Cloud Types and Services, Service Scalability Over the Cloud, Scientific Services on the Cloud, Novel Market-Oriented Dynamic Collaborative Cloud Service Platform.
ModuleIV +V Cloud Application
Enterprise Knowledge Clouds: Applications and Solutions, Open Science in the Cloud:
Towards a Universal Platform for Scientific and Statistical Computing, Multidimensional Environmental Data Resource Brokering on Computational grids and scientific Clouds, HPC on competitive Cloud Resources.
Module VI
Planning for Cloud
Managing and Securing Cloud Services, Governing the Cloud, Virtualization, Managing Desktops and Devices in the Cloud.
Module VII
Hybrid Computing
Using Hybrid Grid/Cloud Computing Technologies for Environmental Data elastic Storage, Processing, and Provisioning.
Books:
1. Borko Furht Armando Escalante-Hand book of Cloud Computing, Editors, springer Publication.
2. Judith Hurwitz, Robin Bloor, Marcia Kaufman, Fern Halper-Cloud Computing for
Dummies, Wiley
Publication.
3. John W. Rittinghouse, JamesF. Ran some -Cloud Computing: Implementation,
management and
security, CRC Press, Taylor and Francis Publication.
IMCA-7017 BIO COMPUTING [3(3-0-0)]
Module I
Introduction: Introduction to Biological Algorithms, Modeling printer, DNA Restriction Mapping , Regulatory Motor finding.
Module II
Genome Rearrangements, Sorting by Reversals, Approximation Algorithms, A Greedy Approach to modify finding, the power of DNA sequence comparison, Longest common subsequences, Global sequence Alignment, Scoring Alignments, Local Sequence Alignment . Alignment with Gap Penalities, Multiple Alignment.
Module III
Gene Prediction , Statistical Approaches to Gene Prediction, Similarity- Based Approaches to prediction, spliced Alignment, Graphs & Genetics, DNA sequencing , Shortest Superstring Problem, DNA arags as an Alternative Sequence Timeline.
Module IV
Fragment Assembly in DNA Sequencing Protein Sequencing & Identification , The Peptide Sequencing Problem ,Spectrum Graphs, Protein Identification via Database Seanely .
Module V
Combination Pattern Mateling , Clnstering, Hierarerical Clrstering K-means Clrstering, Clnstering & Connected Clignes, Small Parsimony Problem Large Parsiming Problem.
Module VI
Databases: Characteristics of Bioifnformatics Databases, Categories of Bioinformatics Databases, Navigating Databases Sequenc Database.
Module VII
Tools: Data submission Tools, DataAnalysis Tools, Predictaion Tools Modelling Tools . Books:
1. Neil C. Jones & P.A. Pevzner- An Introduction to Bioinformatics Algorithms,
Ane Book Pvt.
Ltd. 2009
2. Orbita Base , K.K.Thukral – BioInformatics, Oxford University Press 2008
IMCA7019 DIGITAL IMAGE PROCESSING [3(3-0-0)]
Module I
Introduction : Background, Digital Image Representation, Fundamental Steps in Image Processing, Elements of a Digital Image Processing System.
Digital Image Fundamentals : Elements of Visual Perception, A Simple Image Model, Sampling and Quantization, Some Basic Relationships between Pixels, Imagining Geometry.
Module II
Image Transforms : Introduction to the Fourier Transform, The Discrete Fourier Transform, Some Properties of the Two-Dimensional Fourier Transform, Other Separable Image Transforms.
Module III
Image Enhancement : Spatial Domain Methods, Frequency Domain Methods, Some Simple Intensity Transformations, Histogram Processing, Image Subtraction, Image Averaging, Background, Smoothing Filters, Sharpening Filters, Lowpass Filtering, Highpass Filtering, Generation of Spatial Masks from Frequency Domain Specifications.
Modules IV + V
Image Restoring : Degradations Model - Definitions, Degradation Model for Continuous Functions, Diagonalization of Circulant and Block-Circulant Matrices, Circulant Matrices, Block Circulant Matrices, Effects of Diagonalization on the Degradation Model, Algebraic Approach to Restoration, Unconstrained Restoration, Constrained Restoration, Inverse Filtering – Formulation, Removal of Blur Caused by Uniform Linear Motion, Restoration in the Spatial Domain, Geometric Transformation.
Modules VI + VII
Image Compression : Fundamentals – Coding Redundancy, Interpixel Redundancy, Psychovisual Redundancy, Fidelity Criteria. Image Compression Models – The Source Encoder and Decoder, The Channel Encoder and Decoder. Elements of Information Theory – Measuring Information, The Information Channel, Fundamental Coding Theorems, Using Information Theory. Error-Free Compression – Variable-Length Coding, Bit-Plane Coding, Lossless Predictive Coding. Lossy Compression – Lossy Predictive Coding, Transform Coding.
Text Book :
Rafael. C. Gonzalez & Richard E.Woods.- Digital Image Processing, 2nd Edition Pearson Education, New Delhi - 2006
Reference Books:
1. W.K.Pratt.-Digital Image Processing ,3/e Edn., John Wiley & sons, Inc-2006 2. A.K.Jain.- Fundamentals of Digital Image Processing, PHI, New Delhi- 2006 3. M. Sonka et.al Image Processing, Analysis and Machine Vision, 2nd Edition , Thomson,
Learning, India Edition-2007.
IMCA-7021 GENETIC ALGORITHM AND SWARM INTELLIGENCE [3(3-0-0)]
Modules I +II
Evolutionary Computation as a Process Modeling tool and its historical perspective.
Chromosome representation, encoding, decoding, Genetic operators: Selection, Crossover, Mutation, Elitism, Schema Theorem, Convergence theorem, real-coded GA, Ordered GA, Steady-state GA, Multi-objective evolutionary algorithms, applications in search and optimization
Module III
Mathematical Construction of Genetic Operators. Schema Theorem of John Holland.
Variants of Binary Encoded Genetic Algorithms: Micro Genetic Algorithm, Greedy Genetic Algorithm etc. and their usage in Engineering Problems,
Module IV
Uni-modal vs. Multi-modal problems in Genetic Algorithms and their significance in the context of Process Simulation. Handling of Constraints. The role Pareto-optimal problems in engineering design and their solution strategies based upon Genetic Algorithms.
Module V
Introduction to genetics - based machine learning classifier system, Rule and Message system, Genetic Algorithms and parallel processors
Module VI
Introduction to Swarm Intelligence (SI) and key principles (e.g.,
self-organization,stigmergy), natural and artificial examples, computational and embedded
SI.Foraging,trail laying/following mechanisms.Open-space, multi-source foraging experiments:biological data and microscopic models.
Module VII
From real to virtual ants: Ant System (AS), the first combinatorial optimizationalgorithm based on ant trail/following principles. Application to a classical operationalresearch problem: the Travel Salesman Problem (TSP). From AS to Ant ColonyOptimization (ACO).Ant based algorithmsapplied to engineering problems.
Text Book:
1. David E.Goldberg-Genetic Algorithms in search , Optimization & Machine Learning, Pearson Education-1989.
2. Swarm Intelligence: From Natural to Artificial Systems", E. Bonabeau, M.
Dorigo, and G. Theraulaz .Oxford University Press, 1999 Reference Books:
1. William B. Langdon, Riccardo Poli-Foundations of Genetic Programming 2. P. J. Fleming, A. M. S. Zalzala-Genetic Algorithms in Engineering Systems 3. David A. Coley-An Introduction to Genetic Algorithms for Scientists and Engineers
4. C. Eberhart andYuhui Shi. Morgan-Swarm Intelligence,James Kennedy,Russell
Kaufmann Publishers.
IMCA-7023 ARTIFICIAL INTELLIGENCE [3(3-0-0)]
Module I
Overview of Artificial Intelligence: Definition & Importance of AI.
Knowledge: General Concepts : Introduction, Definition and Importance of Knowledge, Knowledge-Based Systems, Representation of Knowledge, Knowledge Organization, Knowledge Manipulation, Acquisition of Knowledge.
Module II
LISP and Other AI Programming Languages : Introduction to LISP : Syntax and Numeric Function, Basic List Manipulation Functions in LISP, Functions, Predicates and Conditionals, Input, Output and Local Variables, Iteration and Recursion, Property Lists and Arrays, Miscellaneous Topics, PROLOG and Other AI Programming Languages.
Module III
Knowledge Representation : Introduction, Syntax and Semantics for Propositional logic, Syntax and Semantics for FOPL, Properties of Wffs, Conversion to Clausal Form, Inference Rules, The Resolution Principle, No deductive Inference Methods, Representations Using Rules.
Module IV
Dealing With Inconsistencies and Uncertainties: Introduction, Truth Maintenance Systems, Default Reasoning and the Closed World Assumption, Predicate Completion and Circumscription, Modal and Temporal Logics.
Probabilistic Reasoning : Introduction, Bayesian Probabilistic Inference, Possible World Representations, Dumpster-Shafer Theory, Ad-Hoc Methods.
Module V
Structured Knowledge: Graphs, Frames and Related Structures : Introduction, Associative Networks, Frame Structures, Conceptual Dependencies and Scripts.
Object-Oriented Representations : Introduction, Overview of Objects, Classes, Messages and Methods, Simulation Example using an OOS Program.
Module VI
Search and Control Strategies: Introduction, Preliminary Concepts, Examples of Search Problems, Uninformed or Blind Search, Informed Search, Searching And-Or Graphs.
Matching Techniques : Introduction, Structures Used in Matching, Measures for Matching, Matching Like Patterns, Partial Matching.
Module VII
Knowledge Organization and Management: Introduction, Indexing and Retrieval Techniques, Integrating Knowledge in Memory, Memory Organization Systems.
Expert Systems Architectures : Introduction, Rule Based System Architecture, Non-Production System Architecture, Dealing with uncertainty, Knowledge Acquisition and Validation, Knowledge System Building Tools.
Text Book:
Dan W. Patterson - Introduction to Artificial Intelligence and Expert Systems, PHI, New Delhi- 2006.
Reference Books:
1. E. Rich & K. Knight - Artificial Intelligence, 2nd Edition , TATA MCGRAW
HILL , New
Delhi- 2005.
2. P.H. Winston - Artificial Intelligence, 3/e, Pearson Edition, New Delhi-2006.
3. D.W. Rolston,- Principles of AI & Expert System Development, TATA
MCGRAW HILL ,
New Delhi
IMCA-7025 SYSTEM SIMULATION & MODELING [3(3-0-0)]
Module I
The concept of a system, System Environment, stochastic Activities, continuous and discrete systems. System Modeling, Types of Modeling, Types, System Studies:
Subsystem, A corporate Model, Environment segment, Production Segment, management Segment, full Corporate Model, Types of System study , System Analysis, System
Exponential Growth & decay models, modified exponential growth models, logistic curves, generalization of growth models, system dynamics diagrams, simple system dynamics diagrams, multi segment models, representation of time delays.
Module IV
Stochastic variables, discrete & continuous probability functions, measure of probability functions, numerical evaluation of continuous probability functions, continuous uniformly distributed random numbers, a uniform random numbers generating discrete distributions, non- uniform continuously distributed random numbers, the rejection method. Random numbers Generators: Technique for generating random numbers, Test for random numbers. Random vitiate Generation: Inverse transform technique, exponential distribution, uniform distribution.
Module V
Congestion in systems, arrival patterns, Poisson arrival patterns, the exponential distribution, the Erlang distribution, service times, normal distributions, queuing disciplines, measures of queues.
Module VI
Discrete events, representation of time, generation of arrival patterns, simulation of a telephone system, delayed calls, simulation programming tasks, measuring utilization and occupancy.
Module VII
Introduction to GPSS: GPSS programs, general description , action times, succession of events, choice of paths, simulation of a Manufacturing shop , cafilities and storage, gathering statics, conditional transfers, Introducttion to Simscript: Simscript programs, Simscript System Concepts, Simscript Program.
Text Books:
1. Geoffrey Gordon- System Simulation, 2nd edition. Pearson Education- 2007.
2. J.Banks, J.S. Carson, Bl Nelson, D.M. Nicol- Discrite- Event System Simulation.
4th Edition,
Pearson Education-2007.
IMCA-7027 VIRTUAL REALITY AND MODELING [ 3(3-0-0)]
Module I
Introduction, What Is Virtual Reality, Goals and Applications of Virtual Reality ,Two Pillars of VR: Presence and 3D Multimodal Interaction , Building a Virtual Reality System
Module II
Object Modeling, Scene Construction , Object Placement , Multiple Frames of Reference , Re-Expressing Coordinates ,Function and Behavior Modeling
Module III
Performance Estimation and System, Tuning with LOD Models, Presence/Special Effects, Using Images and Textures.
Module IV
Creating the Virtual Reality ,Output Display, The Human Visual System, Human Depth Perception and Stereoscopy, Visual Display System, Human Aural System, Aural Display Systems, Haptics, Stimulation of Other Modalities ,Sensors and Input, Trackers, Event Generators, Sensor Errors and Calibration
Module V
3D Multimodal Interaction Design, 3D Multimodal, Structured Approach to Interaction/Interface, Metaphors , Interface Design, Multimodality
Modules VI+VII
Handling Collision, Collision Detection with Line Segments, Collision Among Polygonal Models, Bounding Volumes , Building a Bounding Volume, Bounding a Volume Hierarchy, Collision Among Bounding Volumes, Virtual Characters , Form of a Character , Motion Control, Forward Kinematics, Inverse Kinematics
Text Books:
1. Kim, G. J.- Designing Virtual Reality Systems The Structured Approach, First
Edition, Springer
Publications
2. Andleig P. K., Thakrar K.- Multimedia Systems Design, Prentice Hall of India, New
Delhi, India
2004