stack – Network Entities in Layers- Connection oriented and Connection less services. Transmission media - description and characteristics - base band and broad band transmission - synchronous and asynchronous transmission - full duplex and half-duplex links. MODEMS serial communication standards - X-21 digital interface.X.25 Networks.
Module 2
Need for data link layer - Error detection and correction Techniques- Elementary data link layer protocols-sliding window protocols - Multiple Access protocols -Random Access protocols: ALOHA-CSMA and CSMA/CD. Terminal handling - polling, multiplexing and concentration. Local area Network: LAN addresses- Address Resolution Protocol-Reverse Address Resolution Protocol. Ethernet: Ethernet Technologies-IEEE standards- Hubs-Bridges and Switches.
Module 3
Network Layer: Virtual circuits and data grams -Datagram and Virtual circuit service- Routing - different types of congestion control – IP protocol – Subnets – Multicasting - Network layer in ATM.
Transport layer – Transport layer services - design issues – Elements of transport Layer – Internet Transport Protocols (TCP and UDP).
Module 4
Session layer - design issue - data exchange – dialogue management - synchronisation -
remote procedure call - client server model.
Application layer - network security and privacy - cryptography – Domain Name System (DNS)- SMTP – SNMP - virtual terminal and file transfer protocols - electronic mail - WWW and HTTP.
References:
1. Andrew S Tannenbaum, Computer Networks, Prentice hall of India Pvt. Ltd, 2003.
2. Uyless Balack, Computer Networks, Protocols Standards & Interfaces, Prentice hall of India Pvt. Ltd, 2000.
3. Zheng, S Akhtar, Networks for computer scientists and Engineers, Oxford Press, 2004
4. S. Keshav, An Engineering Approach to Computer Networking, Pearson education, 2002
5. Uyless Black, Computer Networks - Protocols, Standards and Interfaces, PHI Ltd., 1994
6. Stalling , Local and Metropolitan Area Networks Prentice Hall; 6th edition (April 15, 2000)
7. Jean Walrand Communication networks, Richard D Irwin (May 1991) 2nd Edition
2.Embedded Systems
Module I
Overview of Embedded System: Embedded System, Categories of Embedded System ,Requirements of Embedded Systems, Challenges and Issues in Embedded Software Development, Applications of Embedded Systems in Consumer Electronics, Control System, Biomedical Systems, Handheld computers, Communication devices.
Module II
Embedded Hardware & Software Development Environment :- Hardware Architecture, Micro- Controller Architecture, Communication Interface Standards, Embedded System Development Process, Embedded Operating systems, Types of Embedded Operating systems.
Module III
Real Time & Database Applications :- Real-Time Embedded Software Development, Sending a Message over a Serial Link, Simulation of a Process Control System, Controlling an Appliance from the RT Linux System, Embedded Database Applications using examples like Salary Survey, Energy Meter Readings.
Module IV
Microchip PIC16 family – PIC16F873 processor – features – architecture – memory organization – register file map – I/O ports – PORTA - PORTB – PORTC – Data EEPROM and flash program memory – Asynchronous serial port – SPI mode – I2C mode.
TEXT :
1. Dreamtech Software Team , Programming for Embedded Systems-, Wiley Dreamtech 2002
2. Rajkamal, Microcontrollers- Architecture, programming, Interfacing and system Design, Pearson Education, 2005
3. John B Peatman Design with PIC micro-controllers:, Pearson Education
3. Artificial Neural Networks
EB/CS/IT 705 (C) ARTIFICIAL NEURAL NETWORKS
Module I
Introduction to neural networks. Artificial neural networks. Biological neural networks- Comparison , Basic building blocks of ANN. Activation functions. McCulloch-Pitts Neuron Model, Hebb net. Learning Rules-Hebbian Learning Rules, Perceptron, Delta, Competitive, Boltzmann. Perceptron networks- single layer, multilayer –algorithm.
Module II
Feedback Networks, Discrete Hopfield nets, Continuous Hopfield nets. Feed Forward Networks: Back Propagation Networks, Learning Rule, Architecture, training algorithm. Counter Propagation Network: Full CPN, Forward only CPN, architecture, training phases.
Module III
Adaptive Resonance Theory, architecture, learning in ART, Self Organizing feature maps: Kohonen SOM, Learning Vector Quantization, Max net, Mexican Hat, Hamming net