minor degree final - government of india, all india

Model Curriculum for Minor Degree

for UG Degree Courses in Engineering & Technology

2020

ALLINDIACOUNCILFORTECHNICALEDUCATIONNelsonMandelaMarg,VasantKunj,NewDelhi110070

www.aicte-india.org

MESSAGEWith a view to enhance the employability skills and impart deep knowledge inemergingareaswhichareusuallynotbeingcoveredinUndergraduateDegreecreditframework, AICTE has come upwith the concept of ‘Minor Degree’ in emergingareas.TheconceptofMinorDegreeisdiscussedintheApprovalProcessHandbook(APH) for theacademicsession2020-21 issuedbyAICTE.MinorDegreewillcarry18to20creditsinadditiontothecreditsessentialforobtainingtheUnderGraduateDegreeinMajorDiscipline(i.e.160creditsusually).

Keeping inmindtheneedofmanpower inemergingareas,AICTEwith thehelpofindustry-academiaexperts,hasframedthecurriculumforsevenMinorDegrees:

• ArtificialIntelligenceandMachineLearning• Blockchain• CyberSecurity• DataScience• InternetofThings(IoT)• Robotics• VirtualandAugmentedReality

Courses have been designed after rigorous brainstorming and considering theinputs from theexpertsof correspondingdomain. I amhopeful thatknowledgeofthese emerging areaswill help students in capturing the plethora of employmentopportunitiesavailableinthesedomains.

I gratefully acknowledge the time and efforts of all those who were involved inpreparationof this curriculumespecially, the contributionsof themembersof theWorkingGroup:Prof.RajeshK.Bhatia fromPunjabEngineeringCollege,Prof.AjayMittal from Punjab University, Dr. Varun Dutt from IIT Mandi, Ms. Manisha fromEducation Infosys Ltd,Dr. Shantipal S.Ohol fromCollege of EngineeringPune,Dr.Pushparaj Pathak from IIT Delhi and Dr. S.K Saha from IIT Roorkee. I am verythankful toProf.Uday.B.Desai,Director, IITHyderabadforhelping inrefiningthedraft.

ThewelltimedinitiativetohavethismodelcurriculumaddressingtheneedbyProf.M.PPoonia,ViceChairman,Prof.RajiveKumar,MemberSecretary,AICTEishighlyappreciated.IalsoappreciatethecontinuouseffortputincoordinatingthecompleteprocessofdevelopmentofthiscurriculumbymembersofthePolicyandAcademicPlanning Bureau of AICTE namely, Dr. Dileep Malkhede, Adviser–I; Dr. NeerajSaxena, Adviser-II; Dr. Pradeep Bhaskar, Assistant Director,Mr. Dharmesh KumarDewangan&Mr.RakeshKumarPandit,YoungProfessionalsandothers.

(Prof.AnilD.Sahasrabudhe)Chairman

AllIndiaCouncilforTechnicalEducation

Working Group for this Model Curriculum of Minor Degree for UGDegreeCoursesinEngineering&TechnologyS.No Name Designation&Organization1

Prof.RajeshKBhatiaProfessor, Computer Science and Engineering Dept.,PunjabEngineeringCollege(DeemedUniversity)

2Prof.AjayMittal

Professor, Computer Science and Engineering Dept.,University Institute of Engineering & Technology,PunjabUniversity

3Dr.VarunDutt

Associate Professor, Computer Science andEngineering,IITMandi

4 Ms.Manisha LeadPrincipal,EducationInfosysLtd.

WorkingGroupforRoboticsModelCurriculum:

S.No Name Designation&Organization1 Dr.S.KSaha Professor,IITDelhi2 Dr.PushparajPathak Professor,IITRoorkee3 Prof.S.Ohol Professor,CollegeofEngineeringPune,

TableofContents

S.No. Subject From To1 ArtificialIntelligenceandMachineLearning 1 122 Blockchain 13 243 CyberSecurity 25 344 DataScience 35 465 InternetofThings(IoT) 47 586 Robotics 59 707 VirtualandAugmentedReality 71 82

1

ArtificialIntelligenceandMachineLearning

3

MinorDegreein“ArtificialIntelligenceandMachineLearning”

CourseStructureS.No. CourseCode Title L T P Credits1 AIML-01 IntroductiontoAI&MachineLearning 3 0 2 42 AIML-02 IntroductiontoDataAnalytics 3 0 2 43 AIML-03 DeepLearningandNeuralNetwork 3 0 2 44 AIML-04 SpecialtopicsinArtificialIntelligence 3 0 0 35 AIML-05 ApplicationsofAI 3 0 0 3

TOTAL 15 0 6 18

CourseCodingNomenclature:

• AIMLdenotesthatminordegreein“ArtificialIntelligenceandMachineLearning”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in different

semesters.Multiplecoursemayalsobetakeninthesamesemester(ifrequired).---------------------------------------------------------------------------------------------------------------

5

DetailedSyllabus

CourseCode : AIML-01CourseTitle : IntroductiontoAI&MachineLearningNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : AIML

CourseObjective:

• ToreviewandstrengthenimportantmathematicalconceptsrequiredforAI&ML.• Introduce the concept of learning patterns from data and develop a strong

theoretical foundation for understanding state of the art Machine Learningalgorithms.

CourseContents:

[TotalTheoryDuration:42Lectures]

Module1:[Duration:12Lectures]Defining Artificial Intelligence, Defining AI techniques, Using Predicate Logic andRepresenting Knowledge as Rules, Representing simple facts in logic, Computablefunctions and predicates, Procedural vs Declarative knowledge, Logic Programming,Mathematicalfoundations:MatrixTheoryandStatisticsforMachineLearning.

Module2:[Duration:8Lectures]Idea of Machines learning from data, Classification of problem –Regression andClassification,SupervisedandUnsupervisedlearning.

Module3:[Duration:10Lectures]Linear Regression: Model representation for single variable, Single variable CostFunction,GradientDecentforLinearRegression,GradientDecentinpractice.Module4:[Duration:7Lectures]Logistic Regression: Classification, Hypothesis Representation, Decision Boundary,Cost function, Advanced Optimization, Multi-classification (One vs All), Problem ofOverfitting.

Module5:[Duration:5Lectures]Discussion on clustering algorithms and use-cases cantered around clustering andclassification.

LabWork:

1. ImplementationoflogicalrulesinPython.2. Usinganydataapplytheconceptof:

a. Linerregressionb. Gradientdecentc. Logisticregression

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3. Toaddthemissingvalueinanydataset.4. Performandplotunderfittingandoverfittinginadataset.5. Implementationofclusteringandclassificationalgorithms.

TextBooks/References:

1. SarojKaushik,ArtificialIntelligence,CengageLearning,1stEdition2011.2. Anindita Das Bhattacharjee, “Practical Workbook Artificial Intelligence and Soft

Computingforbeginners,ShroffPublisher-XteamPublisher.3. M.C. Trivedi, A Classical Approach to Artificial Intelligence, Khanna Publishing

House,Delhi.4. JeevaJose,IntroductiontoMachineLearning,KhannaPublishingHouse,Delhi.5. Yuxi (Hayden) Liu, “Python Machine Learning by Example”, Packet Publishing

Limited,2017.6. TomMitchell,MachineLearning,McGrawHill,2017.7. ChristopherM.Bishop,PatternRecognitionandMachineLearning,Springer,2011.8. T.Hastie,R.Tibshirani,J.Friedman.TheElementsofStatisticalLearning, 2e, 2011.

CorrespondingOnlineResources:1. ArtificialIntelligence,https://swayam.gov.in/nd2_cec20_cs10/preview.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Designand implementmachine learning solutions to classification, regressionand

clusteringproblems.2. EvaluateandinterprettheresultsofthedifferentMLtechniques.3. DesignandimplementvariousmachinelearningalgorithmsinarangeofReal-world

applications.

*****

CourseCode : AIML-02CourseTitle : IntroductiontoDataAnalyticsNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : AIML

CourseObjective:

• Provide you with the knowledge and expertise to become a proficient datascientist

• Demonstrateanunderstandingofstatisticsandmachinelearningconceptsthatarevitalfordatascience;

• ProducePythoncodetostatisticallyanalyseadataset;• Critically evaluate data visualisations based on their design and use for

communicatingstoriesfromdata;

CourseContents:

7

Module1:[Duration:7Lectures]Introduction to Data Science, Different Sectors using Data science, Purpose andComponentsofPythoninDataScience.

Module2:[Duration:7Lectures]Data Analytics Process, Knowledge Check, Exploratory Data Analysis (EDA), EDA-Quantitative technique, EDA- Graphical Technique, Data Analytics Conclusion andPredictions.

Module3:[Duration:11Lectures]FeatureGenerationandFeatureSelection(ExtractingMeaning fromData)-Motivatingapplication: user (customer) retention- Feature Generation (brainstorming, role ofdomainexpertise,andplaceforimagination)-FeatureSelectionalgorithms.Module4:[Duration:10Lectures]DataVisualization-Basicprinciples, ideasandtoolsfordatavisualization,Examplesofinspiring (industry) projects- Exercise: create your own visualization of a complexdataset.

Module5:[Duration:7Lectures]Applications ofData Science,Data Science andEthical Issues-Discussions onprivacy,security,ethics-AlookbackatDataScience-Next-generationdatascientists.LabWork:

1.PythonEnvironmentsetupandEssentials.2.MathematicalcomputingwithPython(NumPy).3.ScientificComputingwithPython(SciPy).4.DataManipulationwithPandas.5.PredictionusingScikit-Learn6.DataVisualizationinpythonusingmatplotlibTextBooks/References:1. JoelGrus,DataSciencefromScratch,ShroffPublisherPublisher/O’ReillyPublisher

Media2. V.K.Jain,BigDataandHadoop,KhannaPublishingHouse3. V.K.Jain,DataSciences&Analytics,KhannaPublishingHouse4. AnnalynNg,KennethSoo,Numsense!DataSciencefortheLayman,ShroffPublisher

Publisher5. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The

Frontline.O’ReillyPublisherMedia.6. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets.

v2.1,CambridgeUniversityPress.7. Jake VanderPlas, Python Data Science Handbook, Shroff Publisher Publisher

/O’ReillyPublisherMedia8. Philipp Janert, Data Analysis with Open Source Tools, Shroff Publisher Publisher

/O’ReillyPublisherMedia.

8

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Explainhowdataiscollected,managedandstoredfordatascience;2. Understandthekeyconceptsindatascience,includingtheirreal-worldapplications

andthetoolkitusedbydatascientists;3. ImplementdatacollectionandmanagementscriptsusingMongoDB.

*****

CourseCode : AIML-03CourseTitle : DeepLearningandNeuralNetworkNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : AIML

CourseObjective:

• To strengthen important Mathematical concepts required for Deep learning andneuralnetwork.

• TogetadetailedinsightofadvancedalgorithmsofML.

CourseContents:


Module1:[Duration:8Lectures]Informationflowinaneuralnetwork,understandingbasicstructureandANN.

Module2:[Duration:8Lectures]TrainingaNeuralnetwork,howtodeterminehiddenlayers,recurrentneuralnetwork.

Module3:[Duration:10Lectures]Convolutionalneuralnetworks,imageclassificationandCNN.

Module4:[Duration:9Lectures]RNNandLSTMs.ApplicationsofRNNinrealworld.

Module5:[Duration:7Lectures]Creatinganddeployingnetworksusingtensorflowandkeras.

LabWork:1. IntroductiontoKaggleandhowitcanbeusedtoenhancevisibility.2. Buildgeneralfeaturestobuildamodelfortextanalytics.3. Buildanddeployyourowndeepneuralnetworkonawebsiteusingtensorflow.TextBooks/References:

1. RajivChopra,DeepLearning,KhannaPublishingHouse.2. JohnPaulMueller,LucaMassaron,DeepLearningforDummies,JohnWiley&Sons.3. Adam Gibson, Josh Patterson, Deep Learning, A Practitioner’s Approach, Shroff

Publisher/O’ReillyPublisherMedia.4. ChristopherM.Bishop,NeuralNetworksforPatternRecognition,Oxford.

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5. Russell Reed, Robert J MarksII, Neural Smithing: Supervised Learning inFeedforwardArtificialNeuralNetworks,BradfordBookPublishers.

CorrespondingOnlineResources:1. FuzzyLogicandNeuralNetworks,

https://swayam.gov.in/nd1_noc20_ge09/preview.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. TodesignandimplementArtificialNeuralnetworks.2. TodecidewhentousewhichtypeofNN.

*****

CourseCode : AIML-04CourseTitle : SpecialtopicsinArtificialIntelligenceNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : AIML

Course Objective: To give fundamental knowledge to the students so that they canunderstandwhattheAIisandstudyimportanttopicsrelatedtothefield.

CourseContents:


Module1:[Duration:9Lectures]Bayesian Filtering; Recurrent Neural Networks, Deep Neural Networks, DeepReinforcementLearning.Module2:[Duration:7Lectures]Self-PlayNetworks,GenerativeAdversarialNetworks,Learning fromConcept-DriftingDataStreams.Module3:[Duration:9Lectures]Audio Signal Processing Basics, mirtoolbox contains many useful audio processinglibraryfunctions,VOICEBOX:SpeechProcessingToolboxforMATLAB,AudioprocessinginMatlab.Module4:[Duration:10Lectures]Architectures for secondgenerationknowledgebasedsystems,DistributedAIand itsapplications.Module5:[Duration:7Lectures]Anintroductiontoneurocomputinganditspossiblerole inAI,TheroleofuncertaintymeasuresandprinciplesinAI.

10


1. Dr. Nilakshi Jain, Artificial Intelligence:Making a System Intelligent, JohnWiley&Sons.

2. M.C. Trivedi, A Classical Approach to Artificial Intelligence, Khanna PublishingHouse,Delhi.

3. ArtificialIntelligence&SoftComputingforBeginners,3rdEdition-2018,byAninditaDas,ShroffPublisherPublisher.

4. ArtificialIntelligence:AModernApproach,3rdEdition,byStuartRussellandPeterNorvig,PearsonPublisher.

5. New Artificial Intelligence (Advanced), Takashi Maeda and Fumio Aoki, OhmshaPublisher.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. TounderstandvariousAItechniques.2. TodecidewhentousewhichtypeofAItechnique.

*****

CourseCode : AIML-05CourseTitle : ApplicationsofAINumberofCredits : 3(L:3;T:0;P:0)CourseCategory : AIML

CourseObjective:TogivedeepknowledgeofAIandhowAIcanbeappliedinvariousfieldstomakethelifeeasy.

CourseContents:[TotalTheoryDuration:42Lectures]

Module1:[Duration:12Lectures]Linguistic aspects of natural language processing, A.I. And Quantum Computing,ApplicationsofArtificialIntelligence(AI)inbusiness.Module2:[Duration:8Lectures]EmotionRecognitionusinghumanfaceandbodylanguage,AIbasedsystemtopredictthediseasesearly,SmartInvestmentanalysis,AIinSalesandCustomerSupport.

Module3:[Duration:7Lectures]RoboticProcessesAutomationforsupplychainmanagement.Module4:[Duration:8Lectures]AI-Optimized Hardware, Digital Twin i.e. AI Modelling, Information Technology &SecurityusingAI.Module5:[Duration:7Lectures]RecentTopicsinAI/ML:AI/MLinSmartsolutions,AI/MLinSocialProblemshandling,BlockchainandAI.

11


1. SameerDhanrajani,AIandAnalytics,AcceleratingBusinessDecisions,JohnWiley&Sons.

2. Life3.0:BeingHumanintheAgeofArtificialIntelligencebyMaxTegmark,publishedJuly2018.

3. HomoDeus:ABriefHistoryofTomorrowbyYuvalNoahHarari, publishedMarch2017.

4. Artificial Intelligence in Practice: How 50 Successful Companies Used AI andMachineLearningtoSolveProblems,BernardMarr,MattWard,Wiley.

CourseOutcomes:Aftercompletionofcourse,studentswould:1. TocorrelatetheAIandsolutionstomodernproblem.2. TodecidewhentousewhichtypeofAItechnique.

*****

13

Blockchain

15

MinorDegreein“Blockchain”

CourseStructureS.No. CourseCode Title L T P Credits1 BLC-01 FundamentalsofBlockchain 3 0 0 32 BLC-02 SmartContractsandSolidity 3 0 2 43 BLC-03 BlockchainPlatformsandUsecases 3 0 2 44 BLC-04 BlockchainSecurityandPerformance 3 0 2 45 BLC-05 BlockchainandFinTech 3 0 0 3

TOTAL 15 0 6 18


• BLCdenotesthatminordegreein“Blockchain”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in

differentsemesters.Multiplecoursemayalsobetakeninthesamesemester(ifrequired).

---------------------------------------------------------------------------------------------------------------

17

DetailedSyllabus

CourseCode : BLC-01CourseTitle : FundamentalsofBlockchainNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : BLC

CourseObjective:

• The students should be able to understand a broad overview of the essentialconceptsofblockchaintechnology.

• TofamiliarizestudentswithBitcoinprotocolfollowedbytheEthereumprotocol–tolaythefoundationnecessaryfordevelopingapplicationsandprogramming.

• Studentsshouldbeabletolearnaboutdifferenttypesofblockchainandconsensusalgorithms.

CourseContents:

Module1[6Lectures]Basics:TheDouble-SpendProblem,ByzantineGenerals’ComputingProblems,Public-KeyCryptography,Hashing,DistributedSystems,DistributedConsensus.

Module2[10Lectures]TechnologyStack:Blockchain,Protocol,Currency.Bitcoin Blockchain: Structure, Operations, Features, Consensus Model, IncentiveModel.

Module3[10Lectures]Ethereum Blockchain: Smart Contracts, Ethereum Structure, Operations, ConsensusModel,IncentiveModel.

Module4[6Lectures]TiersofBlockchainTechnology:Blockchain1.0,Blockchain2.0,Blockchain3.0,Typesof Blockchain: Public Blockchain, Private Blockchain, Semi-Private Blockchain,Sidechains.

Module5[10Lectures]Types of Consensus Algorithms: Proof of Stake, Proof ofWork, Delegated Proof ofStake,ProofElapsedTime,Deposite-BasedConsensus,Proofof Importance,FederatedConsensus or Federated Byzantine Consensus, Practical Byzantine Fault Tolerance.BlockchainUseCase:SupplyChainManagement.


1. KirankalyanKulkarni,EssentialsofBitcoinandBlockchain,PacktPublishing.2. AnshulKaushik,BlockChain&CryptoCurrencies,KhannaPublishingHouse.3. TianaLaurence,BlockchainforDummies,2ndEdition2019,JohnWiley&Sons.4. MasteringBlockchain:Deeper insights intodecentralization,cryptography,Bitcoin,

andpopularBlockchainframeworksbyImranBashir,PacktPublishing(2017).

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5. Blockchain:BlueprintforaNewEconomybyMelanieSwan,ShroffPublisherO’ReillyPublisherMedia;1stedition(2015).

6. MasteringBitcoin:ProgrammingtheOpenBlockchainbyAndreasAntonopoulos.CorrespondingOnlineResources:1. https://www.coursera.org/specializations/blockchain.2. https://nptel.ac.in/courses/106105184/3. IntroductiontoBlockchainTechnologyandApplications,

https://swayam.gov.in/nd1_noc20_cs01/preview

CourseOutcomes:Aftercompletionofthiscourse,studentswouldbeable:1. Toexplainthebasicnotionofdistributedsystems.2. Tousetheworkingofanimmutabledistributedledgerandtrustmodelthatdefines

blockchain.3. Toillustratetheessentialcomponentsofablockchainplatform.

*****

CourseCode : BLC-02CourseTitle : SmartContractsandSolidityNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : BLCCourseObjective:

1. Students should be able to understand the concept of smart contracts related toblockchain.

2. Students should be able to understand the smart contract higher-level languageSolidityandapplyittocreatesmartcontracts.

3. StudentsshouldbeabletolearnTruffleIDEforcreatinganddeployingaDApp.

CourseContents:

Module1Smart Contracts: Definition and Need, Features of Smart Contracts, Life Cycle of aSmartContract,IntroductiontoEthereumHigher-LevelLanguages.

Module2Development Environment: Building A Simple Smart Contract with Solidity, Solc-Compiler,EthereumContractABI,Remix-IDEforSmartContractDevelopment.

Module3Introduction to Solidity: Contracts, Constructors & Functions, Variables, Getters &Setters,Arrays,MemoryvsStorage,MappingsinSolidity

AdvancedSolidity: Structs,ErrorHandling&Restrictions,Libraries,GlobalVariablesinSolidity,AbstractContracts,Inheritance,AndInterfaces,Events

19

Module4Truffle Framework & Ganache: Environment Setup for Truffle & Ganache, TruffleProjectCreation,TruffleCompile,MigrateandCreateCommands.

Module5DecentralizedAppCreation:SmartContractCreation,Front-EndCreation,ConnectingSmartContractwithFront-EndApplication,DeployingDapp,Validation,AndTestingofDapp.


1. TianaLaurence,BlockchainforDummies,2ndEdition2019,JohnWiley&Sons.2. AnshulKaushik,BlockChain&CryptoCurrencies,KhannaPublishingHouse.3. BuildingBlockchainProjects,NarayanPrusty,PacktPublishing.4. Mastering Ethereum: Building Smart Contracts and Dapps Book by Andreas

AntonopoulosandGavinWood,ShroffPublisher/O′ReillyPublisher.

CorrespondingOnlineResources:1. https://www.coursera.org/learn/smarter-contracts2. https://www.udemy.com/course/solidity-smart-contracts-build-dapps-in-

ethereum-blockchain/3. IntroductiontoBlockchainTechnologyandApplications,

https://swayam.gov.in/nd1_noc20_cs01/preview

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Tounderstandtheworkingandimportanceofsmartcontracts.2. TolearnthesoliditylanguagerequiredforcodingEthereumsmartcontracts.3. TocreateanddeployaDApponaEthereumtestnetwork.

*****

CourseCode : BLC-03CourseTitle : BlockchainPlatformsandUsecasesNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : BLCCourseObjective:

• Studentsshouldbeabletolearndifferenttypesofblockchainplatforms.• StudentsshouldbeabletounderstanddifferenttypesofDecentralizedapplications

developedusingblockchaintechnology.• Studentsshouldbeabletounderstandseveraltypesofblockchainusecases.

CourseContents:

Module1[14Lectures]Permissioned Blockchains: Hyperledger Fabric Services, Model and Functions,HyperledgerComposer,MicrosoftAzureBlockchainPlatformandServices,OtherPlatforms:IOTA,TRON,Ziliqa,Cosmos,Ripple.

20

Module2[5Lectures]Decentralized Application Platforms: Augur-Decentralised Prediction MarketPlatform,Grid+-EnergyEcosystemPlatform.

Module3[5Lectures]ChallengesandSolutionsRelatedtoBlockchain:Consensus,Scalability,PrivacyandConfidentiality,Escrow,andMultisignature.

Module4[8Lectures]AlternativeDecentralizedSolutions:InterplanetaryFileSystem(IPFS)WorkingandUses,Hashgrapgh-Working,Benefits,AndUse-Cases.Module5[10Lectures]Blockchain Use Cases: Financial Services Related Use Cases, Revolutionization ofGlobalTrade,DigitalIdentity,AuditingServices,SupplyChainManagement,HealthcareRelatedServices,BlockchainandIOT,BlockchainandAI.


1. TianaLaurence,BlockchainforDummies,2ndEdition2019,JohnWiley&Sons.2. AnshulKaushik,BlockChain&CryptoCurrencies,KhannaPublishingHouse.3. BuildingBlockchainProjects,NarayanPrusty,PacktPublishing.4. MasteringBlockchain:Deeper insights intodecentralization,cryptography,Bitcoin,

andpopularBlockchain frameworksby ImranBashir,PacktPublishing (March17,2017).

5. Blockchain: Blueprint for a New Economy by Melanie Swan, Shroff Publisherpublisher/O’ReillyPublisherMedia;1stedition(2015).

CorrespondingOnlineResources:1. https://nptel.ac.in/courses/106105184/2. https://www.coursera.org/learn/blockchain-platforms.3. IntroductiontoBlockchainTechnologyandApplications,

https://swayam.gov.in/nd1_noc20_cs01/preview.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Todistinguishbetweendifferenttypesofblockchainplatforms.2. To understand different types of uses of blockchain and apply it to some real-life

scenariosaccordingly.3. Tolearnabouttheshortcomingsofblockchaintechnologyandtheircorresponding

solutions.

21

******

CourseCode : BLC-04CourseTitle : BlockchainSecurityandPerformanceNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : BLC

CourseObjective:• Studentsshouldbeabletounderstandthesecurityandperformance-relatedissues

ofblockchain.• Studentsshouldbeabletolearntechniquesandtoolstotacklethesecurityrelated

issuesofblockchain.• Studentsshouldbeabletolearnnewapproachesrequiredforenhancingblockchain

performance.CourseContents:

Module1[6Lectures]Security Issues: Blockchain Related Issues, Higher-Level Language (Solidity) RelatedIssues, EVM Bytecode Related Issues, Real-Life Attacks on Blockchain Applications/SmartContracts,TrustedExecutionEnvironments.

Module2[12Lectures]Security Tools for Smart Contracts: Working, Advantages, And Disadvantages ofTools- Oyente, Securify, Maian, Manticore, Mythril, SmartCheck, Verx. Secure KeyManagement,QuantumResilienceKeys.

Module3[5Lectures]Performance Related Issues: Transaction Speed, Transaction Fees, Network Size,Complexity, Interoperability Problems, Lack of Standardization. Lack of SupportiveRegulationsRelatedtoBlockchainApplications.Module4[12Lectures]Performance Improvements: Off-Chain State Channels, Sidechains, Parallels Chains,ConcurrentSmartContractTransactions,ShardingTechniqueand ItsBenefits,AtomicSwapsBetweenSmartContracts.Module5[7Lectures]BlockchainApplications:DecentralizedCryptocurrency,DistributedCloudStorage,E-Voting, Insurance Claims, Cross-Border Payments, Asset Management, SmartAppliances.


1. Mastering Ethereum: Building Smart Contracts and Dapps Book by AndreasAntonopoulosandGavinWood,ShroffPublisher/O′ReillyPublisher.

22

CorrespondingOnlineResources:1. https://www.edx.org/course/blockchain-and-fintech-basics-applications-and-

limitationsCourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Tounderstandthesecurityandperformanceperspectiveofblockchaintechnology.2. Tolearnandapplysecurityanalysisandperformance-enhancingtechniquesrelated

toblockchain.3. To understand the real-life applications of blockchain technology and apply it to

providesolutionstosomereal-lifeproblems.

*****

CourseObjective:• Studentsshouldbeabletounderstandthebenefitsofusingblockchaininfinancial

sector.• Students should understand how decentralized nature of blockchain is impacting

bankingandfinancialsector.• Students should learn blockchain regulations and future trends related to

blockchaintobeusedinfinancialsector.

CourseContents:

Module1[12Lectures]Cryptocurrencies:Concept,CryptocurrencyMining,UsesofCryptocurrencies,Tokens,TokenvsCryptoCoin,Conceptof ICOs (InitialCoinOfferings),BenefitsofUsing ICOs,STOs(Securitytokenofferings),ICOvsSTO,Cryptocurrencywallets.

Module2[5Lectures]Decentralized Finance (DeFi): Concept, Benefits and Risks Associated with DeFi,CentralizedvsDecentralizedfinance,DeFiProjects,DeFifuturetrends.

Module3[11Lectures]DecentralizedMarkets:ConceptofDecentralizedmarkets,impactofdecentralizationon financial market, Decentralized Exchanges (DEX), Security, control and privacyconcerns related to DEX,Liquidity and Usability of DEX,best DEXs for trading, FundManagementandTradinglogicofDEX,ConceptofDecentralizedWeb.

Module4[7Lectures]Blockchain & Cryptocurrency Regulations: Introduction, History Stance of theGovernment, Judicial Approach to Cryptocurrency, Possible Reasons for Ban, Virtual

CourseCode : BLC-05CourseTitle : BlockchaininFinTechNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : BLC

23

Currency Regulations, Global Perspective of Regulations on Blockchain, Future needsforRegulations.Module5[7Lectures]Blockchain in Banking Sector: Cross-Border Payments Using Blockchain and ItsBenefits, Study of blockchain platforms used for cross-border payments, Impact ofBlockchainonBankingServices.StableCoin:Concept,UsesandTypesofStableCoinsCase-Study:TetherandLibraCoinsTextBooks/References:

1. MelanieSwan,Blockchain:Blueprint foraneweconomy,ShroffPublisher/O’ReillyPublisher.

2. Ron Quaranta, Blockchain in Financial Markets and Beyond: Challenges andApplications,RiskBooksPublisher.

3. RichardHayen,Blockchain&FinTech:AComprehensiveBlueprinttoUnderstandingBlockchain & Financial Technology. - Bitcoin, FinTech, Smart Contracts,Cryptocurrency,RiskBooksPublisher.

CorrespondingOnlineResources:1. https://www.accenture.com/in-en/insight-blockchain-technology-how-banks-

building-real-time2. https://medium.com/search?q=decentralized%20exchange3. EmergingTechnology Projection: TheTotal Economic Impact™Of IBMBlockchain

https://www.ibm.com/downloads/cas/QJ4XA0MD4. https://www.globallegalinsights.com/practice-areas/blockchain-laws-and-

regulations/india#chaptercontent15. https://www.eduonix.com/blockchain-and-cryptocurrencies-for-beginners6. https://www.coursera.org/learn/cryptocurrency

CourseOutcomes:Aftercompletionofcourse,studentswould:1. To understand difference between different types of coins and tokens related to

blockchaintechnology.2. Tounderstandthebenefitsofblockchaininbankingsector.3. Tounderstandtheconceptofdecentralizedmarkets.

*****

25

CyberSecurity

27

MinorDegreein“CyberSecurity”

CourseStructureS.No. CourseCode Title L T P Credits1 CBS-01 InformationTheoryforCyberSecurity 3 0 2 42 CBS-02 DataEncryption 3 0 2 4

3 CBS-03 Steganography and DigitalWatermarking 3 0 0 3

4 CBS-04 SecurityAssessmentandRiskAnalysis 3 0 0 35 CBS-05 DatabaseSecurityandAccessControl 3 0 2 4

TOTAL 15 0 6 18


• CBSdenotesthatminordegreein“CyberSecurity”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in


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29

DetailedSyllabus

CourseCode : CBS-01CourseTitle : InformationTheoryforCyberSecurityNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : CBSPre-requisites : ProbabilityTheory,ComputerNetworks

CourseObjective:Theobjectiveof thiscourse is toprovidean insight to informationcodingtechniques,errorcorrectionmechanismforcybersecurity.CourseContents:Module1[8Lectures]Shannon’sfoundationofInformationtheory,Randomvariables,Probabilitydistributionfactors,Uncertainty/entropy informationmeasures,Leakage,QuantifyingLeakageandPartitions, Lower bounds on key size: secrecy, authentication and secret sharing.provablesecurity,computationally-secure,symmetriccipher.

Module2[8Lectures]Secrecy,Authentication,Secretsharing,Optimisticresultsonperfectsecrecy,Secretkeyagreement,UnconditionalSecurity,QuantumCryptography,RandomizedCiphers,Typesofcodes:blockcodes,HammingandLeemetrics,descriptionoflinearblockcodes,paritycheckCodes,cycliccode,Maskingtechniques.

Module3[8Lectures]Information-theoretic security and cryptograph, basic introduction to Diffie-Hellman,AES,andside-channelattacks.Module4[10Lectures]Secrecymetrics:strong,weak,semanticsecurity,partialsecrecy,Securesourcecoding:rate-distortiontheoryforsecrecysystems,sideinformationatreceivers,Differentialprivacy,Distributedchannelsynthesis.

Module5[8Lectures]Digital and network forensics, Public Key Infrastructure, Light weight cryptography,EllipticCurveCryptographyandapplications.TextBooks/References:

1. InformationTheoryandCoding,MuralidharKulkarni,KSShivaprakasha,JohnWiley&Sons.

2. CommunicationSystems:Analoganddigital,SinghandSapre,TataMcGrawHill.3. Fundamentalsininformationtheoryandcoding,MonicaBorda,Springer.4. InformationTheory,CodingandCryptographyRBose.5. InformationSecurity&CyberLaws,Gupta&Gupta,KhannaPublishingHouse.6. Multi-mediaSystemDesign,PrabhatKAndleighandKiranThakrar.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Tointroducetheprinciplesandapplicationsofinformationtheory.2. Tojustifyhowinformationismeasuredintermsofprobabilityandentropy.

30

3. Tolearncodingschemes,includingerrorcorrectingcodes.*****

CourseCode : CBS-02CourseTitle : DataEncryptionandCompressionNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : CBSPre-requisites : LinearAlgebra,Cryptography

Course Objective: This course will cover the concept of security, types of attackexperienced, encryption and authentication for deal with attacks, what is datacompression,needandtechniquesofdatacompression.

CourseContents:

Module1[8Lectures]IntroductiontoSecurity:Needforsecurity,Securityapproaches,Principlesofsecurity,Typesofattacks.Encryption Techniques: Plaintext, Cipher text, Substitution & Transpositiontechniques,Encryption&Decryption,Typesofattacks,Keyrange&Size.Module2[6Lectures]Symmetric&AsymmetricKeyCryptography:Algorithmtypes&Modes,DES, IDEA,Differential&LinearCryptanalysis,RSA,Symmetric&Asymmetrickeytogether,Digitalsignature,Knapsackalgorithm.Module3[9Lectures]Case Studies of Cryptography: Denial of service attacks, IP spoofing attacks,Conventional Encryption and Message Confidentiality, Conventional EncryptionAlgorithms,KeyDistribution.Public Key Cryptography and Message Authentication: Approaches to MessageAuthentication, SHA-1, MD5, Public-Key Cryptography Principles, RSA, Digital,Signatures,KeyManagement,Firewall.Module4[7Lectures]Introduction:Needfordatacompression,Fundamentalconceptofdatacompression&coding,Communicationmodel,Compressionratio,Requirementsofdatacompression,Classification.MethodsofDataCompression:Datacompression--Lossless&Lossy.Module5[8Lectures]Entropy encoding-- Repetitive character encoding, Run length encoding, Zero/Blankencoding; Statistical encoding-- Huffman, Arithmetic & Lempel-Ziv coding; Sourceencoding--Vectorquantization(Simplevectorquantization&witherrorterm).Module6[4Lectures]Recenttrendsinencryptionanddatacompressiontechniques.TextBooks/References:

1. CryptographyandNetworkSecurity,MohammadAmjad,JohnWiley&Sons.

31

2. Cryptography&NetworkSecuritybyAtulKahate,TMH.3. InformationTheoryandCoding,MuralidharKulkarni,KSShivaprakasha,JohnWiley

&Sons.4. CryptographyandNetworkSecuritybyB.Forouzan,McGraw-Hill.5. TheDataCompressionBookbyNelson,BPB.6. Cryptography&NetworkSecurity,V.K.Jain,KhannaPublishingHouse.

CourseOutcomes: At the endof this course the studentwill have theknowledgeofplain text, cipher text, RSA and other cryptographic algorithm, Key Distribution,communicationmodel,Variousmodelsfordatacompression.

*****

CourseCode : CBS-03CourseTitle : SteganographyandDigitalWatermarkingNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : CBSPre-requisites : ImageandVideoProcessing,LinearAlgebra

CourseObjective:The objective of course is to provide an insight to steganographytechniques.Watermarkingtechniquesalongwithattacksondatahidingandintegrityofdataisincludedinthiscourse.

CourseContents:

Module1[8Lectures]Steganography: Overview, History, Methods for hiding (text, images, audio, video,speechetc.).Steganalysis:ActiveandMaliciousAttackers,ActiveandpassiveSteganalysis.

Module2[8Lectures]Frameworks for secret communication (pure steganography, secret key, public keysteganography),Steganographyalgorithms(adaptiveandnon-adaptive).

Module3[6Lectures]Steganography techniques: Substitution systems, Spatial Domain, transform domaintechniques,Spreadspectrum,Statisticalsteganography.

Module4[6Lectures]Detection,Distortion,Techniques:LSBEmbedding,LSBSteganalysisusingprimarysets.

Module5[9Lectures]Digital Watermarking: Introduction, Difference between Watermarking andSteganography,Classification(CharacteristicsandApplications),types and techniques (Spatial-domain, Frequency-domain, and Vector quantization-basedwatermarking),Watermarksecurity&authentication.

Module6[5Lectures]RecenttrendsinSteganographyanddigitalwatermarkingtechniques.CasestudyofLSBEmbedding,LSBSteganalysisusingprimarysets.


32

1. PeterWayner,“DisappearingCryptography–InformationHiding:Steganography&Watermarking”,MorganKaufmannPublishers,NewYork,2002.

2. Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom, Jessica Fridrich, TonKalker,“Digital Watermarking and Steganography”, Margan Kaufmann Publishers, NewYork,2008.

3. Information Hiding: Steganography and Watermarking-Attacks andCountermeasuresbyNeilF.Johnson,ZoranDuric,SushilJajodia.

4. Information Hiding Techniques for Steganography and Digital Watermarking byStefanKatzenbeisser,FabienA.P.Petitcolas.

CorrespondingOnlineResources:1. CyberSecurity,https://swayam.gov.in/nd2_cec20_cs09/preview.2. IntroductiontoCyberSecurity,https://swayam.gov.in/nd2_nou20_cs01/preview

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Learntheconceptofinformationhiding.2. Surveyofcurrenttechniquesofsteganographyandlearnhowtodetectandextract

hiddeninformation.3. Learnwatermarkingtechniquesandthroughexamplesunderstandtheconcept.

*****

CourseCode : CBS-04CourseTitle : SecurityAssessmentandRiskAnalysisNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : CBSPre-requisites : ComputerandNetworkSecurityCourseObjective:Describe theconceptsof riskmanagement in informationsecurity.DefineanddifferentiatevariousContingencyPlanningcomponents.Defineandbeableto discuss incident response options, and design an Incident Response Plan forsustainedorganizationaloperations.

CourseContents:

Module1[8Lectures]SECURITY BASICS: Information Security (INFOSEC) Overview: critical informationcharacteristics–availabilityinformationstates–processingsecuritycountermeasures-education, training and awareness, critical informationcharacteristics – confidentiality critical information characteristics – integrity,information states – storage, information states – transmission, securitycountermeasures-policy,proceduresandpractices,threats,vulnerabilities.

Module2[9Lectures]Threats to and Vulnerabilities of Systems: Threats, major categories of threats (e.g.,fraud,HostileIntelligenceService(HOIS).Countermeasures:assessments(e.g.,surveys,inspections).ConceptsofRiskManagement:consequences (e.g., correctiveaction, riskassessment),cost/benefit analysis and implementation of controls, monitoring the efficiency andeffectivenessofcontrols(e.g.,unauthorizedorinadvertentdisclosureofinformation).

33

Module3[7Lectures]SecurityPlanning:directivesandproceduresforpolicymechanism.Contingency Planning/Disaster Recovery: agency response procedures and continuityof operations, contingency plan components, determination of backup requirements,developmentofplansforrecoveryactionsafteradisruptiveevent.

Module4[8Lectures]Personnel SecurityPractices andProcedures: access authorization/verification (need-to-know), contractors, employee clearances, position sensitivity, security training andawareness,systemsmaintenancepersonnel.Auditing and Monitoring: conducting security reviews, effectiveness of securityprograms,investigationofsecuritybreaches,privacyreviewofaccountabilitycontrols,reviewofaudittrailsandlogs.

Module5[7Lectures]Operations Security (OPSEC): OPSEC surveys/OPSEC planning INFOSEC: computersecurity–audit,cryptography-encryption(e.g.,point-to-point,network,link).

Module6[3Lectures]Casestudyofthreatandvulnerabilityassessment.TextBooks/References:1. InformationSystemsSecurity,2ed:SecurityManagement,Metrics,Frameworksand

BestPractices,NinaGodbole,JohnWiley&Sons.2. PrinciplesofIncidentResponseandDisasterRecovery,Whitman&Mattord,Course

TechnologyISBN:141883663X.

CorrespondingOnlineResources:1. IntroductiontoCyberSecurity,https://swayam.gov.in/nd2_nou20_cs01/preview2. (WebLink)http://www.cnss.gov/Assets/pdf/nstissi_4011.pdfCourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Toapply contingency strategies includingdatabackupand recoveryandalternate

siteselectionforbusinessresumptionplanning2. ToSkilledtobeabletodescribetheescalationprocessfromincidenttodisasterin

caseofsecuritydisaster.3. ToDesignaDisasterRecoveryPlanforsustainedorganizationaloperations.

*****

CourseCode : CBS-05CourseTitle : DatabaseSecurityandAccessControlNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : CBSPre-requisites : DatabaseManagement

CourseObjective:Theobjectiveof thecourse is toprovidefundamentalsofdatabasesecurity. Various access control techniques mechanisms were introduced along withapplicationareasofaccesscontroltechniques.

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CourseContents:

Module1[7Lectures]IntroductiontoAccessControl,Purposeandfundamentalsofaccesscontrol.Module2[8Lectures]Policies of Access Control, Models of Access Control, and Mechanisms, DiscretionaryAccess Control (DAC), Non- Discretionary Access Control, Mandatory Access Control(MAC).CapabilitiesandLimitationsofAccessControlMechanisms:AccessControlList(ACL)andLimitations,CapabilityListandLimitations.Module3[10Lectures]Role-Based Access Control (RBAC) and Limitations, Core RBAC, Hierarchical RBAC,Statically Constrained RBAC, Dynamically Constrained RBAC, Limitations of RBAC.Comparing RBAC to DAC and MAC Access Control policy, Integrating RBAC withenterpriseITinfrastructures:RBACforWFMSs,RBACforUNIXandJAVAenvironments.Module5[8Lectures]Smart Card based Information Security, Smart card operating system-fundamentals,design and implantation principles, memory organization, smart card files, filemanagement.PPSSecuritytechniques-user identification,smartcardsecurity,qualityassuranceandtesting,smartcardlifecycle-5phases,smartcardterminals.

Module6[9Lectures]CloudDataSecurity:RecenttrendsinDatabasesecurityandaccesscontrolmechanisms.CloudDataAudit:Intro,Audit,BestPractice,Keymanagement,CloudKeyManagementAudit.


1. Role Based Access Control: David F. Ferraiolo, D. Richard Kuhn, RamaswamyChandramouli.

CorrespondingOnlineResources:1. http://www.smartcard.co.uk/tutorials/sct-itsc.pdf:SmartCardTutorial.2. Advanced System Security Topics, https://www.coursera.org/lecture/advanced-

system-security-topics/role-based-access-control-rbac-bYvzS.

CourseOutcomes:Aftercompletionofthiscourse,thestudentswillbeenable:

1. Tounderstandandimplementclassicalmodelsandalgorithms.2. To analyze the data, identify the problems, and choose the relevant models and

algorithmstoapply.3. To assess the strengths and weaknesses of various access control models and to

analyzetheirbehaviour.

*****

35

DataScience

37

MinorDegreein“DataScience”

CourseStructureS.No. CourseCode Title L T P Credits1 DAS-01 IntroductiontoDataScience 3 0 2 42 DAS-02 IntroductiontoAIandML 3 0 2 43 DAS-03 ComputationalDataanalytics 3 0 2 44 DAS-04 WebDataMining 3 0 0 3

5 DAS-05 Analysing, Visualizing and Applyingdatasciencewithpython 3 0 2 4

TOTAL 15 0 8 19


• DASdenotesthatminordegreein“DataScience”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in


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39

DetailedSyllabus

CourseCode : DAS-01CourseTitle : IntroductiontoDataScienceNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : DAS

CourseObjective:

• ToProvidetheknowledgeandexpertisetobecomeaproficientdatascientist;• Demonstrateanunderstandingofstatisticsandmachinelearningconceptsthat

arevitalfordatascience;• ProducePythoncodetostatisticallyanalyseadataset;• Critically evaluate data visualisations based on their design and use for

communicatingstoriesfromdata;

CourseContents:

Module1:[7Lectures]Introduction to Data Science, Different Sectors using Data science, Purpose andComponentsofPythoninDataScience.

Module2:[7Lectures]Data Analytics Process, Knowledge Check, Exploratory Data Analysis (EDA), EDA-Quantitative technique, EDA- Graphical Technique, Data Analytics Conclusion andPredictions.

Module3:[11Lectures]FeatureGenerationandFeatureSelection(ExtractingMeaning fromData)-Motivatingapplication: user (customer) retention- Feature Generation (brainstorming, role ofdomainexpertise,andplaceforimagination)-FeatureSelectionalgorithms.

Module4:[10Lectures]DataVisualization-Basicprinciples, ideasandtoolsfordatavisualization,Examplesofinspiring (industry) projects- Exercise: create your own visualization of a complexdataset.

Module5:[7Lectures]Applications ofData Science,Data Science andEthical Issues-Discussions onprivacy,security,ethics-AlookbackatDataScience-Next-generationdatascientists.

LabWork:1.PythonEnvironmentsetupandEssentials.2.MathematicalcomputingwithPython(NumPy).3.ScientificComputingwithPython(SciPy).4.DataManipulationwithPandas.5.PredictionusingScikit-Learn6.DataVisualizationinpythonusingmatplotlib

40

TextBooks/References:1. DataSciences&Analytics,V.K.Jain,KhannaPublishingHouse.2. BusinessAnalytics:TheScienceofData-DrivenDecisionMaking,UDineshKumar,

JohnWiley&Sons.3. IntroducingDataScience:BigData,MachineLearning,andMore,UsingPython

Tools,DavyCielen,JohnWiley&Sons.4. JoelGrus,DataSciencefromScratch,ShroffPublisher/O’ReillyPublisherMedia5. AnnalynNg,KennethSoo,Numsense!DataSciencefortheLayman,ShroffPublisher

Publisher6. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The

Frontline.O’ReillyPublisher.7. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets.

v2.1,CambridgeUniversityPress.8. JakeVanderPlas,PythonDataScienceHandbook,ShroffPublisher/O’ReillyPublisher

Media.9. Philipp Janert, Data Analysis with Open Source Tools, Shroff Publisher/O’Reilly

PublisherMedia.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Toexplainhowdataiscollected,managedandstoredfordatascience;2. To understand the key concepts in data science, including their real-world

applicationsandthetoolkitusedbydatascientists;3. ToimplementdatacollectionandmanagementscriptsusingMongoDB.

*****

CourseCode : DAS-02CourseTitle : IntroductiontoAIandMLNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : DAS

CourseObjective:

• Tounderstandbasicsofmachinelearningindatascience.• To understand various basic machine learning algorithm that can be used with

varioustypeofdata.CourseContents:

Module1:[6Lectures]LinearRegression:Basicfactsoflinearregression,implementationoflinearregression,casestudiesoflinearregressionusingdataset

Module2:[8Lectures]LogisticRegression:Basicfactsandimplementationof logisticregression,solveacasestudytopredictoutputusingexistingdataset

Module3:[11Lectures]

41

ClusteringandPrincipleComponentAnalysis:Kmeansandhierarchicalclustering,howtomakemarketstrategiesusingclustering,recommendationandPCA

Module4:[9Lectures]Support Vector Machine: basics of SVM and use it to detect the spam emails andrecognizealphabets

Module5:[8Lectures]ModelSelectionandadvancedregression:useofLassoandRidge

LabWork:1. Use python to predict employee attrition in a firm and help them plan their

manpower.(takedatasetfromkaggle).2. Createcustomerclustersusingdifferentmarketstrategiesonadataset.3. Makeamovierecommendationsystem.4. Develop a predictionmechanism to predictwhich employee can go on leave in a

companyinnearfuture.5. RecognizingalphabetsusingSVM.


1. MachineLearningusingPython ,UDineshKumarand ManaranjanPradhan, JohnWiley&Sons.

2. A Classical Approach to Artificial Intelligence, M.C. Trivedi, Khanna PublishingHouse.

3. MachineLearning,V.K.Jain,KhannaPublishingHouse.4. AdvancedDataAnalyticsUsingPython:WithMachineLearning,DeepLearningby

BySayanMukhopadhyay,Apress.5. PracticalDataMining”byMonteF.Hancock,AuerbachPublication.6. “Machine Learning for Absolute Beginners: A Plain English Introduction (Second

Edition)”byOliverTheobald.7. PracticalDataSciencewithR,NinaZumel,JohnWiley&Sons.8. PythonforDataScienceforDummies,JohnPaulMueller,LucaMassaron,JohnWiley

&Sons.9. Big Data and Analytics, Seema Acharya and Subhashini Chellappan, Wiley

Publication.10. IntroductiontoMachineLearning,JeevaJose,KhannaPublishingHouse.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Toexplainhowdataiscollected,managedandstoredfordatascience;2. TousevarioustypeofMachinelearningmodel3. ToimplementvariousMLalgorithmsondatamodels

42

*****

CourseCode : DAS-03CourseTitle : ComputationalDataAnalyticsNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : DAS

CourseObjective:

• Tolearnhowtothinkaboutyourstudysystemandresearchquestionofinterestinasystematicway inorder todesignanefficient samplingandexperimental researchprogram.

• Tounderstandhowtoanalyzecollecteddatatoderivethemostinformationpossibleaboutyourresearchquestions.

CourseContents:

Module1:[6Lectures]Introduction to R Computing language. Best practices in executing ReproducibleResearch in data science, Sampling and Simulation. Descriptive statistics, and thecreationofgoodobservationalsamplingdesigns.Module2:[8Lectures]Datavisualization,Dataimportandvisualization,IntroductiontovariousplotsModule3:[10Lectures]FrequentistHypothesisTesting,Z-Tests,PowerAnalysisModule4:[10Lectures]Linear regression,diagnostics, visualization,Likelihoodist Inference,Fittinga linewithLikelihood,ModelSelectionwithonepredictorModule5:[8Lectures]Bayesian Inference, Fitting a line with Bayesian techniques, Multiple Regression andInteractionEffects,InformationTheoreticApproachesLabWork:1. TogiveabasicinsightofRanditsvariouslibraries.2. LibrariesinR.RasaDataImportingTool,Dplyr.Forcats.3. SimulationandFrequentistHypothesistesting,SimulationandPower.4. BayesiancomputationinR,FittingalinewithBayesiantechniques.


1. Beginner’sGuideforDataAnalysisusingRProgramming,KhannaPublishingHouse2. PracticalDataSciencewithR,NinaZumel,JohnWiley&Sons.3. BigData&Hadoop,V.K.Jain,KhannaPublishingHouse.4. N. C. Das, Experimental Designs in Data Science with Least Resources, Shroff

PublisherPublisher.

43

5. Hadley Wickham, Garret Grolemund, R for Data Science, Shroff Publisher/O’ReillyPublisherPublisher

6. BenjaminM. Bolker.Ecological Models and Data in R. Princeton University Press,2008.ISBN978-0-691-12522-0.

7. JohnFoxandSanfordWeisberg.AnRCompaniontoAppliedRegression.SagePublications,ThousandOaks,CA,USA,secondedition,2011.ISBN978-1-4129-7514-8.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Explainhowdataiscollected,managedandstoredfordatascience;2. WhentousewhichtypeofMachinelearningmodel.3. ImplementvariousMLalgorithmsondatamodels.

*****

CourseCode : DAS-04CourseTitle : WebDataMiningNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : DAS

CourseObjective:

• TolearnhowtoextractdatafromtheWeb.• Tounderstandhowtoanalyzecollecteddatatoderivethemostinformation

CourseContents:

Module1:[6Lectures]

Introduction to internet and WWW, Data Mining Foundations, Association Rules andSequential Patterns, Basic Concepts of Association Rules, Apriori Algorithm, FrequentItemset Generation, Association Rule Generation, Data Formats for Association RuleMining, Mining with multiple minimum supports, Extended Model, Mining Algorithm,RuleGeneration

Module2:[8Lectures]

MiningClassAssociationRules,BasicConceptsofSequentialPatterns,MiningSequentialPatterns on GSP, Mining Sequential Patterns on Prefix Span, Generating Rules fromSequentialPatterns


ConceptsofInformationRetrieval,IRMethods,BooleanModel,VectorSpaceModelandStatistical Language Model, Relevance Feedback, Evaluation Measures, Text and WebPagePre-processing,StopwordRemoval,Stemming,WebPagePreprocessing,DuplicateDetection, Inverted Index and Its Compression, Inverted Index, Search using InvertedIndex,IndexConstruction,IndexCompression,LatentSemanticIndexing,SingularValueDecomposition,QueryandRetrieval,WebSearch,MetaSearch,WebSpamming.

44


Link Analysis, Social Network Analysis, Co-Citation and Bibliographic Coupling, PageRank Algorithm, HITS Algorithm, CommModuley Discovery, Problem Definition,Bipartite Core CommModuleies, Maximum Flow CommModuleies, EmailCommModuleies,Web Crawling, A Basic Crawler Algorithm – Breadth First Crawlers,Preferential Crawlers, Implementation Issues – Fetching, Parsing, Stopword Removal,Link Extraction, Spider Traps, PageRepository, Universal Crawlers, Focused Crawlers,TopicalCrawlers,CrawlerEthicsandConflicts.

Module5:[8Lectures]

Opinion Mining, Sentiment Classification, Classification based on Sentiment Phrases,Classification Using Text Classification Methods, Feature based Opinion Mining andSummarization,ProblemDefinition,Objectfeatureextraction,ComparativeSentenceandRelationMining,OpinionSearchandOpinionSpam.WebUsageMining,DataCollectionand Preprocessing, Sources and Types of Data, Key Elements of Web Usage DataPreprocessing,DataModelingforWebUsageMining,DiscoveryandAnalysisofWeb

UsagePatterns,SessionandVisitorAnalysis,ClusterAnalysisandVisitorSegmentation,AssociationandCorrelationAnalysis,AnalysisofSequentialandNavigationPatterns.


1. MiningtheWeb:DiscoveringKnowledgefromHypertextData,SoumenChakrabarti,MorganKaufmannPublishers.

2. BingLiu,WebDataMining:ExploringHyperlinks,Contents,andUsageData,SpringerPublications,2011.

3. Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques, SecondEdition,ElsevierPublications2010.

4. AnthonyScime,WebMining:ApplicationsandTechniques,2005.5. Kowalski, Gerald, Mark T Maybury: Information Retrieval Systems: Theory and

Implementation,KluwerAcademicPress,1997.6. Mathew Russell, Mining the Social Web 2nd Edition, Shroff Publisher/O’Reilly

PublisherPublication.7. Data Mining and Data Warehousing Principles and Practical Techniques, Parteek

Bhatia,CambridgeUniversityPress.8. DataMining&BusinessIntelligence,BalramKrishan,KhannaPublishingHouse

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:

1. ToexplainhowdataiscanbecollectedfromtheWeb.2. Toextractdataandinformationfromthewebpages.3. Tomakedecisionbasedonthedatacollected.

*****

CourseCode : DAS-05CourseTitle : Analysing,VisualizingandApplyingdatasciencewithpythonNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : DAS

45

CourseObjective:

• Tolearnhowtousepythonfordatascience.• Tounderstandanduseallthetoolsandlibrariesofpythonfordatascience.

CourseContents:

Module1:[6Lectures]DataAnalysislibraries:willlearntousePandasDataFrames,Numpymulti-dimentionalarrays,andSciPylibrariestoworkwithavariousdataset.

Module2:[8Lectures]Pandas, an open-source library, and we will use it to load, manipulate, analyze, andvisualizevariousdatasets.

Module3:[10Lectures]Scikit-learn, andwewill use some of itsmachine learning algorithms to build smartmodelsandmakepredictions,variousparametersthatcanbeusedtocomparevariousparameters.

Module4:[10Lectures]Descriptive Statistics, Basic of Grouping, ANOVA, Correlation, Polynomial Regressionand Pipelines, R-squared andMSE for In-Sample Evaluation, Prediction and DecisionMaking

Module5:[10Lectures]GridSearch,ModelRefinement,Binning,Indicatorvariables

LabWork:1. DemonstrateknowledgeofDataScienceandMachineLearning.2. ApplyDataScienceprocesstoareallifescenario.3. ExploreNewYorkCity-311ComplaintsandHousingdatasets.4. AnalyzeandVisualizedatausingPython.5. PerformfeatureengineeringexerciseusingPython.6. BuildandvalidatepredictivemachinelearningmodelusingPython.7. CreateandshareActionableInsightstoreallifedataproblems.

TextBooks/References:1. TamingPythonbyProgramming,JeevaJose,KhannaPublishingHouse.2. DataVisualizationwithPythonandJavaScript,KyranDale,ShroffPublisher/O’Reilly

PublisherPublication.3. DataScienceUsingPythonandRbyChantalD.LaroseandDanielT. Larose,Wiley

Publication.4. Data Science & Analytics (with Python, R, SPSS Programming), V.K. Jain, Khanna

PublishingHouse.5. PythonforDataScienceandVisualization-BeginnerstoPro,Udemy.CourseOutcomes:Aftercompletionofcourse,studentswould:1. ToexplainhowdataiscanbecollectedfromtheWeb.2. Toextractdataandinformationfromthewebpages.3. Tomakedecisionbasedonthedatacollected.

46

*****

47

InternetofThings(IoT)

49

MinorDegreein“InternetofThings”

CourseStructureS.No. CourseCode Title L T P Credits1 IoT-01 IntroductiontoInternetofThings 3 0 2 4

2 IoT-02 Introduction to Security of Cyber-PhysicalSystems 3 0 2 4

3 IoT-03 Ubiquitous Sensing, Computing andCommunication 3 0 2 4

4 IoT-04 EmbeddedSystemsforIoT 3 0 0 3

5 IoT-05 IoT with Arduino, ESP, and RaspberryPi 3 0 2 4

TOTAL 15 0 8 19


• IoTdenotesthatminordegreein“InternetofThings”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in


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51

DetailedSyllabus

CourseCode : IoT-01CourseTitle : IntroductiontoInternetofThingsNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : IoT

CourseObjective:

• TomakestudentsknowtheIoTecosystem.• Toprovideanunderstandingof the technologiesand thestandardsrelating to the

InternetofThings.• TodevelopskillsonIoTtechnicalplanning.

CourseContents:

Module1[8Lectures]IoT&WebTechnology:TheInternetofThingsToday,TimeforConvergence,Towardsthe IoT Universe, Internet of Things Vision, IoT Strategic Research and InnovationDirections, IoT Applications, Future Internet Technologies, Infrastructure, Networksand Communication, Processes, Data Management, Security, Privacy & Trust, DeviceLevel Energy Issues, IoT Related Standardization, Recommendations on ResearchTopics.

Module2[9Lectures]M2MtoIoT–ABasicPerspective–Introduction,SomeDefinitions,M2MValueChains,IoT Value Chains, an emerging industrial structure for IoT, the international drivenglobal value chain and global information monopolies. M2M to IoT-An ArchitecturalOverview–Buildinganarchitecture,Maindesignprinciplesandneededcapabilities,AnIoTarchitectureoutline,standardsconsiderations.

Module3[9Lectures]IoTArchitecture-StateoftheArt–Introduction,Stateoftheart,ArchitectureReferenceModel- Introduction, Reference Model and architecture, IoT reference Model, IoTReferenceArchitecture- Introduction,FunctionalView, InformationView,DeploymentandOperationalView,OtherRelevantarchitecturalviews.

Module4[8Lectures]IoTApplicationsforValueCreationsIntroduction,IoTapplicationsforindustry:FutureFactory Concepts, Brownfield IoT, SmartObjects, SmartApplications, FourAspects inyour Business toMaster IoT, Value Creation from Big Data and Serialization, IoT forRetailingIndustry,IoTforOilandGasIndustry,OpinionsonIoTApplicationandValueforIndustry,HomeManagement,eHealth.

Module5[8Lectures]Internet of Things Privacy, Security and Governance Introduction, Overview ofGovernance, Privacy and Security Issues, Contribution from FP7 Projects, Security,PrivacyandTrustinIoT-Data-PlatformsforSmartCities,FirstStepsTowardsaSecurePlatform,SmartApproach.DataAggregationfortheIoTinSmartCities,Security.

52


1. Dr.JeevaJose,InternetofThings,KhannaPublishingHouse.2. NiteshDhanjani,AbusingtheInternetofThings,ShroffPublisher/O’ReillyPublisher.3. Internet of Things, RMD Sundaram Shriram K Vasudevan, Abhishek S Nagarajan,

JohnWileyandSons.4. InternetofThings,ShriramKVasudevan,AbhishekSNagarajan,RMDSundaram,

JohnWiley&Sons.5. CunoPfister, “GettingStartedwith the InternetofThings”,ShroffPublisher/Maker

Media.6. Francis daCosta, “Rethinking the Internet of Things: A Scalable Approach to

ConnectingEverything”,1stEdition,ApressPublications.7. Massimo Banzi, Michael Shiloh Make: Getting Started with the Arduino, Shroff

Publisher/MakerMediaPublishers.CorrespondingOnlineResources:1. https://www.coursera.org/specializations/internet-of-things

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:

1. TounderstandthetechnologyandstandardsrelatingtoIoTs.2. TounderstandthecriticalecosystemrequiredtomainstreamIoTs.3. To Acquire skills on developing their own national and enterprise level technical

strategies.

*****

CourseCode : IoT-02CourseTitle : IntroductiontoSecurityofCyber-PhysicalSystemsNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : IoT

CourseObjective:

• Tolearnthebasicsofsecurityandvarioustypesofsecurityissues.• Tostudydifferentcryptographytechniquesavailableandvarioussecurityattacks.• Explorenetworksecurityandhowtheyareimplementedinrealworld.• TogetaninsightofvariousissuesofWebsecurityandbiometricauthentication.

CourseContents:

Module1[6Lectures]OverviewofSecurityandPrivacyinInformationSystem.

Module2[10Lectures]AppliedCryptography&IntrusionDetection,ArchitectureofAppliedCryptography,OneWay Hash Function and Integrity, Encryption Algorithms and Confidentiality, DigitalSignature andAuthentication (DH,RSA, 2 class), IntrusionDetection and InformationTheory.

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Module3[10Lectures]InternetofThingsSecurity,SecurityandPrivacyforIoTCaseStudy:SmartHome,SmartGridNetwork,ModernVehicle,WearableComputing&BYOD,MobileHealthCare.

Module4[8Lectures]Software-Defined Networks, Introduction of Software-Defined Networks, Security forSoftware-Defined Networks, Privacy Leakages for Software-Defined Networks, CaseStudies:HowtoAttackSoftware-DefinedNetworks.

Module5[8Lectures]Cyber-PhysicalSystems(CPS),CPS-Platformcomponents,CPSimplementationissues,IntelligentCPS,SecureDeploymentofCPS.TextBooks/References:

1. CyberSecurity,NinaGodbole,JohnWiley&Sons.2. LiDaXu,ShancangLi,“SecuringtheInternetofThings”,Syngress.3. AlasdairGilchrist,“IoTSecurityIssues”,DeGruyter4. SeanSmith,“TheInternetofRiskyThings”,SeanSmith,ShroffPublisher/O’Reilly

Publisher5. Dr.JeevaJose,InternetofThings,KhannaPublishingHouse.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. ToApplybasicsofsecurityandissuesrelatedtoit.2. Tousebiometrictechniquesavailableandhowtheyareusedintoday’sworld.3. ToinvestigateSecurityissuesinwebandhowtotacklethem.4. ToLearnmechanismsfortransportandnetworksecurity

*****

CourseCode : IoT-03CourseTitle : UbiquitousSensing,ComputingandCommunicationNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : IoT

CourseObjective:

• Basic introduction of all the elements of IoT-Mechanical, Electronics/sensorplatform,Wirelessandwirelineprotocols,MobiletoElectronicsintegration,Mobiletoenterpriseintegration.

• Tohaveanunderstandingofbasicsofopensource/commercialelectronicsplatformforIoT.

• To have an understanding of basics of open source /commercial enterprise cloudplatformforIoT.

CourseContents:

Module1Introduction,Overview,ChallengesinIoT,NetworkingBasicsofIoT,NFC,WirelessLAN.

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Module2Location in ubiquitous computing: Personal assistants, Location aware computing,Locationtracking,Architecture,Locationbasedserviceandapplications,Locationbasedsocialnetworks(LBSN),LBSNRecommendation.Context-aware computing: Context and Context-aware Computing, Issues andChallenges,DevelopingContext-awareApplications,SystemArchitecture.

Module3Privacy and security in ubiquitous computing, Energy constraints in ubiquitouscomputing.Wearable computing, Glass and Augmented Reality, Eye-Tracking, Digital Pen andPaper,Mobilesocialnetworking&crowdsensing,Eventbasedsocialnetwork.

Module4Mobileaffectivecomputing:HumanActivityandEmotionSensing,HealthApps,Mobilep2pcomputing,SmartHomesand IntelligentBuildings,MobileHCI,Cloudcentric IoT,Openchallenges,Architecture,EnergyEfficiency,Participatorysensing,Protocols,QoS,QoE.

Module5IoTanddata analytics IoTandDataManagement,Data cleaning andprocessing,Datastoragemodels.Searchtechniques,DeepWeb,Semanticsensorweb,SemanticWebDataManagement,SearchinginIoT.Real-time and Big Data Analytics for The Internet of Things, Heterogeneous DataProcessing,High-dimensionalDataProcessing,ParallelandDistributedDataProcessing.TextBooks/References:

1. N.Jeyanthi,AjithAbraham,HamidMcheick,“UbiquitousComputingandComputingSecurityofIoT”.

2. JohnKrumm,UbiquitousComputingFundamentals,CRCPress.3. DirkSlama,“EnterpriseIoT”,ShroffPublisher/O’ReillyPublisher4. Dr.JeevaJose,InternetofThings,KhannaPublishingHouse.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. To understand merging technological options, platforms and case studies of IoT

implementationinhome&cityautomation.2. TodeterminetheMarketperspectiveofIoT.

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*****

CourseCode : IoT-04CourseTitle : EmbeddedSystemsforIoTNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : IoTCourseObjective:

• Tomakestudentsknowthebasicconceptandarchitectureofembeddedsystems.• DifferentdesignplatformsusedforanembeddedsystemforIoTapplications.• TohaveknowledgeabouttheIoTenabledtechnology.

CourseContents:

Module1[7Lectures]Purpose and requirement specification, IoT level specification, Functional viewspecification,Operationalviewspecification,Deviceandcomponentintegration,PillarsofEmbeddedIoTandPhysicalDevices:Theinternetofdevices.

Module2[8Lectures]Design of Embedded Systems: Common Sensors, Actuators, Embedded Processors,MemoryArchitectures,Softwarearchitecture.

Module3[7Lectures]Inputs andOutputs:Digital Inputs andOutputs,Digital Inputs,DigitalOutputs, BusIn,BusOut,andBusInOut,AnalogInputsandOutputs,AnalogInputs,AnalogOutputs,PulseWidth Modulation (PWM), Accelerometer and Magnetometer, SD Card, Local FileSystem(LPC1768).

Module4[10Lectures]IoT Enabling Technologies: Communications, RFID and NFC (Near-FieldCommunication), Bluetooth LowEnergy (BLE), LiFi, 6LowPAN, ZigBee, Z-Wave, LoRa,Protocols, HTTP,WebSocket, MQTT, CoAP, XMPP, Node-RED, Platforms, IBMWatsonIoT—Bluemix, Eclipse IoT, AWS IoT, Microsoft Azure IoT Suite, Google Cloud IoT,ThingWorx,GEPredix,Xively,macchina.io,Carriots.

Module5[10Lectures]Web of Things and Cloud of Things: Web of Things versus Internet of Things, TwoPillarsoftheWeb,ArchitectureStandardizationforWoT,PlatformMiddlewareforWoT,CloudofThings.IoTPhysicalServers,

Cloud Offerings and IoT Case Studies: Introduction to Cloud Storage Models,CommunicationAPI.TextBooks/References:

1. Dr.JeevaJose,InternetofThings,KhannaPublishingHouse.2. RMDSundaramShriramKVasudevan,AbhishekSNagarajan,InternetofThings,

JohnWileyandSons.3. KlausElk,“EmbeddedSoftwarefortheIoT”.

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4. PerryXiao,“DesigningEmbeddedSystemsandtheInternetofThings(IoT)withtheARMMbed”.

5. ElizabethGootmanet.al,“DesigningConnectedProducts”,ShroffPublisher/O’ReillyPublisher.

CorrespondingOnlineResources:1. Introduction to the Internet of Things and Embedded Systems,

https://www.coursera.org/learn/iot

CourseOutcomes:Aftercompletionofcourse,studentswouldbeableto:1. Understandtheembeddedsystemconceptsandarchitectureofembeddedsystems.2. Understand the different hardware/software co-design techniques for

microcontroller-basedembeddedsystems,applytechniquesinIoTapplications.3. Tobeabletodesignweb/cloudbasedIoTapplications.

*****

CourseCode : IoT-05CourseTitle : IoTwithArduino,ESP,andRaspberryPiNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : IoTCourseObjective:

• Togivestudentshands-onexperienceusingdifferentIoTarchitectures.• To provide skills for interfacing sensors and actuators with different IoT

architectures.• Todevelopskillsondatacollectionandlogginginthecloud.

CourseContents:

Module1[5Lectures]IoT- introductionand its components, IoTbuildingblocks,SensorsandActuators, IoTDevices,IoTboards(ArduinoUno,ESP8266-12ENodeMCU,andRaspberryPi3).

Module2[10Lectures]Arduino Uno – getting started with the Uno boards, blink program, connection ofsensors to the Uno board, reading values of sensors from the Uno board, interrupts.Case study: Temperature/Humidity Control; Case Study: Sending valuesTemperature/HumidityvaluestotheInternetviaGSMmodule.

Module3[10Lectures]ESP 8266-12E Node MCU – getting started with the ESP board, Micropython andEsplorerIDE,FlushingtheESP8266boardwithmicropython,connectingsensorstotheESPboard, ConnectingESPboard toWiFi, InterfacingESPwith theCloud (RESTAPI-GET,POST,MQTT), interrupts,comparisonofESP32boardwith theESP8266board.Case Study: Switching light on /off remotely. Case Study: Voice-based Home

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Automationforswitchinglightson/off(Androidphone–GoogleAssistant(Assistant<->IFTTT),MQTT(ESP<->IFTTT),ESP8266<->Lights).

Module4[8Lectures]RaspberryPi 3 -Rpi3 introduction and installing theRaspbian StretchOS,Headless -Computer and Rpi3 configuration to connect through SSH via Ethernet, Headless -connectingRpi3remotelywithoutEthernetcableviaSSH,IPaddress,Rpi3-TestingtheGPIOpinsthroughScripts.

Module5[9Lectures]Raspberrypi3interfacingwithSensorDHT11,Raspberrypi3pythonlibraryinstallandreadingsensorfeed,'Plugandplay'typecloudplatformoverviewforintegrationtoIOTdevices, 'Plug andplay' cloudplatform for integration to IOTdevice - actuator (LED),Plug andplay platform - Customwidget (DHT11-Sensor) integration throughPython.New - Raspeberry Pi 4 Vs Raspberry Pi3 Mobel B Comparison, LoRawan /LPWAN –Overview.


1. Dr.JeevaJose,InternetofThings,KhannaPublishingHouse.2. Rao,M.(2018).InternetofThingswithRaspberryPi3:Leveragethepowerof

RaspberryPi3andJavaScripttobuildexcitingIoTprojects.PacktPublishingLtd3. Baichtal,J.(2013).Arduinoforbeginners:essentialskillseverymakerneeds.Pearson

Education.4. Schwartz,M.(2016).InternetofThingswithESP8266.PacktPublishingLtd.5. Richardson, M., & Wallace, S. (2012).Getting started with raspberry PI. " O’Reilly

PublisherMedia,Inc."

Software/HardwareRequirements:

Python,IOTboards-ArduinoUNO,NODEMCUESP8266,RaspberryPI3,Fewresistors,potentiometer(5K~10KOHM),breadboard,LEDs,DHT11sensor.

CourseOutcomes:Aftercompletionofcourse,studentswould:

1. TounderstandArduinoUno,NODEMCU8266andRaspberryPIalongwithcriticalprotocolsanditscommunicationtocloud.

2. ToapplycommonlyusedIOTprotocolssuchasRESTAPI,MQTTthroughIOTbaseddemonstration.

3. TosolveanalogsensoranddigitalsensorInterfacingwithIOTdevices.

*****

59

Robotics

61

MinorDegreein“Robotics”

CourseStructureS.No. CourseCode Title L T P Credits1 ROB-01 IntroductiontoRobotics 3 1 0 42 ROB-02 MechanicsofRobots 3 0 0 33 ROB-03 Microprocessor&EmbeddedSystems 3 0 2 44 ROB-04 ControlofRoboticSystems 3 0 0 35 ROB-05 ProjectinRobotics 1 0 6 4

TOTAL 13 1 8 18


• ROBdenotesthatminordegreein“Robotics”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in


• It is preferable to take ROB-05 after completing all previous courses or atleastaftercompletingROB-01,ROB-02,ROB-03,inparallelwithROB-04.

---------------------------------------------------------------------------------------------------------------

63

DetailedSyllabus

CourseCode : ROB-01CourseTitle : IntroductiontoRoboticsNumberofCredits : 4(L:3;T:1;P:0)CourseCategory : ROB

CourseObjective:Thiscourseaimstofamiliarisestudentswithbasicterminologiesofthe robotics sciences and essential knowledge required to get started in the field ofRobotics.

CourseContents:

Module1:Introductiontorobotics:BriefHistory,BasicConceptsofRoboticssuchasDefinition , Three laws, Elements of Robotic Systems i.e. Robot anatomy, DOF,Misunderstood devices etc., Classification of Robotic systems on the basis of variousparameters such as work volume, type of drive, etc., Associated parameters i.e.resolution,accuracy,repeatability,dexterity,compliance,RCCdeviceetc., Introductionto Principles & Strategies of Automation, Types & Levels of Automations, Need ofautomation,Industrialapplicationsofrobot.Module 2: Grippers and Sensors for Robotics: Grippers for Robotics - Types ofGrippers, Guidelines for design for robotic gripper, Force analysis for various basicgrippersystem.SensorsforRobots-TypesofSensorsusedinRobotics,Classificationandapplicationsof sensors, Characteristics of sensing devices, Selections of sensors. Need for sensorsandvisionsystemintheworkingandcontrolofarobot.Module 3: Drives and Control for Robotics: Drive - Types of Drives, Types oftransmission systems, Actuators and its selection while designing a robot system.ControlSystems:TypesofControllers,IntroductiontoclosedloopcontrolModule4:ProgrammingandLanguagesforRobotics:RobotProgramming:Methodsof robot programming, WAIT, SIGNAL and DELAY commands, subroutines,Programming Languages: Generations of Robotic Languages, Introduction to varioustypessuchasVAL,RAIL,AML,Python,ROSetc.,DevelopmentoflanguagessinceWAVEtillROS.Module 5: Related Topics in Robotics: Socio-Economic aspect of robotisation.Economical aspects for robot design, Safety for robot and standards, Introduction toArtificial Intelligence,AI techniques,NeedandapplicationofAI,New trends& recentupdatesinrobotics.

TextBooks/References:1. S.K.Saha,IntroductiontoRobotics2e,TATAMcGrawHillsEducation(2014)2. Asitava Ghoshal, Robotics: Fundamental concepts and analysis, Oxford University

Press(2006)3. DilipKumarPratihar,FundamentalsofRobotics,NarosaPublishingHouse,(2019)

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4. R.K.Mittal,I.J.Nagrath,RoboticsandControl,TATAMcGrawHillPublishingCoLtd,NewDelhi(2003)

5. S.B.Niku,IntroductiontoRobotics–Analysis,Contro,Applications,3rdedition,JohnWiley&SonsLtd.,(2020)

6. J. Angeles, Fundamentals of Robotic Mechanical Systems Theory Methods andAlgorithms,Springer(1997)

7. Mikell Groover, Mitchell Weiss, Roger N. Nagel, Nicholas Odrey, Ashish Dutta,IndustrialRobotics2ndedition,SIE,McGrawHillEducation(India)PvtLtd(2012)

8. R.D.Klafter,ThomasA.Chmielewski,andMechaelNegin,RoboticEngineering–AnIntegratedApproach,EEE,PrenticeHallIndia,PearsonEducationInc.(2009)

AlternativeSWAYAM/NPTELCourse:

NPTELCourseName Instructor HostInstitute

Introductiontorobotics

Dr.KrishnaVasudevan,Dr.BalaramanRavindran,Dr.TAsokan

IITMadras

SensorsandActuators Prof.HardikJeetendraPandya IIScBangalore

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. ToexpresshisviewsasperterminologiesrelatedtoRoboticstechnology.2. Toapplylogicforselectionofroboticsubsystemsandsystems.3. Toanalysebasicsofprincipalsofrobotsystemintegration.4. Tounderstandwaystoupdateknowledgeintherequiredareaofrobotictechnology.

*****

CourseCode : ROB-02CourseTitle : MechanicsofRobotsNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : ROB

CourseObjective:This course aims to inculcate thoroughunderstanding aboutbasicknowledge of mathematics, kinematics and dynamics required for understandingmotionprogrammingandoperational/controlfunctionalityinrobotics.

CourseContents:

Module1:MathematicalPreliminariesofRobotics:SpatialDescriptions:positions,orientations, and frame, mappings: changing description from frame to frame,Operators: translations, rotations and transformations, transformation arithmetic,compoundTransformations, invertinga transform, transformequations,EulerAngles,FixedAngles,EulerParameters.

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Module 2: Robot Kinematics: Manipulator Kinematics, Link Description, Link toreference frameconnections,Denavit-HartenbergApproach,D-HParameters,PositionRepresentations, Homogeneous Transformation Matrix, Forward Kinematics. InverseKinematics,Geometricandanalyticalapproach.

Module 3: Velocities & Statics: Cross Product Operator for kinematics, Jacobians -DirectDifferentiation,BasicJacobian,,JacobianJv/Jw,JacobianinaFrame,JacobianinFrame {0}, Kinematic Singularity, Kinematics redundancy, Force balance equation,Forces, Velocity/Force Duality, Virtual Work, Force ellipsoid, Jacobian, KinematicSingularity,Kinematicsredundancy,MechanicalDesignofrobotlinkages,

Module 4: Robot Dynamics: Introduction to Dynamics, Velocity Kinematics,Acceleration of rigid body, mass distribution Newton’s equation, Euler’s equation,Iterative Newton –Euler’s dynamic formulation, closed dynamic, Lagrangianformulation of manipulator dynamics, dynamic simulation, computationalconsideration.


1. S.K.Saha,IntroductiontoRobotics2e,TATAMcGrawHillsEducation(2014).2. DilipKumarPratihar,FundamentalsofRobotics,NarosaPublishingHouse,(2019)3. Asitava Ghoshal, Robotics: Fundamental concepts and analysis, Oxford University

Press(2006)4. M.Spong,M.Vidyasagar,S.Hutchinson,RobotModelingandControl,Wiley&Sons,

(2005).5. J. J. Craig, “Introduction toRobotics:Mechanics andControl”, 3rd edition,Addison-

Wesley(2003).AlternativeSWAYAM/NPTELCourse:


Robotics Prof.DilipKumarPratihar IITKharagpur

RoboticsProf.P.Seshu,Prof.P.S.Gandhi,Prof.K.KurienIssac,Prof.B.Seth,Prof.C.Amarnath IITBombay

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. TounderstandterminologiesrelatedtoKinematicsandDynamicsofRobotics.2. Toapplymathematicsformanipulatorpositioningandmotionplanning.3. Toanalysebasicsofmotionprogrammingasperkinematics.4. Toestimatetheforce/torquerequiredtodrivearobot.

*****

66

CourseCode : ROB-03CourseTitle : MicroprocessorandEmbeddedSystemsNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : ROBCourse Objective: This course aims to teach the detailed functioning ofmicroprocessorsandtheroleofembeddedsystemsinaroboticsystem.

CourseContents:

Module1: Introduction to Embedded Systems andmicrocomputers:Introductionto Embedded Systems, Embedded System Applications, Block diagram of embeddedsystems, Trends in Embedded Industry, Basic Embedded System Models, EmbeddedSystem development cycle, Challenges for Embedded System Design, Evolution ofcomputing systemsandapplications.BasicComputer architecture:Von-NeumannandHarvard Architecture. Basics on Computer organizations. Computing performance,Throughput and Latency, Basic high performance CPU architectures, MicrocomputerapplicationstoEmbeddedsystemsandMechatronics.

Module 2: Microprocessor:8086 Microprocessor and its Internal Architecture, PinConfigurationandtheirfunctions,ModeofOperation,IntroductiontoI/OandMemory,Timing Diagrams, Introduction to Interrupts. Introduction to C language, Instructionformat, C language programming format, Addressing mode, Instruction Sets,Programming8086microprocessor.

Module 3: Microprocessor Interfacing:Introduction to interfacing, MemoryInterfacing, Programmable Peripheral Interfacing, Programmable I/O, ProgrammableInterrupt Controller, Programmable Timers, Programmable DMA Controller,ProgrammableKeyBoardController,DataacquisitionInterfacing:ADC,DAC,Serialandparallel data Communication interfacing. Microcontroller: Introduction toMicrocontrollerandits families,Criteria forChoosingMicrocontroller.MicrocontrollerArchitecture,Programmingmodel,addressingmodes,Instructionsets,AssemblyandCprogramming for Microcontroller, I/O programming using assembly and C language,Interrupt Controller, I/O interfacing, Timers, Real Time Clock, Serial and parallelCommunicationprotocols,SPIControllers.LCDController.

Module 4: Microcontroller Interfacing:Introduction to Microcontroller Interfacingand applications: case studies: Display Devices, controllers andDrivers for DC, ServoandStepperMotor.

Module 5: Introduction to Advanced Embedded Processor and Software:ARMProcessor,UnifiedModelLanguage(UML),EmbeddedOS,RealTimeOperatingSystem(RTOS),EmbeddedC.

Module 6:Microprocessor and Embedded System Laboratories:Basic C languageprogramming implementation on Microprocessor and Microcontroller. InterfacingDisplays, Key boards and sensors with Microprocessors and Microcontrollers, DataAcquisition usingMicroprocessor andMicrocontroller, Implementation of Controllingschemes for DC, Servo, Steppermotor using C programming inmicroprocessors andMicrocontrollers.

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TextBooks/References:1. K.V.Shibu,IntroductiontoEmbeddedSystems,McGRAWHillPublications(2009).2. RajKamal,EmbeddedSystems,TATAMcGRAWHillPublications(2003).3. M.MorrisMano,ComputerSystemArchitecture,3ed,PearsonPublication,(2007).4. D.V.Hall,8086MicroprocessorsandInterfacings,TATAMcGRAWHill,(2005).5. B.B.Brey,TheIntelMicroprocessors,PrenticeHallPublications,8thed,(2018).6. M. A. Mazidi, R.D. Mckinlay and D. Casey, PIC Microcontrollers and Embedded

Systems,PearsonPublications,(2008).7. M. Predko, Programming and Customizing the PIC Microcontroller, McGRAW Hill

Publications.3ed,(2017).8. R. Barnett, L. O’Cull and S. Cox, Embedded C Programming and Microchip PIC,

CengageLearning,(2003).AlternativeSWAYAM/NPTELCourse:


EmbeddedSystems Prof.SantanuChaudhary IITDelhi

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Toprepareblockdiagramsforanyroboticcontrol-hardwaredesign,2. Tochooseappropriateflowofembeddedsystemsforaspecificapplication.3. ToWritecodeformicrocontrollerdevices.4. Touseadvancedembeddedprocessorandsoftware.

*****

CourseCode : ROB-04CourseTitle : ControlofRoboticSystemsNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : ROB

CourseObjective:Thiscourseaims todevelop theunderstandingofcontrolsystems,itsdesigningandapplication.

CourseContents:

Module 1: Basics of Control: Differential Equation, Transfer function, Frequencyresponse,Routh-Hurwitztest,relativestability,Rootlocusdesign,constructionofrootloci,phaseleadandphase-lagdesign,lag-leaddesign,Bode,polar,Nyquistplot.

Module 2: Linear Control: Concept of states, state space model, different form,controllability,observability;poleplacementbystatefeedback,observerdesign,P,PI&PIDController,controllawpartitioning,modellingandcontrolofasinglejoint.

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Module 3: Non-Linear Control System:Common physical non-linear system, phaseplanemethod, systemanalysis byphaseplanemethod, stability of non-linear system,stability analysis by describing function method, Liapunov’s stability criterion, thecontrolproblemsformanipulators.

Module4:MotionControl: Point toPoint Control, trajectory generation, ContinuousPath Control, Joint based control, Cartesian Control, Force Control, hybridposition/forcecontrolsystem.


1. M.Gopal,ControlSystems,McGraw-Hill(2012)2. K.Ogata,“ModernControlEngineering”,PrenticeHallIndia(2009).3. M. Spong,M. Vidyasagar, S. Hutchinson, RobotModeling and Control,Wiley &

Sons,(2005).4. J. J. Craig, “Introduction to Robotics: Mechanics and Control”, 3rd edition,

Addison-Wesley(2003).5. S.K.Saha,IntroductiontoRobotics2e,TATAMcGrawHillsEducation(2014).6. ThomasKailath,“LinearSystems”,PrenticeHall(1980).7. Alok Sinha, “Linear Systems: Optimal and Robust Control”, Taylor & Francis

(2007).AlternativeSWAYAM/NPTELCourse:


RoboticsandControl:TheoryandPractice

Prof.N.Sukavanam,Prof.M.FelixOrlando IITRoorkee

Controlsystems Prof.C.S.ShankarRam IITMadras

Course Outcomes: After completion of course, students would have thoroughunderstandingoflinear,non-linearcontrolsystemsandMotionControl.

*****

CourseCode : ROB-05CourseTitle : Project in RoboticsNumberofCredits : 4(L:1;T:0;P:6)CourseCategory : ROBCourseObjective:

Toassimilatethetheoreticalknowledgegainedinthelecturecourses(ROB-1to4)forreal-lifepracticalapplicationsinorderhaveeffectivelearningandskill-development,mainly,fromthepointofviewoftheemployabilityinindustries.

69

CourseContents:

Thiscourseisaprojecttype.Theplanofconductingthiscourseisgivenbelow:

1. Participants will be divided into teams of two/four members within first week of the starting of the course by the course coordinators/managers depending on the number of participants registered in the course. The benefits of such team-based projects are listed in the Course Outcomes below.

2. The teams will have a team coordinator or leader, which will be identified by the coordinators/managers of the course (may be the first name in the list of a student team).

3. The projects could be of the following types:

a. Literature search (LS) type: Studying about an aspect of robotics, say, vision, robot kinematics, dynamic, controls, etc.

b. Algorithm development (AD) type: Analyse, say, a robot kinematics using RoboAnalyzer or Matlab/Octave/Freemat/Scilab or similar software or write an algorithm using any programming language (Python, etc.). For example, writing forward kinematics of a robot or image processing in Vision.

c. Design/synthesis (DS) type: Proposing a new type of system/device for performing certain task. For example, a mobile robot for Covid-19 isolation wards.

4. The teams will be asked to contact their team members within a week and decide their topic with two weeks, i.e., within first 3 weeks of the starting of the course.

5. Students MUST spend about 6 hours in a week to discuss their progress together, study together or individually, write programmes, fabricate circuits, etc.

6. During the one lecture hour the coordinators will explain how to do literature survey, how to find the sources of hardware, which software to use for a particular purpose, how to select an electric motor, etc., present case studies, etc.

7. At the end of the course duration, each team will submit no more than 10 slides in .pdf file and/or not more than a video of one min to showcase their project hardware/software/plots, etc. generated during the project to a cloud (say, Google Drive).

8. Evaluation: It will be done in two parts

a. Peer Evaluations (20%): Presentations in .pdf will be evaluated (online) by two other teams and grade them out of 10 marks.

b. Expert evaluation (80%): Coordinators will take a presentation of 3 mins. plus, Q&A in a common online session to give marks out of 80.


Sinceit isaprojecttype,someexperiencesharingbooksandlinkstosimilaractivitiesarelisted.

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1. Chuhan, M., and Saha, S.K., 2010, Robotics Competition Knowledge BasedEduationinEngineering,Pothi.com

2. Baun,M.,andChaffe,J.,2018,EngineeringandBuildingRobotsforCompetitions,Amazon.com

CorrespondingOnlineResources:1. http://www.ddrobocon.in/2. http://courses.csail.mit.edu/iap/6.095/

CourseOutcomes:Theoutcomesareenvisagedasfollows:

1. Each participant will know students from other colleges/states and their work ethics/culture.

2. To Practice how to work together in a team. An essential skill in an industry. 3. To apply the theoretical knowledge learnt from other courses, which is required by an

industry. 4. To learn how to make presentation in a team. A soft skill needed in research and

industry. 5. Peer learning from the evaluation of other teams’ work. A skill which is essential

when one is in a workforce. 6. To examine different hardware components and their working/control using software.

*****

71

VirtualandAugmentedReality

73

MinorDegreein“VirtualandAugmentedReality”

CourseStructureS.No. CourseCode Title L T P Credits1 VAR-01 ComputerGraphicsforVirtualReality 3 0 2 4

2 VAR-02 Concepts of Virtual and AugmentedReality 3 0 2 4

3 VAR-03 Scientific and Engineering DataVisualisation 3 0 2 4

4 VAR-04 MathematicalModellingandComputerAidedEngineering 3 0 0 3

5 VAR-05 MobileVRandAIinModuley 3 0 2 4TOTAL 15 0 8 19


• VARdenotesthatMinorDegreerelatedto“VirtualandAugmentedReality”.• 01, 02, 03, 04, 05 are course in order they have to be taken, if taken in different

semesters.Multiplecoursemayalsobetakeninthesamesemester(ifrequired).---------------------------------------------------------------------------------------------------------------

75

DetailedSyllabus

CourseCode : VAR-01CourseTitle : ComputerGraphicsforVirtualRealityNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : VAR

CourseObjective:• To introduce theuseof thecomponentsofagraphicssystemandbecome familiar

withbuildingapproachofgraphicssystemcomponentsandalgorithmsrelatedwiththem.

• Tolearnthebasicprinciplesof3-dimensionalcomputergraphics.• Provide an understanding of mapping from a world coordinates to device

coordinates,clipping,andprojections.

CourseContents:

Module1[4Lectures]Graphics system and models: applications of computer graphics, graphics system,physicalandsyntheticimages,imagingsystems,graphicsarchitectures.

Module2[10Lectures]Geometricobjectsand transformations: scalars,pointsandvectors, three-dimensionalprimitives,coordinatesystemsandframes,framesinOpenGL,matrixandvectorclasses,modelling a colored cube, affine transformations - translation, rotation and scaling,transformations in homogeneous coordinates, concatenation of transformations,transformationmatricesinOpenGL,interfacesto3Dapplications,quaternion.

Verticestofragments:basicimplementationstrategies,fourmajortasks,clipping-lineclipping, polygon clipping, clipping of other primitives, clipping in three dimensions,polygonrasterization,hidden-surfaceremoval,antialiasing,displayconsiderations.Module3[9Lectures]Lighting and shading: light and matter, light sources, the Phong reflection model,computation of vectors, polygonal shading, approximation of a sphere by recursivesubdivision,specifying lightingparameters, implementinga lightingmodel, shadingofthespheremodel,per-fragmentlighting,globalillumination.

Hierarchicalmodelling:symbolsandinstances,hierarchicalmodels,arobotarm,treesand traversal, use of tree data structures, other tree structures, scene graphs, openscenegraph.

Module4[10Lectures]Discretetechniques:buffers-digitalimages-writingintobuffers-mappingmethods-texturemapping-texturemappinginOpenGL-texturegeneration-environmentmaps-reflectionmap-bumpmapping-compositingtechniques-samplingandaliasing.

Advancedrendering:goingbeyondpipelinerendering-raytracing-buildingasimpleray tracer - the rendering equation - radiosity - Renderman - parallel rendering -

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volume rendering - Isosurfaces and marching cubes - mesh simplification - directvolumerendering-image-basedrendering.

Module5[9Lectures]Fractals:modelling - SierpinskiGasket - coastlineproblem - fractal geometry - fractaldimension-recursivelydefinedcurves-Kochcurves-ccurves-dragons-spacefillingcurves - turtlegraphics -grammarbasedmodels -Graftals -volumetricexamples -k-midpoint subdivision - fractal Brownianmotion - fractalmountains - iteration in thecomplexplane-Mandelbrotset.Virtual reality modelling language: introduction, exploring and building a world,building object, lighting, sound and complex shapes, animation and user interaction,colors, normals and textures, nodes references. Special applications: stereo displayprogramming, multiport display systems, multi-screen display system, fly modenavigation,walkthroughnavigation,virtualtrackballnavigation.

TextBooks/References:1. RajeshK.Maurya,ComputerGraphicswithVirtualRealitySystem,JohnWiley&

Sons.2. EdwardAngel,“InteractiveComputerGraphics:ATop-DownApproachUsing

OpenGL”,Addison-Wesley.3. FoleyJamesD,VanDam,FeinerandHughes,“ComputerGraphics:Principlesand

Practice”,PearsonEducation.4. DonaldHearnandPaulineBaker,“ComputerGraphicsCVersion”,Pearson

Education.

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Tolistthebasicconceptsusedincomputergraphics.2. Toimplementvariousalgorithmstoscan,convertthebasicgeometricalprimitives,

transformations,Areafilling,clipping.3. Todefinethefundamentalsofanimation,virtualrealityanditsrelatedtechnologies.4. Todesignanapplicationwiththeprinciplesofvirtualreality.

*****

CourseCode : VAR-02CourseTitle : ConceptsofVirtualandAugmentedRealityNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : VAR

CourseObjective:• Tomakestudentsknowthebasicconceptandframeworkofvirtualreality.• Toteachstudentstheprinciplesandmultidisciplinaryfeaturesofvirtualreality.• Toteachstudentsthetechnologyformultimodaluserinteractionandperceptionin

VR,inparticularthevisual,audialandhapticinterfaceandbehaviour.• Toteachstudents the technology formanaging largescaleVRenvironment inreal

time.

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• To provide students with an introduction to the VR system framework anddevelopmenttools.

CourseContents:

Module1[9Lectures]Virtual reality and virtual environments: the historical development of VR, scientificlandmarks computer graphics, real-time computer graphics, virtual environments,requirementsforVR,benefitsofvirtualreality.Hardwaretechnologiesfor3Duserinterfaces:visualdisplays,auditorydisplays,hapticdisplays,choosingoutputdevicesfor3Duserinterfaces.

Module2[14Lectures]3Duser interface inputhardware: inputdevice characteristics,desktop inputdevices,tracking devices, 3dmice, special purpose input devices, direct human input, home -brewedinputdevices,choosinginputdevicesfor3Dinterfaces.Software technologies: database -world space,world coordinate,world environment,objects - geometry, position / orientation, hierarchy, bounding volume, scripts andother attributes, VR environment - VR database, tessellated data, LODs, Cullers andOccluders, lights and cameras, scripts, interaction - simple, feedback, graphical userinterface, control panel, 2D controls, hardware controls, room / stage / areadescriptions, world authoring and playback, VR toolkits, available software in themarket.Module3[8Lectures]3D interaction techniques:3Dmanipulation tasks,manipulation techniques and inputdevices,interactiontechniquesfor3Dmanipulation,designguidelines–3Dtraveltasks,travel techniques, design guidelines - theoretical foundations of wayfinding, usercentered wayfinding support, environment centered wayfinding support, evaluatingwayfinding aids, design guidelines - system control, classification, graphical menus,voice commands, Gestrual commands, tools, mutimodal system control techniques,design guidelines, case study: mixing system control methods, symbolic input tasks,symbolicinputtechniques,designguidelines,beyondtextandnumberentry.

Module4[7Lectures]Designing anddeveloping 3Duser interfaces: strategies for designing anddevelopingguidelinesandevaluation.Advancesin3Duserinterfaces:3Duserinterfacesfortherealworld,ARinterfacesas3D data browsers, 3D augmented reality interfaces, augmented surfaces and tangibleinterfaces,agentsinAR,transitionalAR-VRinterfaces-thefutureof3Duserinterfaces,questions of 3D UI technology, 3d interaction techniques, 3d UI design anddevelopment,3DUIevaluationandotherissues.

Module5[4Lectures]Virtual reality applications: engineering, architecture, education, medicine,entertainment,science,training.

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TextBooks/References:1. PaulMealy,Virtual&AugmentedRealityforDummies,JohnWiley&Sons.2. Alan B Craig, William R Sherman and Jeffrey D Will, “Developing Virtual Reality

Applications:FoundationsofEffectiveDesign”,MorganKaufmann.3. Jan Erik Solem, Programming Computer Vision with Python, Shroff

Publisher/O’ReillyPublisher4. GerardJounghyunKim,“DesigningVirtualSystems:TheStructuredApproach”.5. Doug A Bowman, Ernest Kuijff, Joseph J LaViola, Jr and Ivan Poupyrev, “3D User

Interfaces,TheoryandPractice”,AddisonWesley,USA

CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Toanalysethehardwareandsoftwarerequirements.2. Tousethedifferentintersectiontechniques.3. Todesign3Dinterfaces.

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CourseCode : VAR-03CourseTitle : ScientificandEngineeringDataVisualisationNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : VAR

CourseObjective:• Thestudentshouldbeabletodesignprinciplesandtechniquesforvisualizingdata.• Practicalexperiencebuildingandevaluatingvisualizationsystems.• Allowforproject-basedopportModuleies to identify,understand,analyze,prepare,

andpresenteffectivevisualizationsonavarietyoftopics.

CourseContents:

Module1[7Lectures]Visualisation - Scientific and engineering perspective - Impact of Visualisation inproductdesign,anoverviewofcomputergraphics forvisualization–Typesofdata forvisualisation, Introductionto tensors.roleofpre-processor,solverandpostprocessorinsolvingengineeringproblems.Overview of massive data visualization: Simplification methods, Multi-resolutionmethods,Externalmemorymethods,Visualscalability.

Module2[9Lectures]Scalarvisualisationtechniques:VisualisationGoals,Representationofmeshandresultsdata,mappinganalysisresultstoVisualisations,onedimensional,twodimensionaland3DScalarfields-Elementfacecolourcoding-contourdisplay-Isosurfacetechniques-MarchingCubesalgorithm-Particlesampling.

Module3[9Lectures]Visualization of flow data: Visualization mappings of flow data, Vector mapping -elementary icons - particle traces - streaklines, streamlines - streamribbons andstreamtubes-globalicons-Tensormappings-elementaryicons-globalicons.

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Module4[7Lectures]Continuum volume display: Volume rendering Terminology, Surface and Volumerenderingtechniques,Optimisation.Module5[10Lectures]Applicationsofengineeringvisualisation:Casestudiescreatedinthelaboratory.FUTURETRENDS:TrendsinComputingHardware,Animation,Videoandmulti-media,softwaretrendsinVisualisation.

TextBooks/References:1. TorstenMöllerandBerndHamannRobertDRussell,“MathematicalFoundationsof

ScientificVisualization,ComputerGraphicsandMassiveDataExploration”,Springer-VerlagBerlinHeidelberg

2. HelenWright,“IntroductiontoScientificVisualization”,Springer.3. RichardSGallagher,“ComputerVisualization:GraphicsTechniquesforEngineering

andScientificAnalysis”,CRCPress,CRCPressLLC.CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Todesignprocessestodevelopvisualizationmethodsandvisualizationsystems,and

methodsfortheirevaluation.2. To complete preparation and processing of data, visual mapping and the

visualization.3. Toanalyzelarge-scaleabstractdata.

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CourseCode : VAR-04CourseTitle : MathematicalModellingandComputerAidedEngineeringNumberofCredits : 3(L:3;T:0;P:0)CourseCategory : VAR

CourseObjective:• Studentsshouldbeabletoformulate,analyzeandapplymathematicalmodels.• Students should be able to understand the necessarymathematical abstraction to

solveproblems.

CourseContents:

Module1[12Lectures]Introduction: problems in engineering-structural - fluid flow and heat transfer withtheir relevance in product development - examples - need for computer aidedengineering.Partialdifferentialequations:elliptic,parabolicandhyperbolic-physicalsignificance-solutiontechniques.NumericalmethodstosolvePDEs:centraldifferences,Crank-NicolsonandADImethods-examples-stabilityanderrorofnumericalschemes.

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Module2[6Lectures]Variational calculus: introduction, solutions selected differential equations byVariationalmethods,Rayleigh-Ritzmethod-introductiontofiniteelementmethod.Module3[8Lectures]Finite element method: concepts, nodes, elements, connectivity, coordinate systems,shapefunctions,stiffnessmatrix,globalstiffnessmatrix,Isoparametrielementssolutionmethods–examples-useofsoftware.Module4[6Lectures]Fluid flow: introduction to computational fluid dynamics (finite difference, finiteelementtechniques)-formulationoffluidflowproblems(simplecasesonly)-Navier-Stokesequation-solutiontechniques-examples,solutionoffluidflowproblemsusingsoftware.Module5[10Lectures]Heattransfer:derivationofenergyequationingeneralform-solutionsusingnumericalmethods(finitedifferenceandfiniteelementtechniques),solutionsusingFEAandCFDtechniquesforconductiveandconvectiveheattransferproblems.Introductiontomulti-physicsproblems:electrophoresis,electro-osmosis,lab-on–chipusedinbiotechnologyuseofsoftware.

TextBooks/References:1. ReddyJN,“AnIntroductiontotheFiniteElementMethod”,TataMcGrawHill.2. Singerasu S Rao, “The Finite Element Method in Engineering”, Butterworth

Heinemann.3. CurtisFGeraldPatrickOWheatley,“AppliedNumericalAnalysis”,Pearson.4. MuralidharK and SundararajanT, “Computational Fluid Flow andHeat Transfer”,

NarosaPublications.CourseOutcomes:Aftercompletionofcourse,studentswouldbeable:1. Todescribethebasicsofpartialdifferentialequationsandnumericalmethods.2. Tounderstandthemethodsoffiniteelementmethods.3. Tounderstandthemethodsfluidflowtechniques.

*****

CourseCode : VAR-05CourseTitle : MobileVRandAIinModuleyNumberofCredits : 4(L:3;T:0;P:2)CourseCategory : VAR

CourseObjective:• Togivestudentshands-onexposuretomobilevirtualrealityinModuley.• TogivestudentsexperiencewithbasicAIalgorithmsinvirtualreality.• Toprovidestudentswithfundamentalsofgamedesignsinvirtualreality.

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CourseContents:

Module1[12Lectures]Introduction to Moduley, Moduley Editor, Moving a Cube, Lights, Particle Systems,ApplyingPhysics,andModuleyAssetStore,C#CodingIntroduction,Variables,Methods,If Blocks, Loops, Hello Mammoth, User Interaction in Moduley, Inputs IntroductionPreview, Key Presses, Moving a Player, Jumping, Moving Forward, Cycling Cameras,Prefabs Introduction, What are Prefabs?, Instantiating Objects, Random Angles,DestroyingObjects,ExplosionEffects,AddingExplosionEffects.

Module2[6Lectures]

DevelopingaPathfindingGame,HowtoSetUpaProject,Node,StringMap,A*AlgorithmSetup, A* Algorithm Loop, Auxiliary Methods, Finishing the Algorithm, Importing 2DAssets, Building a Level, From Console to Visual, Adding Tanks, Identifying Nodes,MovingtheTank,VisuallyMovingTank,SmoothMovement,SmoothRotation,OrderingTanktoMove,SpeedingupPlayer,SpawningLogic,CrateVisuals,AddingCratestoValidPositions, Collecting Crates, Score Counting, Game Interface, Starting theGame,GameOverScreen,Scoring,Sounds.Module3[8Lectures]VRIntroduction-Moduley,ActivatingVR,BuildingaCastle,CameraChangingPosition,Lowering Castle Doors, Triggering Events Interface, Blender, Download and InstallBlender, Introduction & Customizing Settings, Controlling Blender Camera, EmulateNumpadCamera,ManipulatingObjects,CommonTools,Mirroring1SideofObject.CaseStudy:FlappybirdModuleygame,Firstpersonshootergame,KartModuleygame.

Module4[6Lectures]Introduction to Moduley-ML, Why Machine Learning, different kinds of learnings,Neural Networks (NNs), Training a NN, Optimizer, Convolutional layers, Transferlearning, Imitation learning inModuley,Training thekart inkartgamevia IL,Testingthedrive.

Module5[10Lectures]IntroductiontoReinforcementLearninginModuley-ML,ReinforcementLearning,Initialstate, training a policy, The PPO algorithm, Evolutional Strategies, Reward, training akartinthekartgamewithRL,Tensorboardanalysis,Testingresults.TextBooks/References:

1. Linowes, J.,&Schoen,M. (2016).CardboardVRProjects forAndroid.PacktPublishingLtd.

2. Lanham,M.(2019).Hands-OnDeepLearningforGames:Leveragethepowerofneuralnetworksandreinforcementlearningtobuildintelligentgames.PacktPublishingLtd.

3. Aversa,D.,Kyaw,A.S.,&Peters,C.(2018).ModuleyArtificialIntelligenceProgramming:Addpowerful,believable,andfunAIentitiesinyourgamewiththepowerofModuley2018!PacktPublishingLtd.

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Software/HardwareRequirements:Moduley2017and5.4.3f1;ModuleyML;Googlecardboardoracompatibleheadset;Asmartphonewith2-3GBRAM,16GBofstoragespace,Qualcomm®Snapdragon™675orhigher.

CourseOutcomes:Aftercompletionofcourse,studentswould:1. TolearntocodeforgamedevelopmentinModuleyC#2. Tounderstandthefundamentalsofgamedesign.3. TolearntouseAIalgorithms(A*,IL,andRL)inModuley-ML.

******

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ArtificialIntelligenceandMachineLearning-----------------------------------------------------------

Blockchain

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CyberSecurity

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DataScience

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InternetofThings(IoT)

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Robotics-----------------------------------------------------------

VirtualandAugmentedReality

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ALLINDIACOUNCILFORTECHNICALEDUCATION

NelsonMandelaMarg,VasantKunj,NewDelhi110070

www.aicte-india.org

minor degree final - government of india, all india

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