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VTU TimeTable for December 2014 / January 2015 Exam

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Updates On ReScheduling Of Examintions

Management And Behavioral Process with subject code 10MBA11 has been re-scheduled from Monday 24th November to Wednesday 10th December [2:00 p.m to 5:00 p.m].

Due to overlapping of exams, subjects with **61 [all branches of B.E 2002 Scheme] has been postponed from Wednesday 10th December to Monday 5th January 2015 [9:30 a.m to 12:30 p.m]

Statistics for Management [10MBA13] has been re-scheduled from Friday 28th Nov to Friday. 12th Dec [2:00 p.m to 5:00 p.m].Know More

VTU TimeTable for December 2014 / January 2015 Exam was last modified: December 8th, 2014 by VTU HUB

VTU Even Semester Calendar 2015

Even Semester Calender 2015

Even Semester Calender 2015

Even Semester for BE will commence on 2nd February 2015 and the last working day will be on 23rd May 2015.
For more details please see the above attached image.Know More

VTU Even Semester Calendar 2015 was last modified: December 7th, 2014 by VTU HUB

M.Tech 4th Semester Bio Informatics Syllabus

Download M. TECH Bio Informatics Syllabus [PDF]

NEUROINFORMATICS
Subject Code : 14BBI421

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of modeling and simulation of neurons and brain to understanding the neurological disorders. Students will gain the insights into the applications of neuroinformatics in developing drugs to treat the neurological disorders.

MODULE 1
Linear Response Theory and Single Neuron Models: Properties of a linear system, Convolution and Fourier transforms. Neuron models – Integrate and Fire model, Multi compartment models and Network Models. Neural Encoding: Introduction; Spike Trains and Firing rates, Spike Train Statistics, Neural encoding and decoding – Neural Code, Estimating Firing Rates, Introduction to Receptive Fields, Neural Decoding and Information theory.

MODULE 2
Entropy, Mutual Information, Bayer’s Theorem: Adaptation and learning. Synaptic plasticity rules. Supervised and unsupervised learning. Classical conditioning and Reinforcement learning. Neuroscience Knowledge Management: Managing knowledge in Neuroscience, Interoperability across Neuroscience databases. Database architectures for Neuroscience applications, XML for data representation and Data model specification.

MODULE 3
Computational Neuronal Modeling and Simulation: Tools and methods for simulation of Neurons and Neural Circuits – Model structure analysis in NEURON, Constructing realistic Neural simulations with GENESIS, Simulators for Neural Networks and Action potentials. Data mining through simulation. Computational exploration of Neuron and Neural Network models in Neurobiology.

MODULE 4
Neuroinformatics in Genetics and Neurodegenerative Disorders: Information approach to Systems Neurogenetics. Computational models of dementia and Neurological problems, Application of Systems biology approach to the neuroscience (application to schizophrenia). Brain Image construction, Analysis and Morphometric tools – Brain image Atlases, Databases and Repositories. Tools and databases for Mapping Neural structure and Connectivity Pattern.Know More

M.Tech 4th Semester Bio Informatics Syllabus was last modified: December 15th, 2014 by VTU HUB

M.Tech Bio Informatics 2nd Semester Syllabus

Download M. TECH Bio Informatics Syllabus [PDF]

ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS
Subject Code : 14BBI253

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of artificial intelligence and applications of artificial intelligence in bioinformatics.

MODULE 1
Introduction to Artificial Intelligence: Introduction to Artificial Intelligence, Problems, Approaches and tools for Artificial Intelligence. Introduction to search, Search algorithms,
Heuristic search methods, Optimal search strategies. Use of graphs in Bioinformatics. Grammers, Languages and Automata.
Current Techniques of Artificial Intelligence: Probabilistic approaches: Introduction to probability, Bayes’ theorem, Bayesian networks and Markov networks.

MODULE 2
Classification methods: Nearest Neighbour method, Nearest Neighbour approach for secondary structure protein folding prediction, Clustering and Advanced clustering techniques. Identification Trees – Gain criterion, Over fitting and Pruning. Nearest Neighbour and Clustering Approaches for Bioinformatics.

MODULE 3
Applications: Genetic programming, Neural Networks for the study of Gene-Gene interactions. Artificial neural networks for reducing the dimensionality of expression data. Cancer classification with Microarray data using Support Vector Mechanics. Prototype based recognition of splice sites. Analysis of Large-Scale mRNA expression data sets by genetic algorithms. Artificial Immune Systems in Bioinformatics. Evolutionary algorithms for the protein folding problem. Considering Stem-Loops as sequence signals for finding Ribosomal RNA genes. Assisting cancer diagnosis.

MODULE 4
Neural Networks: Methods and Applications. Application of Neural Networks to Bioinformatics. Genetic algorithms and Genetic programming: Single-Objective Genetic algorithm, Multi- Objective Genetic algorithm. Applications of Genetic algorithms to Bioinformatics. Genetic programming – Method, Applications, Guidelines and Bioinformatics applications. Boolean Networks, Bayesian Networks and Fuzzy Neural Networks with case studies.Know More

M.Tech Bio Informatics 2nd Semester Syllabus was last modified: December 14th, 2014 by VTU HUB

M.Tech. Industrial Electronics 1st Semester Syllabus

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Simulation Modelling and Analysis
Subject Code : 14ELD155

IA Marks : 50
No. of Lecture Hours /week : 04 Exam Hours : 03
Total no. of Lecture Hours : 50 Exam Marks : 100

Basic simulation modeling: nature of simulation, system models, discrete event simulation, single server simulation, alternative approaches, other types of simulation.
Building valid, credible and detailed simulation models. Techniques for increasing model validity and credibility, comparing real world observations
Selecting input probability distributions. Useful probability distributions, assessing sample independence, activity I, II and III. Models of arrival process.
Random numbers generators: linear congruential, other kinds, testing random number generators. Random variate generation: approaches, continuous random variates, discrete random variates, correlated random variates.
Output data analysis. Statistical analysis for terminating simulations, analysis for steady state parameters. Comparing alternative system configurations. Confidence intervals. Variance reduction techniques. Antithetic and Control variates.Know More

M.Tech. Industrial Electronics 1st Semester Syllabus was last modified: December 13th, 2014 by VTU HUB

M.Tech. Digital Communication Engineering 1st Sem Syllabus

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ADVANCED MATHEMATICS
Subject Code : 14ELD11

IA Marks : 50
No. of Lecture Hours / Week : 04 Exam. Hours : 03
Total No. of Lecture Hours : 50 Exam. Marks : 100

Matrix Theory
QR EL Decomposition – Eigen values using shifted QR algorithm- Singular Value EL Decomposition – Pseudo inverse- Least square approximations
Calculus of Variations
Concept of Functionals- Euler’s equation – functional dependent on first and higher order derivatives – Functionals on several dependent variables – Iso perimetric problems- Variational problems with moving boundaries
Transform Methods
Laplace transform methods for one dimensional wave equation – Displacements in a string – Longitudinal vibration of a elastic bar – Fourier transform methods for one dimensional heat conduction problems in infinite and semi infinite rod.
Elliptic Equation
Laplace equation – Properties of harmonic functions – Fourier transform methods for laplace equations. Solution for Poisson equation by Fourier transforms method
Linear and Non Linear Programming
Simplex Algorithm- Two Phase and Big M techniques – Duality theory- Dual Simplex method. Non Linear Programming –Constrained extremal problems- Lagranges multiplier method- Kuhn- Tucker conditions and solutionsKnow More

M.Tech. Digital Communication Engineering 1st Sem Syllabus was last modified: December 9th, 2014 by VTU HUB

M.Tech 1st Semester Bio Informatics Syllabus

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NUMERICAL METHODS & BIOSTATISTICS
Subject Code : 14BBI11

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
i. The objective of this course is to make students learn basic concepts to solve the numerical and improve their problem solving ability.
ii. To improve their ability to analyze the statistical data to optimize.

MODULE 1
Introduction to statistics and study design: Introduction to statistics, data, variables, types of data, tabular, graphical and pictorial representation of data. Significance of statistics to biological problems, experimental studies; randomized controlled studies, historically controlled studies, cross over, factorial design, cluster design, randomized; complete, block, stratified design, biases, analysis and interpretation.

MODULE 2
Descriptive statistics and Observational study design: Types of variables, measure of spread, logarithmic transformations, multivariate data. Basics of study design, cohort studies, case-control studies, outcomes, odd ratio and relative risks.
Principles of statistical inference: Parameter estimation, hypothesis testing. Statistical inference on categorical variables; categorical data, binomial distribution, normal distribution, sample size estimation

MODULE 3
Comparison of means: Test statistics; t-test, F distribution, independent and dependent sample comparison, Wilcoxon Signed Rank Test, Wilcoxon-Mann-Whitney Test, ANOVA.
Correlation and simple linear regression: Introduction, Karl Pearson correlation coefficient, Spearman Rank correlation coefficient, simple linear regression, regression model fit, inferences from the regression model, ANOVA tables for regression.
Multiple linear regression and linear models: Introduction, Multiple linear regression model, ANOVA table for multiple linear regression model, assessing model fit, polynomials and interactions. One-way and Two-way ANOVA tables, F-tests. Algorithm and implementation using numerical methods with case studies.

MODULE 4
Design and analysis of experiments: Random block design, multiple sources of variation, correlated data and random effects regression, model fitting. Completely randomized design, stratified design. Biological study designs. Optimization strategies with case studies.Know More

M.Tech 1st Semester Bio Informatics Syllabus was last modified: December 9th, 2014 by VTU HUB

M.Tech Computer Aided Engineering 2nd Semester Syllabus

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TRIBOLOGY AND BEARING DESIGN
(Common to MDE,MEA,MMD,CAE)
Sub Code : 14MDE41

IA Marks :50
Hrs/ Week : 04 E x a m H o u r s : 0 3
Total Hrs: 50 Exam Marks :100

Course Objective:
Gives in-depth knowledge regarding hydrodynamic, hydrostatic lubrication and various bearings, with their design and applications

Course Content:

1. Introduction to Tribology: Introduction, Friction, Wear, Wear Characterization, Regimes of lubrication, Classification of contacts, lubrication theories, Effect of pressure and temperature on viscosity. Newton’s Law of viscous forces, Flow through stationary parallel plates. Hagen’s poiseuille’s theory, viscometers.Numerical problems, Concept of lightly loaded bearings, Petroff’s equation, Numerical problems.7 Hours

2. Hydrodynamic Lubrications: Pressure development mechanism. Converging and diverging films and pressure induced flow. Reynolds’s 2D equation with assumptions. Introduction to idealized slide bearing with fixed shoe and Pivoted shoes. Expression for load carrying capacity. Location of center of pressure, effect of end leakage on performance, Numerical problems Journal Bearings: Introduction to idealized full journal bearings. Load carrying capacity of idealized full journal bearings, Sommer feld number and its significance, short and partial bearings, Comparison between lightly loaded and heavily loaded bearings, effects of end leakage on performance, Numerical problems. 12 Hours

3. Hydrostatic Bearings: Hydrostatic thrust bearings, hydrostatic circular pad, annular pad, rectangular pad bearings, types of flow restricters, expression for discharge, load carrying capacity and condition for minimum power loss, numerical problems, and hydrostatic journal bearings.
EHL Contacts: Introduction to Elasto – hydrodynamic lubricated bearings. Introduction to ‘EHL’ constant.Grubin type solution.13
Hours

4. Antifriction bearings: Advantages, selection, nominal life, static and dynamic load bearing capacity, probability of survival, equivalent load, cubic mean load, bearing mountings.
Porous Bearings: Introduction to porous and gas lubricated bearings. Governing differential equation for gas lubricated bearings, Equations for porous bearings and working principal, Fretting phenomenon and its stages.
12 Hours

5. Magnetic Bearings: Introduction to magnetic bearings, Active magnetic bearings. Different equations used in magnetic bearings and working principal. Advantages and disadvantages of magnetic bearings, Electrical analogy, Magneto-hydrodynamic bearings.
6 hoursKnow More

M.Tech Computer Aided Engineering 2nd Semester Syllabus was last modified: December 8th, 2014 by VTU HUB

M.Tech Geo Informatics 2nd Semester Syllabus

Download M.Tech Geo Informatics Scheme And Syllabus [PDF]

FUNDAMENTALS OF CARTOGRAPHY, GEODESY AND GLOBAL POSITIONING SYSTEMS
Subject Code: 14 CGI -21

IA Marks: 50
No. of Lecture Hrs/ Week: 04 Exams Hrs: 03
Total no. of Lecture Hrs: 52 Exam Marks: 100

Objective
Upon completion of this subject students should have gained the knowledge of Cartography, Geodesy, and Global Positioning System and also they become familiar with the basic principles and their applications in Geoinformatics Projects.
Cartography
Introduction to Cartography: Definitions, terms, concepts, types, history, applications, conventional cartography v/s digital cartography, cartographic process, cartographic products, cartographic materials, overview of cartography.
Introduction to Map: Types of map, map scale, classes of maps, map composition, the mapping process, map projection, Map Numbering Systems; Base Maps & Thematic Maps; Map Legend, Symbols & Border
Information; Design & Layout of Maps, geographic content of the map, label placement.
Digital Cartography:Cartography in context of GIS, Principles of cartographic design in GIS, cartographic
generalization, atlases and electronic atlases, hypermaps and digital spatial libraries.
Geodesy
Introduction to Geodesy: Definitions, terms, types, history, fundamental goals of geodesy; shape and size
of the earth, applications, overview.
Projections and Co-ordinate Systems: Classification of map projections, Datum surfaces and Coordinate
system, Transformations, Introduction to Azimuthal, Conical and Cylindrical projections with emphasis on LCC, Polyconic and UTM.
Geometric Geodesy: Earth, geoid and reference Ellipsoid, Everest Spheroid, WGS 84, Vertical datum, Mean
Sea Level, geometry of ellipsoid, level surfaces, plumb line and deflection of the vertical, coordinate system in geodesy.
Satellite Geodesy: Introduction – Normal orbits, Equation of motion and laws of Kepler, geometry of elliptic
orbit, line orbit in space, perturbed orbit, Lagrange and Gaussian Planetary equations, Gravitational
perturbation, Doppler surveying GPS
Introduction to GPS: Definition, concept, GPS working principle, history and timeline, overview.
Technical Description and GPS Observables: System Segmentation – Space segment; control segment,
user segment- types of receivers ; GPS satellite signals, GPS data, position and time from GPS, code phase
tracking, pseudorange navigation, receiver position, time and velocity, carrier phase tracking, GPS
positioning types –absolute positioning, differential positioning; Navigation signals -GPS frequencies;
Calculating positions using C/A code using P(Y) code, code phase v/s carrier phase, augmented GPS, local
augmentation; Accuracy and error sources – atmospheric effects, multipath effects, ephemeris and clock
errors; selective availability, relativity, sagnac distortion. Factors that affect GPS – number of satellites,
multipath, ionosphere, troposphere, satellite geometry, satellite health, signal strength, distance from the
reference receiver, RF interference, loss of radio transmission; GPS interference and jamming – natural
sources, artificial sources; Techniques to improve accuracy- augmentation, precise monitoring, GPS time and data, GPS modernization.
DGPS – History, need for DGPS, concepts and principles, differential corrections, accuracy in DGPS, local
area DGPS, wide area DGPS, carrier phase DGPS, pseudolites, LAAS, WAAS; rapid methods with GPS –
rapid static method, semikinematic method, kinematic method. Real time DGPS.
Planning and Realization of GPS Observations: Setting up an observation plan; practical aspects in field
Observations; observation strategies & network design; Ground control for geometric correction of satellite
imagery using DGPS. Ground control points, types, density, planning, reconnaissance survey, field
observations, Criteria for Selecting reference station, reference station equipments, operational procedures, post processing, Georeferencing.
Applications: military – airborne, marine and land based navigation, and civilian –surveying and mapping,
control surveys, cadastral surveying, navigation, RS, GIS and photogrammetry, geodesy, location,
navigation, tracking, mapping and timing, Engineering and Monitoring; Special applications of GPS, etc.,
GPS Technique and project cost.Know More

M.Tech Geo Informatics 2nd Semester Syllabus was last modified: December 8th, 2014 by VTU HUB

M.Tech Geo Informatics 1st Semester Syllabus

Download M.Tech Geo Informatics Scheme And Syllabus [PDF]

FUNDAMENTALS OF REMOTE SENSING
Subject Code: 14 CGI -11

IA Marks: 50
No. of Lecture Hrs/ Week: 04 Exams Hrs: 03
Total no. of Lecture Hrs: 52 Exam Marks: 100

Objectives:
To understand the basic concepts of remote sensing, systems & techniques of data acquisition and to acquire skills in image processing techniques and interpretation of remote sensing data.

Introduction: Definition of terms, Concepts and types of remote sensing; evolution of remote sensing technology, stages in remote sensing technology, spatial data acquisition, interdisciplinary nature and relation with other disciplines, applications of remote sensing, advantages of RS over conventional methods of survey and inventorying.

Basic Principles of Remote Sensing : Characteristics of electro-magnetic radiation; Interactions between matter and electro-magnetic radiation; Wavelength regions of electro-magnetic radiation; Types of remote sensing with respect to wavelength regions; active and passive remote sensing, Definition of radiometry;
Black body radiation; Reflectance; spectral reflectance of land covers; Spectral Signature; Spectral characteristics of solar radiation; Radiative transfer equation; energy interaction in the atmosphere; energy interactions with the earth’s surface- spectral reflectance curves
Sensors: Types of sensors- passive sensors and active sensors; imaging systems, photographic sensors, characteristics of optical sensors; Sensor resolution- spectral, spatial, radiometric and temporal; Dispersing element; Spectroscopic filter; Spectrometer; Characteristic of optical detectors; Cameras for remote sensing;
Film for remote sensing; non-imaging radiometers, imaging sensors, photograph v/s image, Panchromatic, Multispectral, hyperspectral, stereo images, Optical mechanical line scanner;Pushbroom scanner; Imaging spectrometer; spaceborne imaging sensors, active and passive microwave sensors; Thermal sensors; Atmospheric sensors; Sonar; Laser, radar, hyperspectral sensors. Products from scanner data, Image data characteristics, data selection criteria.
Platforms: Types of platforms- airborne remote sensing, space borne remote sensing; Atmospheric condition and altitude; Attitude of platform; Attitude sensors; Orbital elements of satellite; Orbit of satellite; Satellite positioning systems; satellites for Land, Ocean, and atmospheric studies.
Image Interpretation and Analysis: Fundamentals of aerial photos and satellite image interpretation;
Types of imaging, elements of interpretation; Techniques of Visual interpretation; Generations of Thematic maps
Digital Image Processing: Digital data manipulation and analysis; image rectification – Radiometric correction, Atmospheric correction, Geometric correction; image enhancement – Spatial feature manipulation and multi-image manipulation; classification techniques – Supervised classification and unsupervised
classification.
Advanced Remote Sensing Technologies: Synthetic Aperture Radar; Side Looking Airborne Radar; Hyper
spectral Imaging Spectrometer; Lidar; Thermal Imaging System; Advanced Laser Terrain Mapping.Know More

M.Tech Geo Informatics 1st Semester Syllabus was last modified: December 8th, 2014 by VTU HUB

M.Tech Computer Aided Engineering 2nd Semester Syllabus

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COMPOSITE MATERIALS TECHNOLOGY
(Common to MDE,MEA,MMD,CAE)
Sub Code : 14MST21

IA Marks :50
Hrs/ Week : 04 E x a m H o u r s : 0 3
Total Hrs: 50 Exam Marks :100

Course Objective:
Mechanics of composite materials provides a methodology for stress analysis and progressive failure analysis of laminated composite structures for aerospace, automobile, marine and other engineering applications.

Course Content:
1. Introduction to Composite Materials: Definition, Classification, Types of matrices material and reinforcements, Characteristics & selection, Fiber composites, laminated composites, Particulate composites, Prepegs, and sandwich construction.
Metal Matrix Composites: Reinforcement materials, Types, Characteristics and selection, Base metals, Selection, Applications Macro Mechanics of a Lamina: Hooke’s law for different types of materials, Number of elastic constants, Derivation of nine independent constants for orthotropic material, Two – dimensional relationship of compliance and stiffness matrix. Hooke’s law for two-dimensional angle lamina, engineering constants – Numerical problems.Invariant properties.Stress-Strain relations for lamina of arbitrary orientation, Numerical problems. 10 Hours

2. Micro Mechanical Analysis of a Lamina: Introduction, Evaluation of the four elastic moduli, Rule of mixture, Numerical problems. Experimental Characterisation of Lamina- Elastic Moduli and Strengths
Failure Criteria: Failure criteria for an elementary composite layer or Ply, Maximum Stress and Strain Criteria, Approximate strength criteria, Inter-laminar Strength, Tsa-Hill theory, Tsai, Wu tensor theory, Numerical problem, practical recommendations.
10 Hours
3. Macro Mechanical Analysis of Laminate: Introduction, code, Kirchoff hypothesis, Classical Lamination Theory, A, B, and D matrices (Detailed derivation), Special cases of laminates, Numerical problems. Shear Deformation Theory, A, B, D and E matrices (Detailed derivation)
10 Hours

4. Analysis of Composite Structures: Optimization of Laminates, composite laminates of uniform strength, application of optimal composite structures, composite pressure vessels, spinning composite disks, composite lattice structures
10 Hours

5. Manufacturing and Testing: Layup and curing – open and closed mould processing, Hand lay-up techniques, Bag moulding and filament winding. Pultrusion, Pulforming, Thermoforming, Injection moulding, Cutting, Machining, joining and repair. NDT tests – Purpose, Types of defects, NDT method – Ultrasonic inspection, Radiography, Acoustic emission and Acoustic ultrasonic method.
Applications: Aircrafts, missiles, Space hardware, automobile, Electrical and Electronics, Marine, Recreational and sports equipment-future potential of composites. 10 HoursKnow More

M.Tech Computer Aided Engineering 2nd Semester Syllabus was last modified: December 8th, 2014 by VTU HUB

M.Tech Computer Aided Engineering 1st Semester Syllabus

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APPLIED MATHEMATICS
(Common to MDE,MMD,MEA,CAE,MCM,MAR,IAE,MTP,MTH,MTE,MST,MTR)

Sub Code : 14MDE11 IA Marks :50
Hrs/ Week : 04 E x a m H o u r s : 0 3
Total Hrs: 50 Exam Marks :100

Course Objectives:
The main objectives of the course are to enhance the knowledge of various methods in finding the roots of an algebraic, transcendental or simultaneous system of equations and also to evaluate integrals numerically and differentiation of complex functions with a greater accuracy.
These concepts occur frequently in their subjects like finite element method and other design application oriented subjects.

Course Content:

1. Approximations and round off errors: Significant figures, accuracy and precision, error definitions, round off errors and truncation errors. Mathematical modeling and Engineering problem solving: Simple mathematical model, Conservation Laws ofEngineering.06 Hours

2. Roots of Equations: Bracketing methods-Graphical method, Bisection method, False position method, Newton- Raphson method, Secant Method. Multiple roots, Simple fixed point iteration. Roots of polynomial-Polynomials in Engineering and Science, Muller’s method, Bairstow’s Method Graeffe’s Roots Squaring Method.12 Hours

3. Numerical Differentiation and Numerical Integration: Newton –Cotes and Guass Quadrature Integration formulae, Integration of Equations, Romberg integration, Numerical Differentiation Applied to Engineering problems, High Accuracy differentiation formulae 06 Hours

4. System of Linear Algebraic Equations And Eigen Value Problems: Introduction, Direct methods, Cramer’s Rule, Gauss Elimination Method, Gauss-Jordan Elimination Method, Triangularization method, Cholesky Method, Partition method, error Analysis for direct methods, Iteration Methods.
Eigen values and Eigen Vectors: Bounds on Eigen Values, Jacobi method for symmetric matrices, Givens method for symmetric matrices, Householder’s method for symmetric matrices, Rutishauser method for arbitrary matrices, Power method, Inverse power method .14 Hours

5. Linear Transformation: Introduction to Linear Transformation, The matrix of Linear Transformation, Linear Models in Science and Engineering
Orthogonality and Least Squares: Inner product, length and orthogonality, orthogonal sets, Orthogonal projections, The Gram-schmidt process, Least Square problems, Inner product spaces. 12 HoursKnow More

M.Tech Computer Aided Engineering 1st Semester Syllabus was last modified: December 8th, 2014 by VTU HUB