m.tech. programme mechanical engineering artificial...

1

M.Tech. Programme

Mechanical Engineering – Artificial Intelligence

Curriculum and Scheme of Examinations (2020 Admission)

SEMESTER 1 Credits 23

Exam

Slot

Course No.

Course Name

L-T-P

Internal

Marks

End Semester

Exam Credits

Marks Duration

(hrs)

A 02ME6411 Artificial Intelligence –

Principles and Techniques

3-0-0 40 60 3 3

B 02ME6421 Data Structures and

Algorithms

3-1-0 40 60 3 4

C 02ME6431 Machine Learning 3-1-0 40 60 3 4

D 02ME6441 Mathematics for Machine

Learning

3-1-0 40 60 3 4

E 02ME6451 Elective 1 3-0-0 40 60 3 3

02CA6001 Research Methodology 1-1-0 100 0 0 2

02ME6461 Seminar 1 0-0-2 100 0 0 2

02ME6471 Programming Lab 0-0-2 100 0 0 1

List of Electives:

Elective I

02ME6451.1 Game Theory 02ME6451.2 Robotics & Automation 02ME6451.3 Adaptive Signal Processing 02ME6451.4 Biometric Technologies 02ME6451.5 Digital Image Processing 02ME6451.6 Pattern Recognition

02ME6451.7 Human Computer Interface

2

SEMESTER 2 Credits 18

Exam

Slot

Course No.

Course Name

L-T-P

Internal

Marks

End Semester

Exam

Credits Marks Duration

(hrs)

A 02ME6412 Big-Data Analytics 3-0-0 40 60 3 3

B 02ME6422 Topics in Optimisation 3-0-0 40 60 3 3

C 02ME6432 Deep Learning: Theory and

Practice

3-0-0 40 60 3 3

D 02ME6442 Elective 2 3-0-0 40 60 3 3

E

02ME6452

Elective 3

Industry run course or

MOOC course in the

domain of specialisation

(Subjected to the approval

by KTU)

3-0-0

40

60

3

3

02ME6462 Mini Project 0-0-4 100 0 0 2

02ME6472

Advanced Computing Lab (This lab can focus on

AI/ML frameworks)

0-0-2

100

0

0

1

List of Electives:

Elective 2

02ME6442.1 Computer Vision: Foundations and Applications

02ME6442.2 Modelling and Simulation

02ME6442.3 Internet of Things

02ME6442.4 Bioinformatics

02ME6442.5 Soft Computing

3

Semester 3 Credits 14

Exam

Slot

Course No.

Course Name

L-T-P

Internal

Marks

End Semester

Exam

Credits Marks Duration

(hrs)

A

02ME7411

Elective 4

Industry run course or

MOOC course in the

domain of specialisation

(Subjected to the approval

by KTU)

3-0-0

40

60

3

3

B 02ME7421 Elective 5 3-0-0 40 60 3 3

02ME7431 Seminar 0-0-2 100 0 0 2

02ME7441 Project (Phase 1) 0-0-8 50 0 0 6

List of Electives

Elective 4

02ME7411.1 Reinforcement Learning

02ME7411.2 Online Prediction and Learning

02ME7411.3 Medical image processing and Analysis

02ME7411.4 Time series Analysis

Elective 5

02ME7421.1 Scalable Systems for Data Science

02ME7421.2 Machine learning for big data

02ME7421.3 Cloud and big data analytics

02ME7421.4 Approximation Algorithms

02ME7421.5 Parallel and distributed data management

02ME7421.6 Social Network Analytics

Semester 4 Credits 12

Exam

Slot Course No. Course Name L-T-P

Internal

Marks

End Semester Exam

Credits Marks

Duration

(hrs)

02ME7412 Project (Phase 2) 0-0-21 100 0 0 12

Total Credits: 68

4

Credits Distribution

Semester Distribution of Credits Credits

Semester – 1

Core – 17

Elective – 3

Seminar – 2

Lab – 1

23

Semester – 2

Core – 9

Elective – 6

Mini Project – 2

Lab – 1

18

Semester – 3

Elective – 6

Seminar – 2

Project – 6

14

Semester – 4 Project – 12 12

Total

Core – 26

Elective – 15

Mini Project – 2

Lab – 2

Seminar – 4

Project – 18

67

5

Course No: 02ME6411

Course Title: ARTIFICIAL INTELLIGENCE – PRINCIPLES AND TECHNIQUES

Credits: 3-0-0: 3

Course Objectives

• To provide necessary basic concepts in Artificial Intelligence.

• Identify the problems where AI is required

• Compare and contrast different AI techniques available

• Understand learning algorithms

• Apply the basic concepts to various elementary and some advanced applications.

Syllabus

Introduction to artificial intelligence, Problems, Problem Spaces and search, Heuristic search

technique, Advanced search, Constraint satisfaction problems, Knowledge representation and

reasoning, Non-standard logics, Uncertain and probabilistic reasoning, semantic networks and

description logics, Rules systems: use and efficient implementation, Planning systems, Natural

Language Processing, Learning, Expert Systems

Course Outcomes

• Identify and choose the appropriate representation for an AI problem or domain model

• Apply the most appropriate algorithm for search and reasoning within an AI problem

domain

• Have a good knowledge of various learning techniques to solve AI problems

• Design, analyse and demonstrate AI applications or systems that apply to real life

problems.

References

1.Artificial Intelligence – A Modern Approach Stuart Russell, Peter Norvig Pearson

Education Third Edition,2015

2.“Artificial Intelligence”, Elaine Rich and Kevin Knight McGraw-Hil Third

Edition l, 2010

3.“Artificial Intelligence: Structures and Strategies for complex problem Solving”, G.

Luger Pearson Education Fourth Edition-2002

4.“Introduction to Artificial Intelligence” E Charniak and D McDermott Pearson

Education, 2008

5.“Artificial Intelligence and Expert Systems” Dan W. Patterson, Prentice Hall of

India 2010

6."Artificial Intelligence and Expert Systems Development" D W Rolston-. Mc Graw

hill 2010

6

Course No: Name of Course L-T-P Credits Year of

Introduction

02ME6411 ARTIFICIAL INTELLIGENCE –

PRINCIPLES AND TECHNIQUES 3-0-0 3 2020

Pre- requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Introduction to Artificial Intelligence (AI): What is AI, Foundations and

History of AI, Applications of AI

Intelligent Agents: Agents and Environments, the concept of Rationality,

Nature of environments, structure of agents

7 15

MODULE: 2

Problem Solving by Classical Searching: Problem solving agents,

uninformed search strategies, informed search strategies, heuristics in search

Beyond Classical Search: Local search algorithms and Optimization

problems, searching with non-deterministic actions, searching with partial

observations, adversarial search with alpha beta pruning, constraint

satisfaction problem

6 15

FIRST INTERNAL TEST

MODULE: 3

Knowledge Based Logical Agents: The Wumpus World, propositional logic,

propositional theorem proving, agents based on propositional logic

First Order Logic: Syntax and Semantics of First Order Logic, Using First

Order Logic, Knowledge Engineering in First Order Logic, Unification and

Lifting, Forward and Backward chaining, Resolution

6 15

MODULE: 4

Planning: Classical planning, Components of a planning system, algorithms

for planning as state-space search, Hierarchical planning, Multiagent

planning, Reactive Systems

Reasoning: Acting under uncertainty, representing knowledge in uncertain

domains, Semantics of Bayesian networks, Rule based methods for uncertain

reasoning, Time and uncertainty, Inference in temporal models, Weak Slot-

and-Filler Structures, Strong Slot-and-Filler Structures

7 15

SECOND INTERNAL TEST

MODULE: 5

Learning: What is learning? Rote learning, Learning by taking advice,

Learning by problem-solving, Learning by examples, Explanation based

learning, Discovery, Analogy, Formal learning theory, Neural Net learning,

Genetic learning, Connectionist AI and Symbolic AI

Expert Systems: Representing and using domain knowledge, Expert System

Shells, Explanation, Knowledge Acquisition.

7 20

MODULE: 6

Natural Language Processing: Introduction, Syntactic Processing,

Semantic Analysis, Discourse and Pragmatic Processing, Statistical NLP,

Spell Checking.

6 20

7

Course No: 02ME6421

Course Title: DATA STRUCTURES AND ALGORITHM

Credits: 3-1-0: 4

Course Objectives

• To be familiar with basic techniques of algorithm analysis

• To be familiar with writing recursive methods

• Master the implementation of linked data structures such as linked lists, binary trees

other tree representation.

• Be familiar with advanced data structures such as balanced search trees, hash tables,

priority queues and the disjoint set union/find data structure

• To be familiar with sorting algorithms such as quicksort, mergesort and heapsort

• To be familiar with graph algorithms such as shortest path and minimum spanning

tree

• To be familiar with algorithm design techniques.

Syllabus

Data organization and manipulation using data structures such as stacks, queues, linked lists,

binary trees, heaps, graphs, sets and algorithm design techniques.

Course Outcomes: After the completion of the course the student will be able to

CO1. Design algorithms for a task and calculate the time and space complexity of the

algorithm.

CO2. Assess the impact of choice of data structures and algorithm design methods in

the performance of programs.

CO3. Represent data using trees, graphs, heaps and manipulate them.

CO4. Select sorting algorithms appropriate to specific circumstances.

CO5. Arrange data using appropriate Hash Functions

CO6. Select data structure and algorithm design method for a given application.

References

1. S. Sahni, “Data structures, Algorithms and Applications in Java”, Universities Press.

[ISBN:0-07-109217-x]

2. Adam Drozdek, “Data structures and Algorithms in Java”, 3rd edition, Cengage

Learning. [ISBN:978-9814239233].

3. Ellis Horowitz, Sartaj Sahni, Susan Anderson Freed, Fundamentals of Data Structures

in C, Second Edition, University Press, 2008

4. Thomas Cormen, Charles E. Leiserson, Ronald Rivest, Introduction to algorithm,3rd

edition, PHI Learning.

5. Mark Allem Weiss, Data Structures and Algorithm Analysis in C, 2nd Edition, Pearson

8


Introduction

02ME6421 DATA STRUCTURES AND

ALGORITHM 3-1-0 4 2020

Pre- requisites: Strong foundation in any one programming language

MODULES

Conta

ct

hours

Sem Exam

Marks %

MODULE: 1

Algorithms, Performance analysis- time complexity and space complexity,

Asymptotic Notation-Big Oh, Omega and Theta notations, Complexity

Analysis Examples. Data structures-Linear and nonlinear data structures, ADT

concept, Linear List ADT, Array representation, Linked representation, Vector

representation, singly and doubly linked, circular lists. Representation of arrays,

Sparse matrices and their representation.

10 15

MODULE: 2

Stack and Queue ADTs, array and linked list representations, infix to postfix

conversion using stack, implementation of recursion, Circular queue-insertion

and deletion, Dequeue ADT, array and linked list representations, Priority

queue ADT, implementation using Heaps, Insertion into a Max Heap, Deletion

from a Max Heap, Binomial Heaps (Definition and examples only), Symmetric

Min-Max Heaps (Definition and examples only), Interval Heaps (Definition and

examples only).

10 15

FIRST INTERNAL TEST

MODULE: 3

Trees- Ordinary and Binary trees terminology, Properties of Binary trees,

Binary tree ADT, representations, tree traversals.

Graphs- Graphs terminology, Graph ADT, representations, Data Structures for

Disjoint Sets, Disjoint Set operations and representation of disjoint sets.

9 15

MODULE: 4

Search trees- Binary search tree-Binary search tree ADT, insertion, deletion

and searching operations, Balanced search trees, AVL trees-Definition and

examples only, Red Black trees – Definition and examples only, B-Trees-

definition, insertion and searching operations( illustration with examples only)

Tries(Definition and examples only)- Binary Tries, k-d Trees, Point Quad trees.

8 15


MODULE: 5

Searching - Linear and binary search methods, Hashing-Hash functions,

Collision Resolution methods- Open Addressing, Chaining.

Sorting Methods- Quick sort, Merge sort, Heap sort, Radix sort, comparison of

sorting methods.

7 20

MODULE: 6

Algorithm design techniques- Divide and conquer strategy, dynamic

programming, backtracking and branch &bound.

Graph traversals/search methods-DFS and BFS, Applications of Graphs-

Minimum cost spanning tree using Kruskal’s algorithm, Dijkstra’s algorithm

for Single Source Shortest Path Problem

8 20

9

Course No: 02ME6431

Course Title: MACHINE LEARNING

Credits: 3-1-0: 4

Course Objectives

• To provide necessary basic understanding in machine learning

• To learn about the various concepts and models available in machine learning

• Choose and apply machine learning models based on application

Syllabus

Introduction to Machine Learning, Linear and Logistic Regression, Probability and

classification, Neural Networks, SVM, Dimensionality Reduction, Ensemble methods,

Unsupervised Learning, Reinforcement Learning.

Course Outcomes

• Understand the concept, purpose, scope, steps, applications, and effects of ML.

• Identify the concepts of supervised and unsupervised and reinforcement learning, and

forecasting models.

• Illustrate the working of classifier models like SVM, Neural Networks and

probabilistic classification methods and identify classifier model for typical machine

learning applications

• Use dimensionality reduction, cross validation and performance metrics of

classification.

• Acquire skills in unsupervised and reinforcement learning models and identify its

applicability in real life problems

References

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

• Ethem Alpaydın, Introduction to Machine Learning (Adaptive Computation and

Machine Learning), MIT Press, 2004.

• Richard O Duda, Peter E Hart, David G Stork, Pattern Classification, Second Edition.

Wiley.

• Stephen Marsland, Machine Learning: An Algorithmic Perspective, Second Edition.

CRC press.

10


Introduction

02ME6431 MACHINE LEARNING 3-1-0 4 2020

Pre-requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Introduction to Machine Learning (ML), Types of Machine Learning-

Supervised, Unsupervised, Reinforcement. Types of ML problems:

Association, Classification and Regression. General Steps or Process of

Machine Learning, Objective (Minimize Error or Cost Function), Cost

functions: Definition and Types

Linear regression: with single and multiple variables (features), Least

Squares Gradient descent, Bias-Variance trade off, Normal equations.

6 15

MODULE: 2

Logistic Regression: Hypothesis representation, Decision boundary,

Sigmoid Function and its differentiation, Gradient Descent, Regularization

of Logistic Regression.

Classification: Cross validation and re-sampling methods, Classifier

performance measures- Precision, recall, ROC curves.

Neural Networks – Concept of perceptron and Artificial neuron, Feed

Forward Neural Network, Introduction to back propagation, Weight

initialization.

7 15

FIRST INTERNAL TEST

MODULE:3

Probability and classification: Naive Bayes and Gaussian class-conditional

distribution, Bayes' Rule and Naive Bayes Model, Maximum Likelihood

estimation. Discrete Markov Processes, Hidden Markov models.

7 15

MODULE:4

Kernel Machines- Support Vector Machine- Optimal Separating hyper

plane, Soft-margin hyperplane, Kernel trick, Kernel functions.

Dimensionality Reduction: PCA, LDA, MDA

6 15


MODULE: 5

Ensemble methods: Boosting, Bagging, and Stacking

Unsupervised Learning - Clustering Methods - K-means, Expectation-

Maximization Algorithm, Hierarchical Clustering methods, Density based

clustering.

7 20

MODULE: 6

Reinforcement Learning- Markov Decision, Monte Carlo Prediction.

Forecasting models: Trend analysis, Cyclical and Seasonal analysis,

Smoothing, Moving averages, Auto-correlation, ARIMA.

8 20

11

Course No: 02ME6441

Course Title: MATHEMATICS FOR MACHINE LEARNING

Credits: 3-1-0: 4

Course Objectives

• To provide necessary basic concepts in computational techniques and algebraic skills.

• To study about fundamental probability concepts and statistics.

• To study about random process.

Syllabus

Linear Algebra, study of system of linear equation , matrix algebra, vector spaces, eigen

values and eigen vectors, orthogonality and diagonalization ,Probability and Statistics,

Probability Distribution Function, Random Variables, Function of random variables, Random

Process

Course Outcomes

• Have a good knowledge of computational techniques and algebraic skills.

• Have a fundamental knowledge of the basic probability concepts

• Have a good knowledge of standard distributions which can describe real life

phenomena.

• Acquire skills in handling situations involving several random variable and functions

of random variables

• Understand and characterize phenomena which evolve with respect to time in

probabilistic manner

References

• Mathematics for Machine learning by Marc Peter Deisenroth , A Aldo Faisal and

Cheng Soon Ong published by Cambridge University Press

• Gilbert Strang, Linear algebra and learning from data. Wellesley, Cambridge press ,

2019

• Probability and random process ORF 309/ MAT380 lecture notes Prinston University

Version Feb 22, 2016

• Probabilistic Graph models Principles and models ,Daphne Koller and Nir Friedman

• Ross, Sheldon M, Introduction to Probability Models.

• Ross, Sheldon M, Introduction to Probability and statistics for engineers and scientists,

Elsevier 2008

• Pradeep Kumar Ghosh, Theory of Probability and Stochastic process, University press

2010

• A Papoulis and S.O Pillai, Probability Random variable and Stochastic process. Mc

Graw Hill 2002

• V Krishnan , Probability and Random processes, Wiley and Sons 2006

12


Introduction

02ME6441 MATHEMATICS FOR MACHINE

LEARNING 3-1-0 4 2020

Pre-requisites: Nil

MODULES

Contact

hours

Sem

Exam

Marks %

MODULE: 1

LINEAR ALGEBRA: Systems of Linear Equations – Matrices, Solving

Systems of Linear Equations. Vector Spaces - Linear Independence, Basis

and Rank, row space null space and column space - Linear Mappings,

Norms, - Inner Products - Lengths and Distances - Angles and Orthogonality

– Orthonormal Basis - Orthogonal Complement - Orthogonal Projections.

Matrix Decompositions.

7 15

MODULE: 2

Determinant and Trace, Eigenvalues and Eigenvectors, Cholesky

Decomposition, Eigen decomposition and Diagonalization, Singular Value

Decomposition, Matrix Approximation.

7 15

FIRST INTERNAL TEST

MODULE: 3

Introduction: Sets, Fields and Events, Definition of probability, Joint,

Conditional and Total Probability, Bayes’ Theorem and applications.

Random Variable:- Definition, Probability Distribution Function,

Probability Density function, Common density Functions-- Binomial

random variable, Uniform Distribution, Normal Distribution, Poisson,

Exponential, Rayleigh, Chi-square, Weibull Distribution, Lognormal,

Gamma and Beta Distribution

9 15

MODULE: 4

Conditional and Joint Distributions and densities, independence of random

variables. Functions of Random Variables: One function of one random

variable, one function of two random variables, two functions of two random

variables. Markov’s inequality, Chebyshev’s inequality, Independent

/uncorrelated random variables, Sum of random variables

9 15


MODULE: 5

Expectation: Fundamental Theorem of expectation, Moments, Joint

moments, Moment Generating functions, Characteristic functions,

Conditional Expectations, Correlation and Covariance, Jointly Gaussian

Random Variables.

10 20

MODULE: 6

Random Processes: -Basic Definitions, Poisson Process, Wiener Process,

Markov Process, Birth- Death Markov Chains, Chapman- Kolmogorov

Equations, Stationarity, Wide sense Stationarity, WSS Processes and LSI

Systems, Power spectral density, White Noise, Periodic and cyclo-stationary

processes. Chebyshev and Schwarz Inequalities, Chernoff Bound, Central

Limit Theorem. Laws of large numbers.

10 20

13

Elective -1

Course No: 02ME6451.1

Course Title: GAME THEORY

Credits: 3-0-0:

Course Objectives

• To provide necessary concept about game theory formulation

• Solution concepts in game theory

• Apply the basic concepts in game theory to various elementary and some advanced

applications.

Syllabus

Strategic and Extensive Form games – Zero sum and Non-zero sum games – Pure Strategy

and Mixed Strategy Nash Equilibrium : Existence and Computing – Mechanism Design.

Course Outcomes

Student will be able to

• acquire a fundamental knowledge of the basic game theory concepts

• apply game theory framework to various situations

• compute Nash Equilibria of a problem formulated as a game.

• formulate a problem using cooperative games framework.

References

• Y Narahari – Game Theory and Mechanism Design – IISc Press and World Scientific

• Anna R. Karlin and Yuval Peres – Game Theory, Alive (Available Online

https://homes.cs.washington.edu/~karlin/GameTheoryBook.pdf )

• Osborne, M.J. An Introduction to Game Theory, Oxford University Press, 2004

• Tamer Basar – Dynamic Non-cooperative Game Theory

• Gibbons, R. A Primer in Game Theory, Pearson Education, 1992

https://homes.cs.washington.edu/~karlin/GameTheoryBook.pdf

14


Introduction

02ME6451.1 GAME THEORY 3-0-0 3 2020

Pre-requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Notions in Game Theory: Definition of a Game - Strategic Interactions –

Strategic Form Games – Preferences – Utilities – Rationality – Intelligence

– Classification of Games

Strategic Form and Extensive Form Games: Strategic Form Games:

Definition and Examples – Extensive Form Games: Definition and

Examples.

6 15

MODULE: 2

Zero Sum Games and Non-zero Sum Games: Definition and Examples of

Zero sum and Non-zero sum games in Strategic and Extensive Form

Dominant Strategy Equilibria: Strong Dominance and Weak Dominance

Equilibria : Definition and Examples.

6 15

FIRST INTERNAL TEST

MODULE: 3

Pure Strategy Nash Equilibrium: Definition and Illustrative Examples of

Pure Strategy Nash Equilibria in Zero Sum and Non-zero Sum Games – Best

Responses and Reaction Curves – Nash Equilibrium as a Fixed Point -

Saddle Point and Pure Strategy Nash Equilibria - Existence of Pure Strategy

Nash Equilibria – Interpretations of Nash Equilibria.

6 15

MODULE: 4

Mixed Strategy Nash Equilibrium: Mixed Strategies – Mixed Strategy Nash

Equilibrium in Zero sum and Non-zero sum games - Maxmin and Minmax

Values in Mixed Strategies - Existence of Mixed Strategy Nash Equilibrium

– Graphical Approach to compute Mixed Strategy Nash Equilibrium.

7

15


MODULE: 5

Computation of Nash Equilibrium: Example for Computing Nash Equilibria:

Pure and Mixed Strategy Nash Equilibria – General Algorithm For Finding

Nash Equilibria of Finite Strategic Games – Complexity of Computing Nash

Equilibria – Introduction of software tools for computing Nash Equilibria.

7 20

MODULE: 6

Cooperative Games: Nash Bargaining solution - Transferable Utility Games

– The Core – The Shapley Value.

Designing games and mechanisms: (Brief introduction) Fair Division –

Social Choice and Voting –– Auctions – Adaptive Decision Making.

7 20

15


Course Title: ROBOTICS AND AUTOMATION

Credits: 3-0-0: 3

Course Objectives

• To introduce the basic concepts, types and parts of robots.

• To provide the knowledge in robot sensors and their applications in end effectors and

vision systems.

• To introduce robot kinematics, programming and the applications in Artificial

Intelligence.

• To outline various applications of robots in automated factory and other systems.

Syllabus

Automation and Robotics, Robot anatomy, robot configuration, Control System and

Components, Motion Analysis And Control, End Effectors , selection and design, Sensors,

Machine Vision, Robot Programming, Robot Languages, Robot Application, Recent Trends In

Robotics.

Course Outcomes

The students will be able to:

• Identify sensors used for various robotics application and the associated artificial

intelligence Classify robot used for automated factory

• Develop mathematical model for robot kinematics and path planning

• Analyse the principle behind robotic drive system, end effectors, sensor and vision

systems.

References

• K S Fu, Ralph Gonzalez and C S G Lee, “Robotics, Control, Sensing, Vision and

Intelligence”, Tata McGraw-Hill Education, 2008.

• Saeed B. Niku, “Introduction to Robotics: Analysis, Control, Applications”, John

Wiley & Sons, 2011.

• M.P. Groover, “Industrial Robotics – Technology, Programming and Applications”,

McGraw-Hill, 2001.

• Tadej Bajd, Matjaž Mihelj and Marko Munih, “Introduction to Robotics”, Springer,

2013.

• Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani and Giuseppe Oriolo, “Robotics-

Modelling, Planning and Control”, Springer-Verlag, 2010.

16


Introduction

02ME6451.2 ROBOTICS AND AUTOMATION 3-0-0 3 2020

Pre-requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Introduction: Automation and Robotics, Robot anatomy, robot

configuration, motions joint notation work volume, robot drive systems-

Salient Features, Applications and Comparison of all Drives, control

system and dynamic performance, precision of movement.

7 15

MODULE: 2

Robot Sensors: Principles of Sensing, Sensors of Movement, Contact

Sensors, Tactile, Proximity and Ranging Sensors.

End Effectors: Grippers: Types, operation, mechanism, force analysis,

tools as end effectors consideration in gripper selection and design.

7 15

FIRST INTERNAL TEST

MODULE: 3

Kinematics of robot: Direct and inverse kinematics problems and

workspace, inverse kinematics solution for the general 6 DoF manipulator,

redundant and over-constrained manipulators. D-H Parameters.

7 15

MODULE: 4

Planning and control: Trajectory planning, position control, force control,

hybrid control.

Automates storage and retrieval system and Autonomous guided vehicles

in factory environment- Types, interfacing and various systems.

Robot Languages: Textual robot languages, Generation, Robot language

structures, Elements in function.

7 15


MODULE: 5

Machine Vision: Functions, image processing and analysis, training the

vision system, robotic applications.Industrial and medical robotics:

Application in manufacturing processes such as casting, welding, painting,

machining, heat treatment and nuclear power stations, etc; medical robots:

image guided surgical robots, radiotherapy, cancer treatment, etc.

7 20

MODULE: 6

Collaborative Robots: Collaborative Industrial Robot System,

Collaborative Robot, Collaborative Operation. Collaborative Robot

Grippers, Applications.

Mobile Robots: Mobile Robot Kinematics, Navigation.

Humanoid Robotics: Biped Locomotion, Imitation Learning.

7 20

17


Course Title: ADAPTIVE SIGNAL PROCESSING

Credits: 3-0-0: 3

Course Objectives

The course is designed to provide students a strong background in the concept of signal processing

and apply it to the signals which can process adaptively.

Syllabus

Adaptive systems - definitions and characteristics - applications - properties- Correlation

matrix and its properties- z transform- Searching performance surface- gradient estimation -

performance penalty – LMS algorithm- sequential regression algorithm - adaptive recursive

filters - Kalman filters- Applications adaptive modelling and system identification-adaptive

modelling for multipath communication channel, geophysical exploration, inverse adaptive

modelling, equalization, and deconvolution-adaptive equalization of telephone channels

Course Outcomes

The students are expected to :

• Understand basic concepts of adaptive signal processing

• Top-level understanding of the convergence issues, computational complexities and

optimality of different filters

References

1. Bernard Widrow and Samuel D. Stearns, “Adaptive Signal Processing”, Person

Education, 2005.

2. Simon Haykin, “Adaptive Filter Theory”, Pearson Education, 2003.

3. John R. Treichler, C. Richard Johnson, Michael G. Larimore, “Theory and Design of

Adaptive Filters”, Prentice-Hall of India, 2002

4. S. Thomas Alexander, “Adaptive Signal Processing - Theory and Application”,

Springer-Verlag.

5. D. G. Manolokis, V. K. Ingle and S. M. Kogar, “Statistical and Adaptive Signal

Processing”, Mc Graw Hill International Edition, 2000.

18


Introduction

02ME6451.3 ADAPTIVE SIGNAL PROCESSING 3-0-0 3 2020

Pre-requisites: (1) Basic knowledge of Signal processing at UG/PG Level.

(2) Basic knowledge of different transform domains like Fourier, Laplace, Z transform etc.

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Adaptive systems - definitions and characteristics – applications -

properties-examples - adaptive linear combiner-input signal and weight

vectors, performance function, Gradient and minimum mean square error,

Alternate expressions of gradient

6 15

MODULE: 2

Theory of adaptation with stationary signals: Correlation matrix and its

properties, its physical significance. Eigen analysis of matrix, structure of

matrix and relation with its eigen values and eigen vectors. Z Transforms in

Adaptive signal processing and its applications

8 15

FIRST INTERNAL TEST

MODULE: 3

Searching performance surface - stability and rate of convergence -

learning curve-gradient search - Newton's method - method of steepest

descent - comparison - gradient estimation - performance penalty - variance

-excess MSE and time constants – misadjustments

8 15

MODULE: 4

LMS algorithm - convergence of weight vector-LMS/Newton algorithm -

properties - sequential regression algorithm - adaptive recursive filters -

random-search algorithms

8 15


MODULE: 5 Kalman filters - recursive minimum mean square estimation for scalar

random variables- statement of Kalman filtering problem innovation

process-estimation of the state-filtering-initial conditions-Kalman filter as

the unifying basis for RLS filters

7 20

MODULE: 6

Applications - adaptive modeling and system identification adaptive

modeling for multipath communication channel, geophysical exploration,

inverse adaptive modeling, equalization, and deconvolution-adaptive

equalization of telephone channels, Adaptive interference cancelling:

applications in Bio-signal processing

8 20

19


Course Title: BIOMETRIC TECHNOLOGIES

Credits: 3-0-0: 3

Course Objectives

• To familiarize the fundamental understanding of basic concepts in various

biometric traits

Syllabus

Biometric fundamentals and standards- Physiological Biometrics- Behavioural biometrics-

User interfaces- Biometric applications- Assessing the Privacy Risks of Biometrics- Fusion in

biometrics

Course Outcomes

On successful completion of the course, students will be able to:

1. Analyse the basic engineering principles underlying biometric systems

2. Understand and analyse biometric systems and be able to analyse and design basic

biometric system application

3. Identify the sociological issues associated with the design and implementation of

biometric systems

References

1. Anil K Jain, Patrick Flynn and Arun A Ross, “Handbook of Biometrics”, Springer,

USA, 2010.

2. John R Vacca, “Biometric Technologies and Verification Systems”, Elsevier, USA,

2009

3. Samir Nanavati, Michael Thieme and Raj Nanavati, “Biometrics – Identity Verification

in a Networked World”, John Wiley and Sons, New Delhi, 2003.

4. Paul Reid, “Biometrics for Network Security, Pearson Education, New Delhi, 2004.

5. Reid M. Bolle et al, “Guide to Biometrics, Springer”, USA, 2004

6. David D Zhang, “Automated Biometrics: Technologies and Systems”, Kluwer

Academic Publishers, New Delhi, 2000.

7. Arun A Ross, Karthik Nandakumar and Jain A K, “Handbook of Multi-biometrics”,

Springer, New Delhi 2011.

20


Introduction

02ME6451.4 BIOMETRIC TECHNOLOGIES 3-0-0 3 2020

Pre-requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Biometric fundamentals and standards: Definition, Biometrics versus

traditional techniques, Characteristics, Key biometric processes:

Verification - Identification - Biometric matching, Performance measures in

biometric systems: FAR, FRR, FTE rate, EER and ATV rate. Assessing the

privacy risks of biometrics - Designing privacy sympathetic biometric

systems, Different biometric standards, Application properties.

5 15

MODULE: 2

Physiological biometrics: Finger scan, Facial scan, Iris scan, Retina scan,

Ear scan- components, working principles, competing technologies,

strengths and weaknesses, Other Physiological Biometrics: Palm print,

Hand vascular geometry analysis, Knuckle, DNA, Dental, Cognitive

Biometrics - ECG, EEG. Automated fingerprint identification systems

7 15

FIRST INTERNAL TEST

MODULE: 3

Behavioural biometrics: Signature scan, Keystroke scan, Voice scan, Gait

recognition, Gesture recognition, Video face, Mapping the body technology: components, working principles, strengths and weaknesses

7 15

MODULE: 4

User interfaces: Biometric interfaces: Human machine interface - BHMI

structure, Human side interface: Iris image interface - Hand geometry and

fingerprint sensor, Machine side interface: Parallel port - Serial port -

Network topologies, Case study: Palm Scanner interface.

7 15


MODULE: 5 Biometric applications: Categorizing biometric applications, Application

areas: Criminal and citizen identification – Surveillance - PC/network access

- E-commerce and retail/ATM, Costs to deploy, Issues in deployment,

Biometrics in medicine, cancellable biometrics.

Assessing the Privacy Risks of Biometrics: Designing Privacy-

Sympathetic Biometric Systems - Need for standards - different biometric

standards.

7 20

MODULE: 6

Fusion in biometrics: Multi-biometrics, information fusion in biometrics,

Levels of fusion: Sensor level - Feature level - Rank level - Decision level

fusion - Score level fusion, Fusion incorporating ancillary information.

7 20

21


Course Title: DIGITAL IMAGE PROCESSING

Credits: 3-0-0: 3

Course Objectives

(1) To extend the knowledge on DSP to 2-D signal processing and hence to analyze digital

Images.

(2) To study the various aspects of image processing like restoration, enhancement,

compression, etc.

Syllabus

Gray scale and colour Images, image sampling, quantization and reconstruction, Human visual

perception, transforms: DFT, FFT, WHT, Haar transform, KLT, DCT, Filters in spatial and

frequency domains, histogram-based processing, Edge detection - non parametric and model

based approaches, LOG filters, Image Restoration - PSF, circulant and block-circulant

matrices, deconvolution, restoration using inverse filtering, Wiener filtering and maximum

entropy-based methods, Binary morphology, dilation, erosion, opening and closing, gray scale

morphology, applications, thinning and shape decomposition, Image and video compression :

Lossy and lossless compression, Transform based sub-band decomposition, Entropy Encoding,

JPEG, JPEG2000, MPEG, Computer tomography - parallel beam projection, Radon transform,

Back-projection, Fourier-slice theorem, CBP and FBP methods, Fan beam projection, Image

texture analysis - co-occurrence matrix, statistical models, Hough Transform, boundary

detection, chain coding, segmentation and thresholding methods.

Expected Outcomes

The students are expected to:

(1) Attain an ability to extend the one-dimensional DSP principles to two-dimension;

(2) Have good knowledge in various image processing methodologies.

References

1. A. K. Jain, Fundamentals of digital image processing, PHI, 1989.

2. Gonzalez and Woods, Digital image processing ,3/E Prentice Hall, 2008.

3. S Jayaraman, S Esakkirajan, T Veerakumar, Digital image processing, Tata McGraw Hill,

2015.

4. R.M. Haralick, and L.G. Shapiro, Computer and Robot Vision, Addison Wesley, 1992.

5. R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, MGH International Edition, 1995.

6. W. K. Pratt, Digital image processing, Prentice Hall, 1989.

7. David Forsyth & Jean Ponce, Computer Vision: A modern approach, Pearson Edn., 2003

8. C . M. Bishop, Pattern Recognition & Machine Learning, Springer 2006

22


Introduction

02ME6451.5 DIGITAL IMAGE PROCESSING 3-0-0 3 2020

Pre-requisites: Basic knowledge in DSP and Linear Algebra at UG level.

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Image representation - Gray scale and colour Images, Representation of 2D

signals, image sampling, quantization and reconstruction.

Two dimensional orthogonal transforms- Digital images, Human visual

perception, transforms: DFT, FFT, DCT, WHT, Haar transform, KLT, and

Singular Value Decomposition.

2D convolution and correlation- Graphical Method and Matrix method

8 15

MODULE: 2

Image enhancement - filters in spatial and frequency domains, histogram-

based processing, image subtraction, image averaging, Spatial filtering-

smoothing filters, sharpening filters, Frequency domain methods: low pass

filtering, high pass filtering, homomorphic filtering.

Edge detection - non parametric and model based approaches, LOG filters,

localization problem.

8 15

FIRST INTERNAL TEST

MODULE: 3

Image Restoration - PSF, circulant and block-circulant matrices,

deconvolution, restoration using inverse filtering, Wiener filtering and

maximum entropy-based methods.

Image texture analysis - co-occurrence matrix, measures of textures,

statistical models for textures. Hough Transform, boundary detection, chain

coding, segmentation and thresholding methods.

8 15

MODULE: 4

Mathematical morphology - binary morphology, dilation, erosion, opening

and closing, duality relations, gray scale, morphology, applications such as

hit-and-miss transform, thinning and shape decomposition.

8 15


MODULE: 5

Image and Video Compression Standards: Lossy and lossless compression

schemes: Transform Based, Sub-band Decomposition, Entropy Encoding,

JPEG, JPEG2000, MPEG

6 20

MODULE: 6

Computer tomography - parallel beam projection, Radon transform, and its

inverse, Back-projection operator, Fourier-slice theorem, CBP and FBP

methods, ART, Fan beam projection.

6 20

23


Course Title: PATTERN RECOGNITION

Credits: 3-0-0: 3

Course Objectives:

• Study the fundamental algorithms for pattern recognition

• To instigate the various classification techniques

• To originate the various structural pattern recognition and feature extraction

techniques.

Syllabus

Basics of pattern recognition, Parametric and Non Parametric technique, Unsupervised

Methods, Linear discriminant based classifiers and tree classifiers, Regression, Graphical

methods, Recent Advances in Pattern Recognition.

Expected Outcomes:

• Understand and apply various algorithms for pattern recognition

• Realize the clustering concepts and algorithms

• Bring out structural pattern recognition and feature extraction techniques Program

References:

1. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley, 2001

2. Pattern Recognition, S.Theodoridis and K.Koutroumbas, 4th Ed., Academic Press, 2009

3. Pattern Recognition and Machine Learning, C.M.Bishop, Spring

24


Introduction

02ME6451.6 PATTERN RECOGNITION 3-0-0 3 2020

Pre-requisites: Base knowledge of Probability and Statistics

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Introduction to pattern and classification, supervised and unsupervised

learning, Clustering vs classification, Bayesian Decision Theory-Minimum

error rate classification Classifiers, discriminant functions, decision surfaces

-The normal density and discriminant-functions for the Normal density.

6 15

MODULE: 2

Parametric and Non Parametric technique: Parametric estimation

Technique:-Maximum-Likelihood (ML) estimation, Bayesian estimation,

Non Parametric density estimation:-Parzen-window method, K-Nearest

Neighbour method.

6 15

FIRST INTERNAL TEST

MODULE: 3

Linear discriminant based classifiers and tree classifiers: Linear

discriminant function based classifiers-Perceptron-Minimum Mean Squared

Error (MME) method, Support Vector machine, Decision Trees: CART,

C4.5, ID3

8 15

MODULE: 4

Unsupervised Methods: Component Analysis and Dimension Reduction:-

The Curse of Dimensionality ,Principal Component Analysis ,Fisher Linear

Discriminant analysis. Clustering:- Basics of Clustering; similarity /

dissimilarity measures; clustering criteria. Different distance functions and

similarity measures, K-means algorithm.

6 15


MODULE: 5

Regression, Graphical methods: Regression:- Introduction to Linear

models for regression, Polynomial regression and Bayesian regression,

Graphical Models:-Bayesian belief network and Hidden Markov Models

8 20

MODULE: 6

Recent Advances:- Neural network structures for pattern recognition - Self

organizing networks - Fuzzy logic – Fuzzy pattern classifiers -Pattern

classification using Genetic Algorithms.

6 20

25


Course Title: INTRODUCTION TO HUMAN COMPUTER INTERFACE

Credits: 3-0-0:

Course Objectives

• Introduce the student to the concepts of human-computer interaction.

• Understand the need of good user interface design

• Provide user interface design with concepts and strategies for making design

decisions.

• Expose user interface design tools, techniques, and ideas for interface design.

Syllabus

HCI foundation and history; Usability life cycle and methods; Design rules and guidelines;

Empirical research methods; Models in HCI- GOMS, Fitts’ law and Hick-Hyman’s law; Task

analysis; Dialogue design; Cognitive architecture and HCI ; Graphic User Interfaces &

aesthetics; Usability Testing; UML,OOP,OOM; Design Case Studies.

Course Outcomes

• Familiarization with basics of HCI

• Comprehend and understand various user interface and design methodologies

• Equip to select appropriate design contents to develop cognitive model

• Develop sufficient technical know-how to apply the fundamental concepts of HCI to

solve real world problems

References

1. Human – Computer Interaction. ALAN DIX, JANET FINCAY, GRE GORYD,

ABOWD, RUSSELL BEALG,., 3rd Edition, Pearson Education, 5th edition, ISBN-13:

978-0130461094, ISBN-10: 0130461091, 2014

2. “Interaction Design”, Prece, Rogers, Sharps, Wiley, ISBN: 978-1-119-02075-2., 3rd

Edition, 2011.

3.The essential guide to user interface design”, Wilbert O Galitz, “Wiley, 3rdEd,

2007, ISBN: 978-0-471-27139-0.

4. B.Shneiderman; Designing the User Interface,Addison Wesley 2000 (Indian

Reprint).

26


Introduction

02ME6451.7 INTRODUCTION TO HUMAN

COMPUTER INTERFACE 3-0-0 3 2020

Pre-requisites: Nil

MODULES Contact

hours

Sem Exam

Marks %

MODULE: 1

Introduction to HCI: Why Design for Usability? Historical Perspective:

machinery, computers, PCs and GUI networks, mobile, Possible Futures

Human Perception, Information Presentation and Layout: Perception,

gestalt perception, typography, Color, Graphic design Displays, Paper, and

other Output Devices, Information Visualization

6 15

MODULE: 2

Model Based System Design: Basic idea, introduction to different types of

models, GOMS family of models (KLM and CMN-GOMS), Fitts’ law and

HickHyman’s law

The Human Body and Device Design: Input Devices and Ergonomics,

Virtual Reality, GOMS Keystroke- Level Modelling, Time scales and the

illusion of Multitasking, Hypothesis Testing and Statistical Significance

7 15

FIRST INTERNAL TEST

MODULE: 3

Guidelines in HCI: Shneiderman’s eight golden rules, Norman’s seven

Principles, Norman’s model of interaction, Nielsen’s ten heuristics with

example of its use, Heuristic evaluation, Contextual inquiry, Cognitive

walkthrough

6 15

MODULE: 4

Task Modeling and Analysis: Hierarchical task analysis (HTA),

Engineering task models and Concur Task Tree (CTT)

7 15


MODULE: 5

Dialog Design: Introduction to formalism in dialog design, design using

FSM (finite state machines) State charts and (classical) Petri Nets in dialog

design

7 20

MODULE: 6

Cognitive Architecture: Introduction to CA, CA types,relevance of CA in IS

design, Model Human Processor (MHP)

Case studies

6 20

27

Course No: 02CA6001

Course Title: RESEARCH METHODOLOGY

L-T-P-Credits: 1-1-0 : 2

Course Objectives

• Students should get the ability to identify problem related to research topic and to characterize

the research problems. • To developed physical insight about the research design and to develop a more reliable

design. • To study about the research by the methods of data analysis and to develop report and thesis

according to the data.

Syllabus

Introduction to research, objectives of research-types of research, research problems review of

literature, research design, data collection and analysis, research reporting, research application

and ethics.

Expected outcomes

Students will develop an understanding of the potential benefits and technical challenges

associated with conducting a research and the development of thesis and reports according to

the research carried out.

References:

1. Donald R. Cooper, Pamela S. Schindler, Business Research Methods, Tata McGraw-

Hill.

2. Stuart Melville and Wayne Goddard, Research Methodology: An Introduction for Science and

Engineering Students, Wiley

3. C. R. Kothari, Research Methodology Methods and Technique, Tata McGraw-Hill.

4. Leedy, P.D. and Ormirod, J.E., Practical Research : Planning and Design, Prentice Hall

5. Donald H. McBurney, Research Methods, Thomson Learning.

6. Turabian, K.L Revised by Grossman, J. and Bennert, A., A Manual for writers of term papers, thesis and dissertation, University of Chicago press.

28


Introduction

02CA6001 RESEARCH METHODOLOGY 1-1-0 2 2020

Pre-requisites: Nil

MODULES Contact

Hours

Sem

Exam

Marks %

Module I:

Introduction To Research : Meaning and definition of Research- Motivation

and Objectives of research-Types of research- fundamental – applied

descriptive-analytical– qualitative-quantitative-conceptual empirical-research

and scientific methods-research process-criteria for good research

5 9

Module II:

Research Problems : Sources Of Research Problems-Characteristics Of A

Research Problem- Problem Defining Techniques-Sources Of Literature

Review Of Literature-Issues And Gap Areas Identification-Purpose of study-

exploratory and descriptive-qualities of good hypothesis-null and alternative

hypothesis- importance of hypothesis testing

4 9

FIRST ASSESSMENT Module III:

Research Design: Features of good design- different research designs –

Laboratory and field experiments- measurement concepts- scales and levels-

Measurement of variables- Factors affecting validation- Internal and external

validation- Reliability- Stability methods- Development of experimental and

sample designs.

4 9

Module IV:

Data Collection And Analysis: Methods of data collection- Data sources –

Surveys and questionnaires- Methods of data collection and their utility-

Concepts of statistical population- Sampling techniques – Probabilistic and

non-probabilistic samples- Sample size determination issues- Primary and

secondary data analysis- Use of computers, internet and library- Data analysis

with statistical packages- Preparation of data for analysis

5 9

SECOND ASSESSMENT

Module V:

Research Reporting : Purpose of written reports- Concept of audience- Types

of reports- Structure and components of reports- Technical report and thesis-

Features of a good thesis- Layout and language of reports- Illustrations- Tables-

Referencing- Footnotes- Intellectual contents of the thesis- Making oral

presentations- Effective communications- Publishing research findings-

Defending the thesis.

5 12

Module VI:

Research Application And Ethics: Application of results of research

outcome- environmental impacts- Professional ethics- Ethical issues and

committees- Copy right- Royalty- Intellectual property rights- Patent laws and

patenting- Reproduction of published material- Plagiarism- Citation and

acknowledgement- Reproducibility and accountability- Developing research

proposals.

5 12

END SEMESTER ASSESSMENT

29

SEMINAR

Course No: 02ME6461

Course Title: SEMINAR I

Credits: 0-0-2:

Internal marks: 100

To enable a student to be familiar with communication skills. Each student is required to select a

topic on advanced technologies in Artificial Intelligence and allied subject domains and get it

approved by the faculty-in-charge of seminar. He/she should give a presentation with good

quality slides. An abstract of the seminar should be submitted to the faculty members well in

advance before the date of seminar. He/she should also prepare a well-documented report on

the seminar in approved format and submit to the department

Student is expected to learn:

a. How to Make a Presentation

1. Verbal

2. Non Verbal

3. Power Point

b. How to write a report

1. Abstract

2. Body

3. Conclusions

4. Executive Summary

c. Group Discussion

1. Share the work with a group

2. Modularization of the work

d. Communication

1. Horizontal

2. Vertical

Evaluation: A committee with the Head of the department as the chairman and two faculty

members from the department as members shall evaluate the seminar based on the coverage of

the topic, presentation and ability to answer the questions put forward by the committee.

Students will be Given a Topic of Importance and are expected

A. To Present the Topic Verbally in 30 minutes + Question Answering

B. To Present the Topic as a Report in not less than 50 Pages

30

Course No: 02ME6471

Course Title: PROGRAMMING LAB

Course Credit: 0-0-2:1 Year: 2020

Internal Mark: 100

Course Objectives:

• To implement the applications of linear and non-linear data structures.

• To implement algorithms for various sorting and searching techniques.

• To develop program with minimum time and space complexity

Syllabus

The syllabus consists of 20 experiments covering the topics of the course “Data Structures and

Algorithms”. Student requires a strong knowledge in any one of the programming languages.

Expected Course Outcomes:

At the end of the course, student will able to

• Develop strong knowledge on the implementation aspects of algorithms

• Implement efficient algorithms by identifying suitable data structures to solve real world

problems.

• Implement existing algorithms and compare the time complexity.

• Analyze the efficiency of algorithms by implementing programs using different data

structures.

List of Experiments:

1. a. Write a program for the following operations on Single Linked List.

(i) Creation (ii) insertion (iii) deletion (iv) traversal

b. To store a polynomial expression in memory using single linked list

2 Write a program to implement following using single linked list

(i). to check whether list is Palindrome.

(ii). Reverse a singly Link List

(iii). Move last element to front of a singly link list.

(iv). Detect a Loop in Singly Link List. [Hint: Floyd’s Cycle-Finding Algorithm.]

3. a. Write a program for the following operations on Circular Linked List.

31


4. Write a program to implement following using doubly link list

(i). Swap kth node from beginning with kth node from end

(ii). Merge two sorted arrays with O(1) extra space

5. Write a program to

i). To find occurrence of each element with minimal time complexity

ii) to construct a sparse matrix

6. Write a program to implement the following:

(i). Uses Stack operations to convert infix expression into postfix expression.

(ii). Uses Stack operations for evaluating the postfix expression.

(iii). Tower of Hanoi

7. Write a program to implement circular queue with following operation


8 Write a program to check if a queue can be sorted into another queue using a stack

9. Write a program to implement priority queue using max Heap.

10. Write a program to find the kth largest and smallest element in a list of number without

sorting.[Hint: Max Heap]

11. Write a program to represent graph using

(i). Adjacency Matrix

(ii). Adjacency List

12. Write a program to perform the following:

a. Create a binary search tree.

b. Display the BST using preorder, post order and in order.

c. Find distance between two nodes of a Binary Search Tree

d. Remove all leaf nodes in a BST.

13. Write a program to implement

a. AVL Tree

b. RB Tree

with following operations

(i) insertion (ii) deletion (iii) display

https://www.geeksforgeeks.org/swap-kth-node-from-beginning-with-kth-node-from-end-in-a-linked-list/

https://www.geeksforgeeks.org/merge-two-sorted-arrays-o1-extra-space/

https://www.geeksforgeeks.org/check-queue-can-sorted-another-queue-using-stack/

https://www.geeksforgeeks.org/find-distance-two-nodes-binary-search-tree/

32

14. Write a program to implement search techniques

a. Linear Search

b. Binary Search

c. Hashing using Linear Probing

15. Write a program to implement following sorting technique

a. Merge Sort

b. Quick Sort

c. Radix Sort

16. Write a program with optimized time to search an element in a sorted and rotated array.

[Hint: A[] ={23,56,78,12,18,21], from index k=3 onwards the array A is a sorted rotated

array.]

17. Write a python program to sort an array containing two types of elements in O(n) time.

Hint: A[] ={1,0,0,1,1,0,0,1}

18. (i). Write a program to generate Fibonacci series using dynamic programming methodology.

(ii). Write a program to implement matrix chain multiplication.

19. Write a program to implement the following graph traversal algorithms:

a. Depth first search.

b. Breadth first search.

20. Write a program to implement

i) .Dijkstra’s Algorithm

ii). Minimum spanning tree of a weighted graph

m.tech. programme mechanical engineering artificial...

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