New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to familiarize the students with the fundamentals of information security and the methods used in protecting both the information present in computer storage as well as information traveling over computer networks.
William Stallings, "Cryptography and Network Security", Fourth edition, PHI
Atul Kahate, “Cryptography and Network Security”, McGraw Hill.
V.K. Pachghare, “Cryptography and Information Security”, PHI Learning
Nina Godbole, “Information System Security”, Wiley
After the completion of this course, the students will be able to:
Understand key terms and concepts in information security and Cryptography and evaluate the cyber security needs of an organization.
Acquire knowledge to secure computer systems, protect personal data, and secure computer networks in an organization
Apply knowledge of various encryption algorithms and authentication mechanisms to secure information in computer systems and networks
Understand principles of web security to secure network by monitoring and analyzing the nature of attacks and design/develop security architecture for an organization.
Design operational and strategic information security strategies and policies.
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To familiarize students with the knowledge of machine learning and enable them to apply suitable machine learning techniques for data handling and to gain knowledge from it. Evaluate the performance of algorithms and to provide solution for various real-world applications.
Introduction, Examples of various Learning Paradigms, Perspectives and Issues, Concept Learning, Version Spaces, Finite and Infinite Hypothesis Spaces, PAC Learning, VC Dimension
Learning a Class from Examples, Linear, Non-linear, Multi-class and Multi-label classification, Decision Trees: ID3, Classification and Regression Trees (CART), Regression: Linear Regression, Multiple Linear Regression, Logistic Regression, Neural Networks: Introduction, Perceptron, Multilayer Perceptron, Support vector machines: Linear and NonLinear, Kernel Functions, K-Nearest Neighbors
Ensemble Learning Model Combination Schemes, Voting, Error-Correcting Output Codes, Bagging: Random Forest Trees, Boosting: Adaboost, Stacking
Introduction to clustering, Hierarchical: AGNES, DIANA, Partitional: K-means clustering, K- Mode Clustering, Self-Organizing Map, Expectation Maximization, Gaussian Mixture Models, Principal Component Analysis (PCA), Locally Linear Embedding (LLE), Factor Analysis
Bayesian Learning, Bayes Optimal Classifier, Naïve Bayes Classifier, Bayesian Belief Networks, Mining Frequent Patterns
EthemAlpaydin,"Introduction to Machine Learning”, MIT Press, Prentice Hall of India, Third Edition 2014.
MehryarMohri, AfshinRostamizadeh, AmeetTalwalkar "Foundations of Machine Learning”, MIT Press, 2012.
Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition,1997.
Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014.
Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, 2nd Edition, CRC Press, 2015.
Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press, 2012
Jiawei Han and MichelineKambers and Jian Pei, “Data Mining –Concepts and Techniques”, 3rd Edition,Morgan Kaufman Publications, 2012.
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, “Mathematics for Machine Learning”, Cambridge University Press, 2019.
After the completion of this course, the students will be able to:
Recognize the characteristics of machine learning strategies.
Apply various supervised learning methods to appropriate problems.
Identify and integrate more than one technique to enhance the performance of learning.
Create probabilistic and unsupervised learning models for handling unknown pattern.
Analyze the co-occurrence of data to find interesting frequent patterns and Preprocess the data before applying to any real-world problem and can evaluate its performance
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To provide a broad introduction to NLP with a particular emphasis on core algorithms, data structures, and machine learning for NLP.
Introduction to various levels of natural language processing, Ambiguities and computational challenges in processing various natural languages. Introduction to Real life applications of NLP such as spell and grammar checkers, information extraction, question answering, and machine translation
Character Encoding, Word Segmentation, Sentence Segmentation, Introduction to Corpora, Corpora Analysis
Inflectional and Derivation Morphology, Morphological Analysis and Generation using finite state transducers
Introduction to word types, POS Tagging, Maximum Entropy Models for POS tagging, Multi- word Expressions.
The role of language models. Simple N-gram models. Estimating parameters and smoothing. Evaluating language models.
Introduction to phrases, clauses and sentence structure, Shallow Parsing and Chunking, Shallow Parsing with Conditional Random Fields (CRF), Lexical Semantics, Word Sense Disambiguation, WordNet, Thematic Roles, Semantic Role Labelling with CRFs.
NL Interfaces, Text Summarization, Sentiment Analysis, Machine Translation, Question answering, Recent Trends in NLP
J. H. Speech and Language Processing, Jurafsky, D. and Martin, Prentice Hall, 2nd Edition, 2014
C. D. and H. Schütze: Foundations of Statistical Natural Language Processing, Manning, The MIT Press
After the completion of this course, the students will be able to:
Identify and discuss the characteristics of different NLP techniques
Understand the fundamental mathematical models and algorithms in the field of NLP and apply these mathematical models and algorithms in applications in software design and implementation for NLP
Understand the complexity of speech and the challenges facing speech engineers
Understand approaches to syntax and semantics in NLP
Understand approaches to discourse, generation, dialogue and summarization within NLP
New Scheme Based On AICTE Flexible Curricula
The objective of this course is to impart knowledge about industrial robots for their control and design.
Types and components of a robot, Classification of robots, closed-loop and open-loop control systems;
Kinematics systems: Definition of mechanisms and manipulators, Social issues and safety
Kinematic Modelling: Translation and Rotation Representation, Coordinate transformation, DH parameters, Jacobian, Singularity, and Statics;
Dynamic Modelling: Equations of motion: Euler-Lagrange formulation
Sensor: Contact and Proximity, Position, Velocity, Force, Tactile etc.
Introduction to Cameras, Camera calibration, Geometry of Image formation, Euclidean/Similarity/Affine/Projective transformations, Vision applications in robotics.
Basics of control: Transfer functions, Control laws: P, PD, PID, Non-linear and advanced controls Robot Actuation Systems: Actuators: Electric, Hydraulic and Pneumatic; Transmission: Gears, Timing Belts and Bearings, Parameters for selection of actuators.
Embedded systems: Architecture and integration with sensors, actuators, components, Programming for Robot Applications
Saha, S.K., “Introduction to Robotics, 2nd Edition, McGraw-Hill Higher Education, New Delhi, 2014.
Ghosal, A., “Robotics”, Oxford, New Delhi, 2006.
Niku Saeed B., “Introduction to Robotics: Analysis, Systems, Applications”, PHI, New Delhi.
Mittal R.K. and Nagrath I.J., “Robotics and Control”, Tata McGraw Hill.
Mukherjee S., “Robotics and Automation”, Khanna Publishing House, Delhi.
Craig, J.J., “Introduction to Robotics: Mechanics and Control”, Pearson, New Delhi, 2009
Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, “Robot Modelling and Control”, John Wiley and Sons Inc, 2005
Steve Heath, “Embedded System Design”, 2nd Edition, Newnes, Burlington, 2003
Merzouki R., Samantaray A.K., Phathak P.M. and Bouamama B. Ould, “Intelligent Mechatronic System: Modeling, Control and Diagnosis”, Springer.
After the completion of this course, the students will be able to:
Understand robot mechanism
Perform kinematic and dynamic analyses with simulation
Design control laws for a robot
Integrate mechanical and electrical hardware for a real prototype of robotic device
Select a robotic system for given application
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to impart necessary knowledge to the learner so that he/she can develop and implement algorithm and write programs using these algorithm
Motivation for studying Quantum Computing , Major players in the industry (IBM, Microsoft, Rigetti, D-Wave etc.), Origin of Quantum Computing
Overview of major concepts in Quantum Computing: Qubits and multi-qubits states, Braket notation, Bloch Sphere representation, Quantum Superposition, Quantum Entanglement
Math Foundation for Quantum Computing: Matrix Algebra: basis vectors and orthogonality, inner product and Hilbert spaces, matrices and tensors, unitary operators and projectors, Dirac notation, Eigen values and Eigen vectors
Building Blocks for Quantum Program: Architecture of a Quantum Computing platform,
Details of q-bit system of information representation: Block Sphere, Multi-qubits States, Quantum superposition of qubits (valid and invalid superposition), Quantum Entanglement, Useful states from quantum algorithmic perceptive e.g. Bell State, Operation on qubits: Measuring and transforming using gates.
Quantum Logic gates and Circuit: Pauli, Hadamard, phase shift, controlled gates, Ising, Deutsch, swap etc.
Programming model for a Quantum Computing Program: Steps performed on classical computer, Steps performed on Quantum Computer, Moving data between bits and qubits.
Basic techniques exploited by quantum algorithms, Amplitude amplification, Quantum Fourier
Transform, Phase Kick-back, Quantum Phase estimation, Quantum Walks
Major Algorithms: Shor’s Algorithm, Grover’s Algorithm, Deutsch’s Algorithm, Deutsch -Jozsa Algorithm OSS Toolkits for implementing Quantum program: IBM quantum experience, Microsoft Q, Rigetti PyQuil (QPU/QVM)
Michael A. Nielsen, “Quantum Computation and Quantum Information”, Cambridge University Press.
David McMahon, “Quantum Computing Explained”, Wiley
After the completion of this course, the students will be able to:
Understand major concepts in Quantum Computing
Explain the working of a Quantum Computing program, its architecture and program model
Develop quantum logic gate circuits
Develop quantum algorithm
Program quantum algorithm on major toolkits
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to provide conceptual understanding of how block chain technology can be used to innovate and improve business processes. The course covers the technological underpinning of block Chain operations in both theoretical and practical implementation of solutions using block Chain technology.
Working with Consensus in Bitcoin: Distributed consensus in open environments, Consensus in a Bitcoin network, Proof of Work (PoW) – basic introduction, HashCash PoW, Bitcoin PoW, Attacks on PoW and the monopoly problem, Proof of Stake, Proof of Burn and Proof of Elapsed Time, The life of a Bitcoin Miner, Mining Difficulty, Mining Pool
Melanie Swan, “Block Chain: Blueprint for a New Economy”, O’Reilly, 2015
Josh Thompsons, “Block Chain: The Block Chain for Beginners- Guide to Block chain Technology and Leveraging Block Chain Programming”
Daniel Drescher, “Block Chain Basics”, Apress; 1stedition, 2017
Anshul Kaushik, “Block Chain and Crypto Currencies”, Khanna Publishing House, Delhi.
Imran Bashir, “Mastering Block Chain: Distributed Ledger Technology, Decentralization and Smart Contracts Explained”, Packt Publishing
Ritesh Modi, “Solidity Programming Essentials: A Beginner’s Guide to Build Smart Contracts for Ethereum and Block Chain”, Packt Publishing
Salman Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, Anthony O’Dowd, Venkatraman Ramakrishna, “Hands-On Block Chain with Hyperledger: Building Decentralized Applications with Hyperledger Fabric and Composer”, Import, 2018
After the completion of this course, the students will be able to:
Understand block chain technology
Acquire knowledge of crytocurrencies
Develop block chain based solutions and write smart contract using Hyperledger Fabric and Ethereum frameworks
Build and deploy block chain application for on premise and cloud based architecture
Integrate ideas from various domains and implement them using block chain technology in different perspectives
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To provide the basic knowledge on the levels of interaction, design models, techniques and validations focusing on the different aspects of human-computer interface and interactions
Input–output channels, Human memory, Thinking: reasoning and problem solving, Emotion, Individual differences, Psychology and the design of interactive systems, Text entry devices, Positioning, pointing and drawing, Display devices, Devices for virtual reality and 3D interaction, Physical controls, sensors and special devices, Paper: printing and scanning
Overview of Interaction Design Models, Discovery - Framework, Collection - Observation, Elicitation, Interpretation - Task Analysis, Storyboarding, Use Cases, Primary Stakeholder Profiles, Project Management Document
Model Human Processor - Working Memory, Long-Term Memory, Processor Timing, Keyboard Level Model - Operators, Encoding Methods, Heuristics for M Operator Placement, What the Keyboard Level Model Does Not Model, Application of the Keyboard Level Model, GOMS - CMN-GOMS Analysis, Modeling Structure, State Transition Networks - Three-State Model, Glimpse Model, Physical Models, Fitts’ Law
Shneideman's eight golden rules, Norman's Sever principles, Norman's model of interaction, Nielsen's ten heuristics, Heuristic evaluation, contextual evaluation, Cognitive walk-through Collaboration and Communication:
Face-to-face Communication, Conversation, Text-based Communication, Group working, Dialog design notations, Diagrammatic notations, Textual dialog notations, Dialog semantics, Dialog analysis and design
Groupware, Meeting and decision support systems, Shared applications and artifacts, Frameworks for groupware Implementing synchronous groupware, Mixed, Augmented and Virtual Reality Validation: Validations - Usability testing, Interface Testing, User Acceptance Testing
A Dix, Janet Finlay, G D Abowd, R Beale., Human-Computer Interaction, 3rd Edition, Pearson Publishers,2008
Shneiderman, Plaisant, Cohen and Jacobs, Designing the User Interface: Strategies for Effective Human Computer Interaction, 5th Edition, Pearson Publishers, 2010.
Hans-Jorg Bullinger,” Human-Computer Interaction”, Lawrence Erlbaum Associates, Publishers
Jakob Nielsen,” Advances in Human-computer Interaction”,Ablex Publishing Corporation
Thomas S. Huang,” Real-Time Vision for Human-Computer Interaction”, Springer
Preece et al, Human-Computer Interaction, Addison-Wesley, 1994
After the completion of this course, the students will be able to:
Enumerate the basic concepts of human, computer interactions
Create the processes of human computer interaction life cycle
Analyze and design the various interaction design models
Apply the interface design standards/guidelines for evaluating the developed interactions
Apply product usability evaluations and testing methods
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To impart knowledge and skills related to 3D printing technologies, selection of material and equipment and develop a product using this technique in Industry 4.0 environment
Introduction, Process, Classification, Advantages, Additive V/s Conventional Manufacturing processes, Applications.
CAD for Additive Manufacturing: CAD Data formats, Data translation, Data loss, STL format.
Stereo- Lithography, LOM, FDM, SLS, SLM, Binder Jet technology. Process, Process parameter, Process Selection for various applications.
Additive Manufacturing Application Domains: Aerospace, Electronics, Health Care, Defence, Automotive, Construction, Food Processing, Machine Tools
Polymers, Metals, Non-Metals, Ceramics
Various forms of raw material- Liquid, Solid, Wire, Powder; Powder Preparation and their desired properties, Polymers and their properties, Support Materials.
Process Equipment- Design and process parameters, Governing Bonding Mechanism, Common faults and troubleshooting, Process Design
Post Processing Requirement and Techniques. Product Quality: Inspection and testing, Defects and their causes
Lan Gibson, David W. Rosen and Brent Stucker, “Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing”, Springer, 2010.
Andreas Gebhardt, “Understanding Additive Manufacturing: Rapid Prototyping, Rapid Tooling, Rapid Manufacturing”, Hanser Publisher, 2011.
Khanna Editorial, “3D Printing and Design”, Khanna Publishing House, Delhi.
CK Chua, Kah Fai Leong, “3D Printing and Rapid Prototyping- Principles and Applications”, World Scientific, 2017.
J.D. Majumdar and I. Manna, “Laser-Assisted Fabrication of Materials”, Springer Series in Material Science, 2013.
L. Lu, J. Fuh and Y.S. Wong, “Laser-Induced Materials and Processes for Rapid Prototyping”, Kulwer Academic Press, 2001.
Zhiqiang Fan And Frank Liou, “Numerical Modelling of the Additive Manufacturing (AM) Processes of Titanium Alloy”, InTech, 2012.
After the completion of this course, the students will be able to:
Develop CAD models for 3D printing.
Import and Export CAD data and generate .stl file.
Select a specific material for the given application.
Select a 3D printing process for an application.
Produce a product using 3D Printing or Additive Manufacturing (AM).
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To develop an understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computers and to develop programming skills to effectively implement parallel architecture
D. E. Culler, J. P. Singh, and A. Gupta, “Parallel Computer Architecture”, MorganKaufmann, 2004
Rajeev Balasubramonian, Norman P. Jouppi, and Naveen Muralimanohar, “Multi-Core Cache Hierarchies”, Morgan & Claypool Publishers, 2011
Peter and Pach Eco, “An Introduction to Parallel Programming”, Elsevier, 2011
James R. Larus and Ravi Rajwar, “Transactional Memory”, Morgan & Claypool Publishers, 2007
David B. Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors: A Hands-on Approach”, 2010
Barbara Chapman, F. Desprez, Gerhard R. Joubert, Alain Lichnewsky, Frans Peters “Parallel Computing: From Multicores and GPU's to Petascale”, 2010
Michael McCool, James Reinders, Arch Robison, “Structured Parallel Programming: Patterns for Efficient Computation”, 2012
Jason Sanders, Edward Kandrot, “CUDA by Example: An Introduction to GeneralPurpose GPU Programming”, 2011
After the completion of this course, the students will be able to:
To develop an understanding of various basic concepts associated with parallel computing environments
Understand, appreciate and apply parallel and distributed algorithms in problem solving
Acquire skills to measure the performance of parallel and distributed programs
Design parallel programs to enhance machine performance in parallel hardware environment
Design and implement parallel programs in modern environments such as CUDA, OpenMP, etc
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to familiarize the students with the fundamentals of information security and the methods used in protecting both the information present in computer storage as well as information traveling over computer networks.
William Stallings, "Cryptography and Network Security", Fourth edition, PHI
Atul Kahate, “Cryptography and Network Security”, McGraw Hill.
V.K. Pachghare, “Cryptography and Information Security”, PHI Learning
Nina Godbole, “Information System Security”, Wiley
After the completion of this course, the students will be able to:
Understand key terms and concepts in information security and Cryptography and evaluate the cyber security needs of an organization.
Acquire knowledge to secure computer systems, protect personal data, and secure computer networks in an organization
Apply knowledge of various encryption algorithms and authentication mechanisms to secure information in computer systems and networks
Understand principles of web security to secure network by monitoring and analyzing the nature of attacks and design/develop security architecture for an organization.
Design operational and strategic information security strategies and policies.
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To familiarize students with the knowledge of machine learning and enable them to apply suitable machine learning techniques for data handling and to gain knowledge from it. Evaluate the performance of algorithms and to provide solution for various real-world applications.
Introduction, Examples of various Learning Paradigms, Perspectives and Issues, Concept Learning, Version Spaces, Finite and Infinite Hypothesis Spaces, PAC Learning, VC Dimension
Learning a Class from Examples, Linear, Non-linear, Multi-class and Multi-label classification, Decision Trees: ID3, Classification and Regression Trees (CART), Regression: Linear Regression, Multiple Linear Regression, Logistic Regression, Neural Networks: Introduction, Perceptron, Multilayer Perceptron, Support vector machines: Linear and NonLinear, Kernel Functions, K-Nearest Neighbors
Ensemble Learning Model Combination Schemes, Voting, Error-Correcting Output Codes, Bagging: Random Forest Trees, Boosting: Adaboost, Stacking
Introduction to clustering, Hierarchical: AGNES, DIANA, Partitional: K-means clustering, K- Mode Clustering, Self-Organizing Map, Expectation Maximization, Gaussian Mixture Models, Principal Component Analysis (PCA), Locally Linear Embedding (LLE), Factor Analysis
Bayesian Learning, Bayes Optimal Classifier, Naïve Bayes Classifier, Bayesian Belief Networks, Mining Frequent Patterns
EthemAlpaydin,"Introduction to Machine Learning”, MIT Press, Prentice Hall of India, Third Edition 2014.
MehryarMohri, AfshinRostamizadeh, AmeetTalwalkar "Foundations of Machine Learning”, MIT Press, 2012.
Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition,1997.
Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014.
Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, 2nd Edition, CRC Press, 2015.
Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press, 2012
Jiawei Han and MichelineKambers and Jian Pei, “Data Mining –Concepts and Techniques”, 3rd Edition,Morgan Kaufman Publications, 2012.
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, “Mathematics for Machine Learning”, Cambridge University Press, 2019.
After the completion of this course, the students will be able to:
Recognize the characteristics of machine learning strategies.
Apply various supervised learning methods to appropriate problems.
Identify and integrate more than one technique to enhance the performance of learning.
Create probabilistic and unsupervised learning models for handling unknown pattern.
Analyze the co-occurrence of data to find interesting frequent patterns and Preprocess the data before applying to any real-world problem and can evaluate its performance
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To provide a broad introduction to NLP with a particular emphasis on core algorithms, data structures, and machine learning for NLP.
Introduction to various levels of natural language processing, Ambiguities and computational challenges in processing various natural languages. Introduction to Real life applications of NLP such as spell and grammar checkers, information extraction, question answering, and machine translation
Character Encoding, Word Segmentation, Sentence Segmentation, Introduction to Corpora, Corpora Analysis
Inflectional and Derivation Morphology, Morphological Analysis and Generation using finite state transducers
Introduction to word types, POS Tagging, Maximum Entropy Models for POS tagging, Multi- word Expressions.
The role of language models. Simple N-gram models. Estimating parameters and smoothing. Evaluating language models.
Introduction to phrases, clauses and sentence structure, Shallow Parsing and Chunking, Shallow Parsing with Conditional Random Fields (CRF), Lexical Semantics, Word Sense Disambiguation, WordNet, Thematic Roles, Semantic Role Labelling with CRFs.
NL Interfaces, Text Summarization, Sentiment Analysis, Machine Translation, Question answering, Recent Trends in NLP
J. H. Speech and Language Processing, Jurafsky, D. and Martin, Prentice Hall, 2nd Edition, 2014
C. D. and H. Schütze: Foundations of Statistical Natural Language Processing, Manning, The MIT Press
After the completion of this course, the students will be able to:
Identify and discuss the characteristics of different NLP techniques
Understand the fundamental mathematical models and algorithms in the field of NLP and apply these mathematical models and algorithms in applications in software design and implementation for NLP
Understand the complexity of speech and the challenges facing speech engineers
Understand approaches to syntax and semantics in NLP
Understand approaches to discourse, generation, dialogue and summarization within NLP
New Scheme Based On AICTE Flexible Curricula
The objective of this course is to impart knowledge about industrial robots for their control and design.
Types and components of a robot, Classification of robots, closed-loop and open-loop control systems;
Kinematics systems: Definition of mechanisms and manipulators, Social issues and safety
Kinematic Modelling: Translation and Rotation Representation, Coordinate transformation, DH parameters, Jacobian, Singularity, and Statics;
Dynamic Modelling: Equations of motion: Euler-Lagrange formulation
Sensor: Contact and Proximity, Position, Velocity, Force, Tactile etc.
Introduction to Cameras, Camera calibration, Geometry of Image formation, Euclidean/Similarity/Affine/Projective transformations, Vision applications in robotics.
Basics of control: Transfer functions, Control laws: P, PD, PID, Non-linear and advanced controls Robot Actuation Systems: Actuators: Electric, Hydraulic and Pneumatic; Transmission: Gears, Timing Belts and Bearings, Parameters for selection of actuators.
Embedded systems: Architecture and integration with sensors, actuators, components, Programming for Robot Applications
Saha, S.K., “Introduction to Robotics, 2nd Edition, McGraw-Hill Higher Education, New Delhi, 2014.
Ghosal, A., “Robotics”, Oxford, New Delhi, 2006.
Niku Saeed B., “Introduction to Robotics: Analysis, Systems, Applications”, PHI, New Delhi.
Mittal R.K. and Nagrath I.J., “Robotics and Control”, Tata McGraw Hill.
Mukherjee S., “Robotics and Automation”, Khanna Publishing House, Delhi.
Craig, J.J., “Introduction to Robotics: Mechanics and Control”, Pearson, New Delhi, 2009
Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, “Robot Modelling and Control”, John Wiley and Sons Inc, 2005
Steve Heath, “Embedded System Design”, 2nd Edition, Newnes, Burlington, 2003
Merzouki R., Samantaray A.K., Phathak P.M. and Bouamama B. Ould, “Intelligent Mechatronic System: Modeling, Control and Diagnosis”, Springer.
After the completion of this course, the students will be able to:
Understand robot mechanism
Perform kinematic and dynamic analyses with simulation
Design control laws for a robot
Integrate mechanical and electrical hardware for a real prototype of robotic device
Select a robotic system for given application
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to impart necessary knowledge to the learner so that he/she can develop and implement algorithm and write programs using these algorithm
Motivation for studying Quantum Computing , Major players in the industry (IBM, Microsoft, Rigetti, D-Wave etc.), Origin of Quantum Computing
Overview of major concepts in Quantum Computing: Qubits and multi-qubits states, Braket notation, Bloch Sphere representation, Quantum Superposition, Quantum Entanglement
Math Foundation for Quantum Computing: Matrix Algebra: basis vectors and orthogonality, inner product and Hilbert spaces, matrices and tensors, unitary operators and projectors, Dirac notation, Eigen values and Eigen vectors
Building Blocks for Quantum Program: Architecture of a Quantum Computing platform,
Details of q-bit system of information representation: Block Sphere, Multi-qubits States, Quantum superposition of qubits (valid and invalid superposition), Quantum Entanglement, Useful states from quantum algorithmic perceptive e.g. Bell State, Operation on qubits: Measuring and transforming using gates.
Quantum Logic gates and Circuit: Pauli, Hadamard, phase shift, controlled gates, Ising, Deutsch, swap etc.
Programming model for a Quantum Computing Program: Steps performed on classical computer, Steps performed on Quantum Computer, Moving data between bits and qubits.
Basic techniques exploited by quantum algorithms, Amplitude amplification, Quantum Fourier
Transform, Phase Kick-back, Quantum Phase estimation, Quantum Walks
Major Algorithms: Shor’s Algorithm, Grover’s Algorithm, Deutsch’s Algorithm, Deutsch -Jozsa Algorithm OSS Toolkits for implementing Quantum program: IBM quantum experience, Microsoft Q, Rigetti PyQuil (QPU/QVM)
Michael A. Nielsen, “Quantum Computation and Quantum Information”, Cambridge University Press.
David McMahon, “Quantum Computing Explained”, Wiley
After the completion of this course, the students will be able to:
Understand major concepts in Quantum Computing
Explain the working of a Quantum Computing program, its architecture and program model
Develop quantum logic gate circuits
Develop quantum algorithm
Program quantum algorithm on major toolkits
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
The objective of this course is to provide conceptual understanding of how block chain technology can be used to innovate and improve business processes. The course covers the technological underpinning of block Chain operations in both theoretical and practical implementation of solutions using block Chain technology.
Working with Consensus in Bitcoin: Distributed consensus in open environments, Consensus in a Bitcoin network, Proof of Work (PoW) – basic introduction, HashCash PoW, Bitcoin PoW, Attacks on PoW and the monopoly problem, Proof of Stake, Proof of Burn and Proof of Elapsed Time, The life of a Bitcoin Miner, Mining Difficulty, Mining Pool
Melanie Swan, “Block Chain: Blueprint for a New Economy”, O’Reilly, 2015
Josh Thompsons, “Block Chain: The Block Chain for Beginners- Guide to Block chain Technology and Leveraging Block Chain Programming”
Daniel Drescher, “Block Chain Basics”, Apress; 1stedition, 2017
Anshul Kaushik, “Block Chain and Crypto Currencies”, Khanna Publishing House, Delhi.
Imran Bashir, “Mastering Block Chain: Distributed Ledger Technology, Decentralization and Smart Contracts Explained”, Packt Publishing
Ritesh Modi, “Solidity Programming Essentials: A Beginner’s Guide to Build Smart Contracts for Ethereum and Block Chain”, Packt Publishing
Salman Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, Anthony O’Dowd, Venkatraman Ramakrishna, “Hands-On Block Chain with Hyperledger: Building Decentralized Applications with Hyperledger Fabric and Composer”, Import, 2018
After the completion of this course, the students will be able to:
Understand block chain technology
Acquire knowledge of crytocurrencies
Develop block chain based solutions and write smart contract using Hyperledger Fabric and Ethereum frameworks
Build and deploy block chain application for on premise and cloud based architecture
Integrate ideas from various domains and implement them using block chain technology in different perspectives
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To provide the basic knowledge on the levels of interaction, design models, techniques and validations focusing on the different aspects of human-computer interface and interactions
Input–output channels, Human memory, Thinking: reasoning and problem solving, Emotion, Individual differences, Psychology and the design of interactive systems, Text entry devices, Positioning, pointing and drawing, Display devices, Devices for virtual reality and 3D interaction, Physical controls, sensors and special devices, Paper: printing and scanning
Overview of Interaction Design Models, Discovery - Framework, Collection - Observation, Elicitation, Interpretation - Task Analysis, Storyboarding, Use Cases, Primary Stakeholder Profiles, Project Management Document
Model Human Processor - Working Memory, Long-Term Memory, Processor Timing, Keyboard Level Model - Operators, Encoding Methods, Heuristics for M Operator Placement, What the Keyboard Level Model Does Not Model, Application of the Keyboard Level Model, GOMS - CMN-GOMS Analysis, Modeling Structure, State Transition Networks - Three-State Model, Glimpse Model, Physical Models, Fitts’ Law
Shneideman's eight golden rules, Norman's Sever principles, Norman's model of interaction, Nielsen's ten heuristics, Heuristic evaluation, contextual evaluation, Cognitive walk-through Collaboration and Communication:
Face-to-face Communication, Conversation, Text-based Communication, Group working, Dialog design notations, Diagrammatic notations, Textual dialog notations, Dialog semantics, Dialog analysis and design
Groupware, Meeting and decision support systems, Shared applications and artifacts, Frameworks for groupware Implementing synchronous groupware, Mixed, Augmented and Virtual Reality Validation: Validations - Usability testing, Interface Testing, User Acceptance Testing
A Dix, Janet Finlay, G D Abowd, R Beale., Human-Computer Interaction, 3rd Edition, Pearson Publishers,2008
Shneiderman, Plaisant, Cohen and Jacobs, Designing the User Interface: Strategies for Effective Human Computer Interaction, 5th Edition, Pearson Publishers, 2010.
Hans-Jorg Bullinger,” Human-Computer Interaction”, Lawrence Erlbaum Associates, Publishers
Jakob Nielsen,” Advances in Human-computer Interaction”,Ablex Publishing Corporation
Thomas S. Huang,” Real-Time Vision for Human-Computer Interaction”, Springer
Preece et al, Human-Computer Interaction, Addison-Wesley, 1994
After the completion of this course, the students will be able to:
Enumerate the basic concepts of human, computer interactions
Create the processes of human computer interaction life cycle
Analyze and design the various interaction design models
Apply the interface design standards/guidelines for evaluating the developed interactions
Apply product usability evaluations and testing methods
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To impart knowledge and skills related to 3D printing technologies, selection of material and equipment and develop a product using this technique in Industry 4.0 environment
Introduction, Process, Classification, Advantages, Additive V/s Conventional Manufacturing processes, Applications.
CAD for Additive Manufacturing: CAD Data formats, Data translation, Data loss, STL format.
Stereo- Lithography, LOM, FDM, SLS, SLM, Binder Jet technology. Process, Process parameter, Process Selection for various applications.
Additive Manufacturing Application Domains: Aerospace, Electronics, Health Care, Defence, Automotive, Construction, Food Processing, Machine Tools
Polymers, Metals, Non-Metals, Ceramics
Various forms of raw material- Liquid, Solid, Wire, Powder; Powder Preparation and their desired properties, Polymers and their properties, Support Materials.
Process Equipment- Design and process parameters, Governing Bonding Mechanism, Common faults and troubleshooting, Process Design
Post Processing Requirement and Techniques. Product Quality: Inspection and testing, Defects and their causes
Lan Gibson, David W. Rosen and Brent Stucker, “Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing”, Springer, 2010.
Andreas Gebhardt, “Understanding Additive Manufacturing: Rapid Prototyping, Rapid Tooling, Rapid Manufacturing”, Hanser Publisher, 2011.
Khanna Editorial, “3D Printing and Design”, Khanna Publishing House, Delhi.
CK Chua, Kah Fai Leong, “3D Printing and Rapid Prototyping- Principles and Applications”, World Scientific, 2017.
J.D. Majumdar and I. Manna, “Laser-Assisted Fabrication of Materials”, Springer Series in Material Science, 2013.
L. Lu, J. Fuh and Y.S. Wong, “Laser-Induced Materials and Processes for Rapid Prototyping”, Kulwer Academic Press, 2001.
Zhiqiang Fan And Frank Liou, “Numerical Modelling of the Additive Manufacturing (AM) Processes of Titanium Alloy”, InTech, 2012.
After the completion of this course, the students will be able to:
Develop CAD models for 3D printing.
Import and Export CAD data and generate .stl file.
Select a specific material for the given application.
Select a 3D printing process for an application.
Produce a product using 3D Printing or Additive Manufacturing (AM).
New Scheme Based On AICTE Flexible Curricula Information Technology, VIII- semester
To develop an understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computers and to develop programming skills to effectively implement parallel architecture
D. E. Culler, J. P. Singh, and A. Gupta, “Parallel Computer Architecture”, MorganKaufmann, 2004
Rajeev Balasubramonian, Norman P. Jouppi, and Naveen Muralimanohar, “Multi-Core Cache Hierarchies”, Morgan & Claypool Publishers, 2011
Peter and Pach Eco, “An Introduction to Parallel Programming”, Elsevier, 2011
James R. Larus and Ravi Rajwar, “Transactional Memory”, Morgan & Claypool Publishers, 2007
David B. Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors: A Hands-on Approach”, 2010
Barbara Chapman, F. Desprez, Gerhard R. Joubert, Alain Lichnewsky, Frans Peters “Parallel Computing: From Multicores and GPU's to Petascale”, 2010
Michael McCool, James Reinders, Arch Robison, “Structured Parallel Programming: Patterns for Efficient Computation”, 2012
Jason Sanders, Edward Kandrot, “CUDA by Example: An Introduction to GeneralPurpose GPU Programming”, 2011
After the completion of this course, the students will be able to:
To develop an understanding of various basic concepts associated with parallel computing environments
Understand, appreciate and apply parallel and distributed algorithms in problem solving
Acquire skills to measure the performance of parallel and distributed programs
Design parallel programs to enhance machine performance in parallel hardware environment
Design and implement parallel programs in modern environments such as CUDA, OpenMP, etc