09EA01 / 09EE01 applied mathematics for systems engineering




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09EA27 DATA COMMUNICATION NETWORKS

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INTRODUCTION: Definition of Networks – Classification of Networks – LAN, MAN, WAN, internet – Network Topology – Protocols and Standards – Network Models – OSI, TCP/IP Models of networking – Internet (4)


PHYSICAL LAYER AND THE MEDIA: Review of Signals – Data Rate Limits – Performance Issues – Bandwidth, Throughput, Latency, Bandwidth-Delay Product, Jitter. Digital Transmission and Analog Transmission: Line coding techniques, PCM and Delta Modulation techniques – ASK, FSK, PSK, and QAM Techniques – Bandwidth Utilization: Multiplexing and Spreading - Data Transmission using Telephone Networks – Dial-up MODEMS, Digital Subscriber Line (DSL) (10)


DATA LINK LAYER : Error Detection and Correction techniques – Data Link Control: Framing, Flow and Error Control – HDLC and PPP protocols. Multiple Access Techniques – CSMA, CSMA/CD, CSMA/CA – Channelization – TDMA, FDMA, and CDMA

(10)


LANS: Wired LANs– IEEE 802 standards - Ethernet – IEEE 802.3 MAC Frame – Token Ring LAN - IEEE 802.5 MAC Frame – Wireless LANs – IEEE 802.11 standard – Bluetooth Technology – Interconnection of LANs (6)


WANS: Wired WANs -Circuit-Switched Networks, Datagram Networks, Virtual Circuit-Switched Networks, Structure of Circuit and Packet Switches - Wireless WANs – Introduction to Cellular Telephone and Satellite networks (6)


INTERNETWORKING: Internetworking – tunneling – IP Addressing Scheme – Structure of IP Datagram – IP Routing – TCP as Transport Layer Protocol – Structure of TCP Segment – TCP Connection: Establishment and Closing – SMTP Protocol for E-Mail Application. (6)


Total 42

REFERENCES:

  1. Behrouz A. Forouzan, “ Data Communications and Networking”, 4th Edition, Tata McGraw-Hill, New Delhi, 2006

  2. Larry L. Peterson and Bruce S. Davie, “Computer Networks: A Systems Approach”, 4th Edition, Elsevier Publications, New Delhi, 2007

  3. Stanford H. Rowe and Marsha L. Schuh, “Computer Networking”, Pearson Education, New Delhi, 2005.

  4. James Kurose and Keith Ross,” Computer Networking :Top Down Approach featuring the Internet”, Pearson Education, New Delhi,2002.



09EA28/09ED28/09EM24/09EE27 SOFT COMPUTING

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FEED FORWARD NETWORKS AND SUPERVISED LEARNING: Fundamentals – Biological neural network – Artificial neuron – Activation function – Learning rules - Perceptron Networks – Adaline – Madaline – Back propagation networks – Learning factors – Linear separability (6)


SINGLE LAYER FEEDBACK NETWORKS: Hopfield network – Discrete Hopfield networks – Associative memories – Recurrent auto association memory – Bi-directional associative memory – Boltzman machine (6)


UNSUPERVISED LEARNING NETWORKS: Hamming networks – Self-organising feature maps – Adaptive resonance theory network – Instar model – Outstar model – Counter propagation network – Radial basis function networks (7)


CLASSICAL AND FUZZY SETS AND RELATIONS: Properties and Operations on Classical and Fuzzy Sets - Crisp and Fuzzy Relations - Cardinality, Properties and Operations, Composition, Tolerance and Equivalence Relations - Fuzzy Ordering - Simple Problems (6)


MEMBERSHIP FUNCTIONS: Features of membership function - Standard forms and Boundaries - fuzzification - membership value assignments - Fuzzy to Crisp Conversions - Lambda Cuts for fuzzy sets and relations – Defuzzification methods (6)


APPLICATIONS OF NEURAL NETWORKS AND FUZZY LOGIC: Application of Neural Networks - Pattern Recognition - Image compression – Communication - Control systems - Applications of Fuzzy Logic - Fuzzy Pattern Recognition - Fuzzy Image compression - Fuzzy Logic controllers (5)


GENETIC ALGORITHMS: Introduction – Terminologies – Genetic operators – Selection, cross-over and mutation – Fitness function – A simple genetic algorithm – Applications (6)


Total 42


REFERENCES:

1. S N Sivanandam, and S N Deepa, “Principles of Soft Computing”, Wiley India (P) Ltd.,New Delhi, 2007

2. S N Sivanandam, S Sumathi, and S N Deepa, “Introduction to Neural Networks using Matlab 6.0”, Tata McGrawHill Publications, New Delhi, 2005.

3. Laurene Fausett, “Fundamentals of Neural Networks”, Pearson Education India, New Delhi, 2004.

4. Timothy Ross, “Fuzzy Logic with Engineering Applications”, McGraw Hill, Singapore, 1998.

5. Zimmermann H.J., “Fuzzy set Theory and its Applications”, Springer India (P) Ltd, New Delhi, 2006.

6. David E Goldberg, “Genetic Algorithms in Search, Optimisation and Machine Learning:, Pearson Education, New Delhi, 2004.


09EA29/09ED29 NANO COMPUTING

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INTRODUCTION: The development of Microelectronics – The region of Nanoelectronics - The Complexity Problem – The challenge initiated by Nanoelectronics . Basics of Nanoelectronics: Electromagnetic Fields and Photons – Quantization of Action, Charge, and Flux – Electrons behaving as waves – Electrons in potential wells – Ddiffusion Process. (10)


BIOCHEMICAL AND QUANTUM-MECHANICAL COMPUTERS: DNA Computer – Information Processing with Chemical reactions – Nanomachines – Parallel Processing. Quantum Computers – Bit and Qubit – Coherence and Entanglement – Quantum Parallelism. (8)


PARALLEL ARCHITECTURES FOR NANOSYSTEMS: Mono and Multiprocessor Systems – Some considerations to Parallel Processing – Influence of Delay Time – Power Dissipation - Architecture for Processing in Nanosystems: Clasic Systolic Arrays – Processor with large memory – Processor array with SIMD and PIP Architectures – Reconfigurable Computers – The Teramac Concept as a Prototype. (8)


SOFT COMPUTING AND NANOELECRONICS: Methods of Soft Computing – Fuzzy Systems – Evolutionary Algorithms – Connectionistic Systems – Computationally Intelligent Systems – Characteristics of Neural Networks in Nanoelectronics - Local Processing – Distributed and Fault-tolerant Storage – Self-organization. (8)


NANOSYSTEMS AS INFORMATION PROCESSING MACHINES: Nanosystems as Functional Machines – Information Processing as Information Modification – System Design and its interfaces – Requirements of Nanosystems. Uncertainties: Removal of Uncertainties by Nanomachines – Uncertainties in Nanosystems – Uncertainties in the Development of Nanoelectronics. (8)


Total 42

REFERENCE:

1. Karl Goser et.al, “Nanoelectronics and Nanosystems: From Transistors to Molecular and Quantum devices”, Springer, New Delhi, 2005.





09EA30/09ED30/09EM21 OPTIMIZATION TECHNIQUES

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INTRODUCTION TO OPTIMIZATION: Statement of Optimization problems - Classical optimization techniques - Single variable and multi variable optimization - Method of direct substitution constraint variation - Lagrange multipliers multivariable optimization with equality constraints - Kuhn Tucker conditions.                                                                                                  (7)


LINEAR PROGRAMMING: Linear programming definition - Pivotal reduction of general system of equations - Simplex algorithms - Two phases of the simplex method - Revised simplex method - Duality in linear programming.                            (6)


NONLINEAR PROGRAMMING  (ONE DIMENSIONAL):  Unimodal  function  – Elimination  methods  -  Unrestricted and exhaustive search, Dichotomous search, Fibonacci method - Interpolation methods - Direct root method.                              (5)


NONLINEAR PROGRAMMING (UNCONSTRAINED OPTIMIZATION)  : Direct search methods  -  Univariate  method,  Pattern search methods - Rosenbrock's method – The simplex method - Descent method - Conjucate gradient method - Quasi Newton methods.                                                                                                                                                                                  (6)

NONLINEAR PROGRAMMING (CONSTRAINED OPTIMIZATION) : Direct methods - The Complex method - Cutting plane method - Methods of feasible directions and  determination of step length - Termination criteria, determination of step length.   (7)


DYNAMIC PROGRAMMING:  Multistage   decision process  - Computational procedure - Final  value  problem  to  initial  value problem -Continuous dynamic  programming - Discrete dynamic programming.                                                                         (6)


HEURISTIC TECHNIQUES FOR OPTIMIZATION - Neural Networks -  Genetic algorithm – Adaptive genetic algorithm – Typical applications.                                                                                                                                                                 (5)

 

Total   42


REFERENCES:

1.     Nash S G and Ariela Sofer, "Linear and Nonlinear Programming", McGraw Hill Book Com Inc, New York, 1996.

2.     David E Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning",  Addison Wesley Publishing

Company, 1999.

3.     Rao S S., "Optimization Theory and Applications", Wiley Eastern Limited, New Delhi, 2003.

4. Lawrence Hasdorff,” Gradient Optimization and Non-Linear control”, John Wiley & sons Inc, New York, 1976





09EA41 INDUSTRIAL VISIT AND TECHNICAL SEMINAR

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The student will make atleast two technical presentations on current topics related to the specialization. The same will be assessed by a committee appointed by the department. The students are expected to submit a report at the end of the semester covering the various aspects of his/her presentation together with the observation in industry visits. A quiz covering the above will be held at the end of the semester.


09EA51   APPLIED ELECTRONICS LAB - I

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LIST OF EXPERIMENTS


1.        Design and Simulation of Digital Circuits using VHDL and porting them into FPGA

2.        Simulation of NMOS / CMOS circuits

3. Study of DOS/BIOS interrupts

4.        Applications using DSP Processors

5. Implementation of Digital Circuit testing algorithms using C.


09EA52   APPLIED ELECTRONICS LAB – II

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LIST OF EXPERIMENTS


  1. Layout of simple NMOS/CMOS circuits

  2. Study of ARM processor and RTOS

  3. Interface using 8051 Microcontroller

  4. Interfaces using RS232C Bus

  5. Implementation of DSP Algorithms using MATLAB



09EA53/09ED53/09EM53/09EE53 INDUSTRIAL AUTOMATION LAB

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LIST OF EXPERIMENTS


1. Implementation of Star-Delta Starter using RLL for S7-200 PLC

2. Development of a Monitoring Program for Induction Motor in RLL/STL for S7-300

3. PWM/PTO based drive control using PLC

4. Analog Sensor Interface using PLC

5. Monitoring and Control of PLC through HMI

6. Monitoring of Industrial drive through WinCC SCADA system

7. Interfacing of S7-300 with WinCC SCADA system

8. Programming and Control of a robot for pick and place application

9. Machine monitoring and control through Ethernet

10. Simulation experiments on Robot


09EA55/ 09ED55/ 09EM55/ 09EE55 OBJECT COMPUTING AND DATA STRUCTURES LABORATORY


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PRINCIPLES OF OOP: Programming paradigms, basic concepts and benefits of OOP, applications of OOP. (2)

INTRODUCTION TO C++: History of C++, structure of C++, basic data types, derived data types, symbolic constants, dynamic initialization, type modifiers, type casting, operator and control statements, input and output statements. (3)


CLASSES AND OBJECTS: Class specification, member function specification , scope resolution operator, access qualifiers, instance creation, member functions, function prototyping, function components, passing parameters, call by reference, return by reference, inline function, default arguments, overloaded function. Array of objects, pointers to objects, this pointer, dynamic allocation operators, dynamic objects. Constructors, parameterized constructors, overloaded constructors, constructors with default arguments, copy constructors, static members and static objects as arguments, returning objects, friend function and friend class. (7)

OPERATOR OVERLOADING: Operator function, overloading unary and binary operator, overloading the operator using friend function. (2)


INHERITANCE: Defining derived class, single inheritance, protected data with private inheritance, multiple inheritance, multi level inheritance, hierarchical inheritance, hybrid inheritance, multipath inheritance, constructors in derived and base classes, abstract classes. (5)


INTRODUCTION TO DATA STRUCTURES: Abstract data types, primitive data structures, analysis of algorithms, notation. (5)


ARRAYS: Operations, implementation of one, two and multi dimensioned arrays, different types of array applications. (5)


STRINGS: Implementation, Operations, applications. (3)


STACKS: Primitive operations, sequential implementation, applications. Recursion definition, process and implementation using stacks, evaluation of expressions. (3)


QUEUES: Primitive operations, sequential implementation, applications. Priority queues, dequeues. (3)


SORTING: Insertion sort, selection sort, bubble sort, heap sort, radix sort algorithms and analysis. (4)


Total : 42


REFERENCES:

  1. Bjarne Stroustrup, “The C++ Programming Language”, Addison Wesley, 2004.

  2. Stanley B Lippman and Josee Lajoie, “The C++ Primer”, Addison Wesley, 2005.

3. Harvey M Deitel,and Paul J. Deitel, “C++ How to Program”, Prentice Hall, 2007.

4.     Aaron M Tanenbaum, Moshe J Augenstein and Yedidyah Langsam, “Data structures using C and C++”, Prentice Hall of

India, 2005.

5.   Sahni Sartaj, “Data Structures, Algorithms and Applications in C++”, Universities Press, 2005.

6.   Nell Dale, “C++ Plus Data Structures”, Jones and Bartlett, 2006.

7.     Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, Addison-Wesley, 2006.

8.     Robert L Kruse and Clovis L Tondo, “ Data Structures and Program design in C”, Pearson Education, 2005.


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