UNIT 1
Digital Image Formation and low-level processing: Overview and State-of-the-art, Fundamentals of Image Formation, Transformation: Orthogonal, Euclidean, Affine, Projective, etc. Fourier Transform, Convolution and Filtering, Image Enhancement, Restoration, Histogram Processing.
UNIT 2
Depth estimation and Multi-camera views, Multiple View Geometry Perspective, Binocular Stereopsis: Camera and Epipolar Geometry; Homography, Rectification, DLT, RANSAC, 3-D reconstruction framework; Auto-calibration.
UNIT 3
Feature Extraction Edges - Canny, LOG, DOG; Line detectors (Hough Transform), Corners - Harris and Hessian Affine, Orientation Histogram, SIFT, SURF, HOG, GLOH, Scale-Space AnalysisImage Pyramids and Gaussian derivative filters, Gabor Filters and DWT.
UNIT 4
Image Segmentation Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift, MRFs, Texture Segmentation; Object detection.
Pattern Analysis Clustering: K-Means, K-Medoids , Mixture of Gaussians, Classification: Discriminant Function, Supervised, Un-supervised, Semi-supervised; Classifiers: Bayes, KNN, ANN models; Dimensionality Reduction: PCA, LDA, ICA; Non-parametric methods.
UNIT 5
Motion Analysis Background Subtraction and Modeling, Optical Flow, KLT, Spatio-Temporal Analysis, Dynamic Stereo; Motion parameter estimation.
Shape from X Photometric Stereo; Use of Surface Smoothness Constraint; Shape from Texture, color, motion and edges.
Digital Image Processing using MATLAB, By: Rafael C. Gonzalez, Richard Eugene Woods, 2nd Edition, Tata McGraw-Hill Education 2010
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011.
Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003.
UNIT 1
UNIT 2
UNIT 3
UNIT 4
UNIT 5
Manning, Raghavan and Schutze, Introduction to Information Retrieval, Cambridge University Press.
Baeza-Yates and Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley.
Soumen Charabarti, Mining the Web, Morgan-Kaufmann.
Survey by Ed Greengrass available in the Internet.
Passwords, security questions, challenge-response, Cryptographic hash functions, Biometrics, Phishing
Web security model, Web authentication and session management, Cross-site request forgery, SQL injection, cross-site scripting, Logic flaws in Web applications, Clickjacking
Online tracking, Symmetric encryption, Kerberos, Memory corruption attacks and defenses, Viruses and rootkits.
Spam, Attacks on TCP/IP, DNS, BGP. Denial of service, Worms and botnets, Advance Persistent Threats
Firewall and intrusion detection, Public Key Cryptography, SSL and certificates, Anonymity networks, Side channel attacks: acoustics and reflections Security Engineering by Anderson Network Security (2nd edition) by Kaufman, Perlman, and Speciner -- required textbook!
The Art of Intrusion by Mitnick and Simon
Network Security Essentials by Stallings
Secure Programming for Unix and Linux HOWTO by Wheeler
The Shellcoder's Handbook by Koziol et al.
Introduction to IoT Defining IoT, Characteristics of IoT, Physical design of IoT, Logical design of IoT, Functional blocks of IoT, Communication models and APIs IoT and M2M, Difference between IoT and M2M, Software define Network.
Network and Communication aspects: Wireless medium access issues, MAC protocol survey, Survey routing protocols, Sensor deployment, Node discovery, Data aggregation and Dissemination
Challenges in IoT Design: challenges, Development challenges, Security challenges, Other Challenges Domain specific applications: IoT Home automation, Industry applications, Surveillance applications, Other IoT applications
Developing IoTs: Introduction to Python, Introduction to different IoT tools, Developing applications through IoT tools, Developing sensor based application through embedded system platform, Implementing IoT concepts with python
PRIVACY PRESERVATION AND TRUST MODELS FOR IOT : Concerns in data
dissemination – Lightweight and robust schemes for Privacy protection – Trust and Trust models for IoT – self-organizing Things - Preventing unauthorized access.
CLOUD SECURITY FOR IOT : Cloud services and IoT – offerings related to IoT from cloud service providers – Cloud IoT security controls – An enterprise IoT cloud security architecture – New directions in cloud enabled IoT computing.
Practical Internet of Things Security (Kindle Edition) by Brian Russell, Drew Van Duren
Securing the Internet of Things Elsevier
Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and Implementations
Pattern recognition fundamentals: Basic concepts of pattern recognition, fundamental problems in pattern recognition system, design concepts and methodologies, example of automatic pattern recognition systems, a simple automatic pattern recognition model.
Bayesian decision theory: Minimum-error-rate classification, Classifiers, Discriminant functions, Decision surfaces, Normal density and discriminant functions, Discrete features, Missing and noisy features, Bayesian networks (Graphical models) and inferencing.
Maximum-likelihood and Bayesian parameter estimation: Maximum-Likelihood estimation: Gaussian case, Maximum a Posteriori estimation, Bayesian estimation: Gaussian case, Problems of dimensionality, Dimensionality reduction: Fisher discriminant analysis, PCA ExpectationMaximization method: Missing features
Sequential Models: State Space, Hidden Markov models, Dynamic Bayesian, Non-parametric techniques for density estimation: Parzen-window method, K-Nearest Neighbour method Linear discriminant functions: Gradient descent procedures, Perceptron criterion function, Minimum- squared-error procedures, Ho-Kashyap procedures, Support vector machines
Unsupervised learning and clustering: Unsupervised maximum-likelihood estimates, Unsupervised Bayesian learning, Criterion functions for clustering, Algorithms for clustering: Kmeans, Hierarchical and other methods, Cluster validation, Low-dimensional representation and multidimensional scaling (MDS).
Pattern Recognition principles: Julus T. Tou and Rafel C. Gonzalez, Addision –Wesley.
Pattern recognition and machine learning, Christopher M. Bishop, Springer 2006.
A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi, Springer, 1996. 4. Pattern classification, Richard O. Duda, Peter E. Hart and David G. Stork, Wiley, 2001.
5. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley
Rajiv Gandhi ProudyogikiVishwavidyalaya Bhopal M.Tech (Artificial Intelligence & Data Science) Third Semester Syllabus
Introduction to bioinformatics, Proteomics, Strategies for Protein Separation, Secondary structure and Tertiary structure, Strategies for Protein Identification, Quantitation, Structural Proteomics, Protein Chips, Methods of Protein Engineering.
Introduction to Molecular biology, Molecular Dynamics, Monte Carlo and Molecular Dynamics in Various Ensembles. System biology, biological sequences, patterns in biological sequences, genetic, genetic alterations and genomics, Engineering of Macromolecules.
DNA, RNA, Application of Recombinant DNA Technology.DNA re-association kinetics, repetitive and unique sequences, kinetics and sequence complexities, DNA polymorphism, nucleotides, DNA sequences, DNA engineering.
Biological database, DNA and protein database, DNA Data Bank of Japan (DDBJ), DHCP database.
Applications of Bioinformatics in molecular medicine, personalized medicine, preventative medicine, gene therapy, agriculture, animal, waste cleanup etc. case studies.
Bryan Bergeron M.D., Bioinformatics Computing, Pearson publication.
Hancock J M, Bioinformatics and Computational Biology, Second Edition, Wiley publication.
David Mount, Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, Second Edition.
Vince Buffalo, Bioinformatics Data Skills, O’Reilly publication.
Introduction to Social Media and Social Networks, Social Media: New Technologies of Collaboration, Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections
Getting Started with NodeXL, Layout, Visual Design, and Labeling, Calculating and Visualizing Network Metrics, Preparing Data and Filtering, Clustering and Grouping
Email: The Lifeblood of Modern Communication, Thread Networks: Mapping Message Boards and Email Lists, Twitter: Conversation, Entertainment, and Information, All in One Network, WWW Hyperlink Networks
Visualizing and Interpreting Facebook Networks, Photos: Linking People, Photos, and Tags, YouTube: Contrasting Patterns of Content, Interaction, and Prominence, Wiki Networks: Connections of Creativity and Collaboration
Social Media Network Analysis Case Studies: Email, YouTube, Facebook, Twitter, Photos, WWW, WhatsApp.
Derek Hansen Ben Shneiderman Marc Smith: Analyzing Social Media Networks with NodeXL, Elsevier, 1th edition. 2010
David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World., Cambridge University Press, 2010.
Mark Newman, Networks: An Introduction., Oxford University Press, 2010.
Avinash Kaushik., Web Analytics 2.0: The Art of Online Accounta-bility, Sybex, 2009.
UNIT 1
Discrete optimization models – Linear optimization, geometric and algebraic solutions, integer programs and combinatorial optimization, binary decisions
UNIT 2
Network models – Graphs and networks, network flows, assignment problems, graph coloring, vertex covers, local search algorithms
Discrete probabilistic models – Finite discrete time Markov chains and stationary distribution, component and system reliability
UNIT 3
System Dynamics & Probability concepts in Simulation: Exponential growth and decay models, logistic curves, Generalization of growth models, System dynamics diagrams, Multi segment models, Representation of Time Delays. Discrete and Continuous probability functions, Continuous Uniformly Distributed Random Numbers, Generation of a Random numbers, Generating Discrete distributions, Non-Uniform Continuously Distributed Random Numbers, Rejection Method.
UNIT 4
Simulation of Queueing Systems and Discrete System Simulation: Poisson arrival patterns, Exponential distribution, Service times, Normal Distribution Queuing Disciplines, Simulation of single and two server queues. Application of queuing theory in computer system Discrete Events, Generation of arrival patterns, Simulation programming tasks, Gathering statistics, Measuring occupancy and Utilization, Recording Distributions and Transit times.
UNIT 5
Introduction to Simulation languages and Analysis of Simulation output: GPSS: Action times, Succession of events, Choice of paths, Conditional transfers, program control statements, SIMSCRIPT: Organization of SIMSCRIPT Program, Names & Labels, SIMSCRIPT statements. Estimation methods, Relication of Runs, Batch Means, Regenerative techniques, Time Series Analysis, Spectral Analysis and Autoregressive Processes.
Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition, Cengage.
Gorden G., System simulation, Prentice Hall.
Seila, Simulation Modeling, Cengage Learning
Law .,Simulation Modeling And Analysis, McGraw Hill
Deo, System Simulation with Digital Computer, PHI
Harrington, Simulation Modeling methods, McGraw Hill
Severance, “ System Modeling & Simulation, Willey Pub
UNIT 1
Digital Image Formation and low-level processing: Overview and State-of-the-art, Fundamentals of Image Formation, Transformation: Orthogonal, Euclidean, Affine, Projective, etc. Fourier Transform, Convolution and Filtering, Image Enhancement, Restoration, Histogram Processing.
UNIT 2
Depth estimation and Multi-camera views, Multiple View Geometry Perspective, Binocular Stereopsis: Camera and Epipolar Geometry; Homography, Rectification, DLT, RANSAC, 3-D reconstruction framework; Auto-calibration.
UNIT 3
Feature Extraction Edges - Canny, LOG, DOG; Line detectors (Hough Transform), Corners - Harris and Hessian Affine, Orientation Histogram, SIFT, SURF, HOG, GLOH, Scale-Space AnalysisImage Pyramids and Gaussian derivative filters, Gabor Filters and DWT.
UNIT 4
Image Segmentation Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift, MRFs, Texture Segmentation; Object detection.
Pattern Analysis Clustering: K-Means, K-Medoids , Mixture of Gaussians, Classification: Discriminant Function, Supervised, Un-supervised, Semi-supervised; Classifiers: Bayes, KNN, ANN models; Dimensionality Reduction: PCA, LDA, ICA; Non-parametric methods.
UNIT 5
Motion Analysis Background Subtraction and Modeling, Optical Flow, KLT, Spatio-Temporal Analysis, Dynamic Stereo; Motion parameter estimation.
Shape from X Photometric Stereo; Use of Surface Smoothness Constraint; Shape from Texture, color, motion and edges.
Digital Image Processing using MATLAB, By: Rafael C. Gonzalez, Richard Eugene Woods, 2nd Edition, Tata McGraw-Hill Education 2010
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011.
Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003.
UNIT 1
UNIT 2
UNIT 3
UNIT 4
UNIT 5
Manning, Raghavan and Schutze, Introduction to Information Retrieval, Cambridge University Press.
Baeza-Yates and Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley.
Soumen Charabarti, Mining the Web, Morgan-Kaufmann.
Survey by Ed Greengrass available in the Internet.
Passwords, security questions, challenge-response, Cryptographic hash functions, Biometrics, Phishing
Web security model, Web authentication and session management, Cross-site request forgery, SQL injection, cross-site scripting, Logic flaws in Web applications, Clickjacking
Online tracking, Symmetric encryption, Kerberos, Memory corruption attacks and defenses, Viruses and rootkits.
Spam, Attacks on TCP/IP, DNS, BGP. Denial of service, Worms and botnets, Advance Persistent Threats
Firewall and intrusion detection, Public Key Cryptography, SSL and certificates, Anonymity networks, Side channel attacks: acoustics and reflections Security Engineering by Anderson Network Security (2nd edition) by Kaufman, Perlman, and Speciner -- required textbook!
The Art of Intrusion by Mitnick and Simon
Network Security Essentials by Stallings
Secure Programming for Unix and Linux HOWTO by Wheeler
The Shellcoder's Handbook by Koziol et al.
Introduction to IoT Defining IoT, Characteristics of IoT, Physical design of IoT, Logical design of IoT, Functional blocks of IoT, Communication models and APIs IoT and M2M, Difference between IoT and M2M, Software define Network.
Network and Communication aspects: Wireless medium access issues, MAC protocol survey, Survey routing protocols, Sensor deployment, Node discovery, Data aggregation and Dissemination
Challenges in IoT Design: challenges, Development challenges, Security challenges, Other Challenges Domain specific applications: IoT Home automation, Industry applications, Surveillance applications, Other IoT applications
Developing IoTs: Introduction to Python, Introduction to different IoT tools, Developing applications through IoT tools, Developing sensor based application through embedded system platform, Implementing IoT concepts with python
PRIVACY PRESERVATION AND TRUST MODELS FOR IOT : Concerns in data
dissemination – Lightweight and robust schemes for Privacy protection – Trust and Trust models for IoT – self-organizing Things - Preventing unauthorized access.
CLOUD SECURITY FOR IOT : Cloud services and IoT – offerings related to IoT from cloud service providers – Cloud IoT security controls – An enterprise IoT cloud security architecture – New directions in cloud enabled IoT computing.
Practical Internet of Things Security (Kindle Edition) by Brian Russell, Drew Van Duren
Securing the Internet of Things Elsevier
Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and Implementations
Pattern recognition fundamentals: Basic concepts of pattern recognition, fundamental problems in pattern recognition system, design concepts and methodologies, example of automatic pattern recognition systems, a simple automatic pattern recognition model.
Bayesian decision theory: Minimum-error-rate classification, Classifiers, Discriminant functions, Decision surfaces, Normal density and discriminant functions, Discrete features, Missing and noisy features, Bayesian networks (Graphical models) and inferencing.
Maximum-likelihood and Bayesian parameter estimation: Maximum-Likelihood estimation: Gaussian case, Maximum a Posteriori estimation, Bayesian estimation: Gaussian case, Problems of dimensionality, Dimensionality reduction: Fisher discriminant analysis, PCA ExpectationMaximization method: Missing features
Sequential Models: State Space, Hidden Markov models, Dynamic Bayesian, Non-parametric techniques for density estimation: Parzen-window method, K-Nearest Neighbour method Linear discriminant functions: Gradient descent procedures, Perceptron criterion function, Minimum- squared-error procedures, Ho-Kashyap procedures, Support vector machines
Unsupervised learning and clustering: Unsupervised maximum-likelihood estimates, Unsupervised Bayesian learning, Criterion functions for clustering, Algorithms for clustering: Kmeans, Hierarchical and other methods, Cluster validation, Low-dimensional representation and multidimensional scaling (MDS).
Pattern Recognition principles: Julus T. Tou and Rafel C. Gonzalez, Addision –Wesley.
Pattern recognition and machine learning, Christopher M. Bishop, Springer 2006.
A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi, Springer, 1996. 4. Pattern classification, Richard O. Duda, Peter E. Hart and David G. Stork, Wiley, 2001.
5. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley
Rajiv Gandhi ProudyogikiVishwavidyalaya Bhopal M.Tech (Artificial Intelligence & Data Science) Third Semester Syllabus
Introduction to bioinformatics, Proteomics, Strategies for Protein Separation, Secondary structure and Tertiary structure, Strategies for Protein Identification, Quantitation, Structural Proteomics, Protein Chips, Methods of Protein Engineering.
Introduction to Molecular biology, Molecular Dynamics, Monte Carlo and Molecular Dynamics in Various Ensembles. System biology, biological sequences, patterns in biological sequences, genetic, genetic alterations and genomics, Engineering of Macromolecules.
DNA, RNA, Application of Recombinant DNA Technology.DNA re-association kinetics, repetitive and unique sequences, kinetics and sequence complexities, DNA polymorphism, nucleotides, DNA sequences, DNA engineering.
Biological database, DNA and protein database, DNA Data Bank of Japan (DDBJ), DHCP database.
Applications of Bioinformatics in molecular medicine, personalized medicine, preventative medicine, gene therapy, agriculture, animal, waste cleanup etc. case studies.
Bryan Bergeron M.D., Bioinformatics Computing, Pearson publication.
Hancock J M, Bioinformatics and Computational Biology, Second Edition, Wiley publication.
David Mount, Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, Second Edition.
Vince Buffalo, Bioinformatics Data Skills, O’Reilly publication.
Introduction to Social Media and Social Networks, Social Media: New Technologies of Collaboration, Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections
Getting Started with NodeXL, Layout, Visual Design, and Labeling, Calculating and Visualizing Network Metrics, Preparing Data and Filtering, Clustering and Grouping
Email: The Lifeblood of Modern Communication, Thread Networks: Mapping Message Boards and Email Lists, Twitter: Conversation, Entertainment, and Information, All in One Network, WWW Hyperlink Networks
Visualizing and Interpreting Facebook Networks, Photos: Linking People, Photos, and Tags, YouTube: Contrasting Patterns of Content, Interaction, and Prominence, Wiki Networks: Connections of Creativity and Collaboration
Social Media Network Analysis Case Studies: Email, YouTube, Facebook, Twitter, Photos, WWW, WhatsApp.
Derek Hansen Ben Shneiderman Marc Smith: Analyzing Social Media Networks with NodeXL, Elsevier, 1th edition. 2010
David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World., Cambridge University Press, 2010.
Mark Newman, Networks: An Introduction., Oxford University Press, 2010.
Avinash Kaushik., Web Analytics 2.0: The Art of Online Accounta-bility, Sybex, 2009.
UNIT 1
Discrete optimization models – Linear optimization, geometric and algebraic solutions, integer programs and combinatorial optimization, binary decisions
UNIT 2
Network models – Graphs and networks, network flows, assignment problems, graph coloring, vertex covers, local search algorithms
Discrete probabilistic models – Finite discrete time Markov chains and stationary distribution, component and system reliability
UNIT 3
System Dynamics & Probability concepts in Simulation: Exponential growth and decay models, logistic curves, Generalization of growth models, System dynamics diagrams, Multi segment models, Representation of Time Delays. Discrete and Continuous probability functions, Continuous Uniformly Distributed Random Numbers, Generation of a Random numbers, Generating Discrete distributions, Non-Uniform Continuously Distributed Random Numbers, Rejection Method.
UNIT 4
Simulation of Queueing Systems and Discrete System Simulation: Poisson arrival patterns, Exponential distribution, Service times, Normal Distribution Queuing Disciplines, Simulation of single and two server queues. Application of queuing theory in computer system Discrete Events, Generation of arrival patterns, Simulation programming tasks, Gathering statistics, Measuring occupancy and Utilization, Recording Distributions and Transit times.
UNIT 5
Introduction to Simulation languages and Analysis of Simulation output: GPSS: Action times, Succession of events, Choice of paths, Conditional transfers, program control statements, SIMSCRIPT: Organization of SIMSCRIPT Program, Names & Labels, SIMSCRIPT statements. Estimation methods, Relication of Runs, Batch Means, Regenerative techniques, Time Series Analysis, Spectral Analysis and Autoregressive Processes.
Giordano, Fox, Horton, A First Course in Mathematical Modeling, 5th edition, Cengage.
Gorden G., System simulation, Prentice Hall.
Seila, Simulation Modeling, Cengage Learning
Law .,Simulation Modeling And Analysis, McGraw Hill
Deo, System Simulation with Digital Computer, PHI
Harrington, Simulation Modeling methods, McGraw Hill
Severance, “ System Modeling & Simulation, Willey Pub