sandip university school of engineering and technology second year of computer science ... ·...

Sandip University

School of Engineering and Technology

Second Year of Computer Science and Engineering (2017 Course)

Modern Mathematics

School: Engineering & Technology Programme: B.Tech

Year: Second Year Semester – II

Course: Modern Mathematics Course Code: YCA401

Theory: 04 Hrs/ Week

Credits: 04

End Semester: 50 Marks

CIA: 50 Marks

Prerequisite:- Basic Mathematics

Course Objectives:

To solve order linear differential equations.

To find the roots of polynomial equations by using numerical methods and to learn the

concepts of interpolation.

To understand the basic concepts of probability distributions, correlation, regression and

fitting of curves.

To understand the basic concepts of linear Programing problems.

To find the optimal solution of LPP by using simplex and dual simplex method

Course Outcomes: On completion of the course, student will be able to–

Solve linear differential equation using appropriate techniques.

Apply statistical methods like correlation, regression analysis and probability theory for

analysis and prediction of a given data as applied to machine intelligence.

Solve Linear Programming Problems.

.

Course Contents

Unit I Linear Differential Equations 08 Hours

Linear Differential Equations with constant coefficients, Homogeneous Linear differential equations,

Applications of LDE with constant coefficients to Electrical systems.

Unit II Numerical Methods 08 Hours

Zeroes of transcendental and polynomial equation using Bisection method, Secant method, Regula

falsi method and Newton Raphson method, Rate of convergence of above methods.

Interpolation: Finite differences, difference tables, Newton’s forward and backward interpolation,

Lagrange’s and Newton’s divided difference formula for unequal intervals.

Unit III Probability and Statistics 08 Hours

Probability: Random variable, Binomial, Poisson, and Normal distributions.

Fitting of curves: Coefficient of correlation and lines of regression of bivariate data, Fitting of

Curves by method of Least squares.

Unit IV Linear Programming Problems - I 07 Hours

Formulation of Linear Optimization Problems, constraints, Graphical method to solve LPP, Standard

and Canonical forms, basic solutions and feasible solutions, optimal solutions by simplex method.

Unit V Linear Programming Problems - II

07 Hours

Artificial Variables, Duality concept, formulation of dual problems, duality principle, Relation

between Primal and Dual L.P.P., Dual simplex method.

Books:

Text:

1. B. S. Grewal, Higher Engineering Mathematics, 43rd edition, Khanna Publishers.

2. A text book of Applied Mathematics: Vol. I, II and III by J. N. Wartikar& P. N. Wartikar ,

VidyarthiGrihaPrakashan, Pune.

3. Operations Research by T. A. Taha.

Reference:

1. Ervin Kreyszig, Advanced Engineering Mathematics, 10th edition, John Wiley and Sons.

2. Peter V. O'Neil, Advanced Engineering Mathematics, 7th edition, Cengage Learning.

3. Operations Research by S. D. Sharma.

Sandip University



Computer Graphics



Course: Computer Graphics Course Code: YCA402


Credits: 04


CIA: 50 Marks

Prerequisite: -C, C++, Linear algebra, Matrices and Basic Data structures.

Course Objectives:

To acquaint the learner with the basic concepts of Computer Graphics

To learn the various algorithms for generating and rendering graphical figures

To get familiar with mathematics behind the graphical transformations

To understand and apply various methods and techniques regarding projections, animation,

shading, illumination and lighting


To understand the various computer graphics hardware and display technologies.

Apply mathematics and logic to develop Computer programs for elementary graphic

operations

Develop scientific and strategic approach to solve complex problems in the domain of

Computer Graphics

Develop the competency to understand the concepts related to Computer Vision and Virtual

reality.

Course Contents

Unit I Basic of Computer Graphics & Devices 08 Hours

Introduction to computer graphics, state of art applications of computer graphics, pixel, frame buffer,

resolution, aspect ratio. Video display devices: CRT (Raster scan and random scan displays), flat

panel displays. Interactive devices: joysticks, touch panels, light pens. Data generating devices:

scanners and digitizers.

Unit II Scan Conversion 08 Hours

Line and line segments, line and circle drawing algorithms: DDA and Bresenham , Line styles: thick,

dotted and dashed. Antialising and antialising techniques. Character generating methods: stroke and

bitmap method, Multiligual character standards. Display Files: display file structure, algorithms and

display file interpreter. Primitive operations on display file.

Unit III Polygons and Clipping Algorithms 08 Hours

Introduction to polygon, types: convex, concave and complex. Representation of polygon, Inside test,

polygon filling algorithms – flood fill, seed fill, scan line fill and filling with patterns.

Windowing and clipping: viewing transformations, 2-D clipping: Cohen – Sutherland algorithm,

Polygon clipping: Sutherland Hodgeman algorithm, generalized clipping.

Unit IV Geometric Transformations 07 Hours

2-D transformations: Introduction, matrices, Translation, scaling, rotation, homogeneous

coordinates and matrix representation, translation, coordinate transformation, rotation about an

arbitrary point, inverse and shear transformation.

3-D transformations: Introduction, 3-D geometry, primitives, 3-D transformations and matrix

representation, rotation about an arbitrary axis, concept of parallel and perspective projections,

viewing parameters.

Unit V Curves, Fractals, Hidden Surfaces, Light and

Color Models

07 Hours

Hidden surfaces: introduction, back-face removal algorithm: Painter’s algorithm, binary space

partition, Warnock algorithm, Z –buffer.

Light and Color: Introduction, Diffused illumination, point source illumination, Shading

Algorithms, reflections, shadows, ray tracing, Color models and tables: RGB, HIS,

Introduction to curve generation, interpolation, B-splines, Bezier curve, Blending function,

fractals, Fractal lines and surfaces.

Books:

Text:

1. S. Harrington, “Computer Graphics”, 2nd Edition, McGraw-Hill Publications, 1987, ISBN 0

– 07 –100472 – 6.

2. D. Rogers, “Procedural Elements for Computer Graphics”, 2nd Edition, Tata McGraw-Hill

Publication, 2001, ISBN 0 – 07 – 047371 – 4.

Reference: 1. Sinha &Udai , “Computer Graphics”, Tata McGraw-Hill, 2007, ISBN 978-0-07-0634374.

2. J. Foley, V. Dam, S. Feiner, J. Hughes, “Computer Graphics Principles and Practice”, 2nd

Edition,

3. Pearson Education, 2003, ISBN 81 – 7808 – 038 – 9.

4. D. Hearn, M. Baker, “Computer Graphics – C Version”, 2nd Edition, Pearson Education,

2002, ISBN 81 – 7808 – 794 – 4.

5. D. Rogers, J. Adams, “Mathematical Elements for Computer Graphics”, 2nd Edition, Tata

McGraw-Hill Publication, 2002, ISBN 0 – 07 – 048677 – 8.

Sandip University



Fundamentals of Java Programming



Course: Fundamentals of Java

Programming

Course Code: YCA403


Credits: 04


CIA: 50 Marks

Prerequisite: - C, C++

Course Objectives:

To understand fundamental concepts of OOP such as data abstraction, encapsulation,

inheritance, dynamic binding and polymorphism.

To understand the implementation of OOP concepts with JAVA.

To learn the features of core java that makes it more popular.


Implement Object Oriented Programming Concepts

Use and create packages and interfaces in a Java program

Use graphical user interface in Java programs

Create Applets

Implement exception handling in Java

Implement Multithreading

Use Input/output Streams

Course Contents

Unit I Introduction and Basics of OOP 08 Hours

Introduction: Programming language Types and Paradigms, Why Java , Flavors of Java, Features of

Java Language, JVM –The heart of Java, Java’s Magic Bytecode, Java Program Development, Data

types, Loops, Java Source File Structure, Compilation, Executions.

Class & Object: Class Fundamentals,Object & Object reference, Object Life time & Garbage

collection, Creating and Operating Objects, Constructor & initialization code block, Access Control,

Modifiers, Inner Class & Anonymous Classes, Abstract Class & Interfaces, Defining Methods,

Argument Passing Mechanism , Method Overloading, Recursion, Use of “this “ reference, Use of

Modifiers with Classes & Methods.

Unit II Inheritance, Polymorphism and Packages 08 Hours

Inheritance : Use and Benefits of Inheritance in OOP, Types of Inheritance in Java, Inheriting

Data members and Methods , Role of Constructors in inheritance , Overriding Super Class

Methods, Use of “super”, Polymorphism in inheritance, Type Compatibility and Conversion

Implementing interfaces. Packages: Organizing Classes and Interfaces in Packages , Package as Access Protection , Defining

Package ,CLASSPATH Setting for Packages , Making JAR Files for Library Packages Import and

Static Import Naming Convention For Packages.

Unit III Array, Strings and Thread 08 Hours

Array & String: Defining an Array, Initializing & Accessing Array, Multi –Dimensional Array,

Operation on String, Mutable & Immutable String, Using Collection Bases Loop for String,

Tokenizing a String, Creating Strings using StringBuffer.

Thread: Understanding Threads , Needs of Multi-Threaded Programming ,Thread Life-Cycle,

Thread Priorities ,Synchronizing Threads.

Unit IV Exception Handling and File Handling 07 Hours

Exception Handling: The Idea behind Exception, Exceptions & Errors, Types of Exception, Control

Flow In Exceptions, JVM reaction to Exceptions, Use of try, catch, finally, throw, throws in

Exception Handling, In-built and User Defined Exceptions, Checked and Un-Checked Exceptions.

File Handling: Input/Output Operation in Java (java.io Package), Streams and the new I/O

Capabilities, Understanding Streams, The Classes for Input and Output, The Standard Streams,

Working with File Object, File I/O Basics, Reading and Writing to Files, Buffer and Buffer

Management.

Unit V GUI Programming and Event Handling 07 Hours

GUI Programming: Designing Graphical User Interfaces in Java, Components and Containers,

Basics of Components, Using Containers, Layout Managers, AWT Components, Adding a Menu to

Window, Extending GUI Features Using Swing Components, Java Utilities (java.util Package) The

Collection Framework : Collections of Objects , Collection Types, Sets , Sequence, Map,

Understanding Hashing, Use of ArrayList & Vector.

Event Handling: Event-Driven Programming in Java, Event- Handling Process, Event-Handling

Mechanism, The Delegation Model of Event Handling, Event Classes, Event Sources, Event

Listeners.

Books:

Text:

1. E Balagurusamy "Programming with Java", Fifth Edition, McGraw Hill Education, 2014,

ISBN: 978-9351343202.

2. R. Nageswara Rao, “Core Java: An Integrated Approach”, 1est Edition, DreamtechPress ,

2016, ISBN: 978-9351199250.

3. Herbert Schildt, “Java A Beginner's Guide”, 6th Edition, Tata McGraw-Hill , 2014, ISBN:

9789339213039.

Reference: 1. Herbert Schildt"Java: The Complete Reference"; Ninth Edition, Oracle Press, ISBN 978-0-

07-180855-2.

2. D.T.Editorial Serices "Java 8 programming" Black Book.

3. D.T Editorial Services, R. Nageswara Rao"Core Java: An Integrated Approach"

Sandip University



Advanced Data Structures


Year: Second Year Semester – IV

Course: Advanced Data Structures Course Code: YCA404


Credits: 04


CIA: 50 Marks

Prerequisite:-

Data Structures and algorithms

Basic Mathematics, Geometry, linear algebra, vectors and matrices.

Course Objectives:

To develop a logic for graphical modelling of the real life problems.

To suggest appropriate data structure and algorithm for graphical solutions of the problems.

To understand advanced data structures to solve complex problems in various domains.

To operate on the various structured data

To build the logic to use appropriate data structure in logical and computational solutions.


To apply appropriate advanced data structure and efficient algorithms to approach the

problems of various domain.

To design the algorithms to solve the programming problems.

To use effective and efficient data structures in solving various Computer Engineering

domain problems.

To analyze the algorithmic solutions for resource requirements and optimization

Course Contents

Unit I Trees 08 Hours

Tree- basic terminology, General tree and its representation, representation using sequential and

linked organization, Binary tree- properties, converting tree to binary tree, binary tree traversals-

inorder, preorder, post order, level wise -depth first and breadth first, Operations on binary tree.

Binary Search Tree (BST), BST operations, Threaded binary tree- concepts, threading, insertion and

deletion of nodes in in-order threaded binary tree.

Case Study- Use of binary tree in expression tree-evaluation and Huffman's coding

Unit II Graphs 07 Hours

Graphs- Basic Concepts, Storage representation, Adjacency matrix, adjacency list, adjacency multi

list, inverse adjacency list. Traversals-depth first and breadth first, Minimum spanning Tree, Greedy

algorithms for computing minimum spanning tree- Prims and Kruskal Algorithms, Dikjtra's Single

source shortest path, Topological ordering.

Unit III Hashing 08 Hours

Hash Table- Concepts-hash table, hash function, bucket, collision, probe, synonym, overflow, open

hashing, closed hashing, perfect hash function, load density, full table, load factor, rehashing, issues

in hashing, hash functions- properties of good hash function, division, multiplication, extraction,

mid-square, folding and universal, Collision resolution strategies- open addressing and chaining,

Hash table overflow- open addressing and chaining, extendible hashing.

Heap-Basic concepts, realization of heap and operations

Unit IV Search Trees, Indexing and Multiway Trees 07 Hours

Symbol Table-Representation of Symbol Tables- Static tree table and Dynamic tree table, Height

Balanced Tree- AVL tree.

Indexing and Multiway Trees- Indexing, indexing techniques, Types of search tree- Multiway

search tree, B-Tree, B+Tree

Unit V File Organization 08 Hours

Sequential file organization- concept and primitive operations, Direct Access File- Concepts and

Primitive operations, Indexed sequential file organization-concept, types of indices, structure of

index sequential file, Linked Organization- multi list files, coral rings, inverted files and cellular

partitions.

Books:

Text:

1. Horowitz, Sahani, Dinesh Mehata, “Fundamentals of Data Structures in C++”, Galgotia

Publisher, ISBN: 8175152788, 9788175152786.

2. M Folk, B Zoellick, G. Riccardi, “File Structures, Pearson Education”, ISBN:81-7758-37-5.

3. Peter Brass, “Advanced Data Structures‖”, Cambridge University Press, ISBN:978-1-107-

43982-5

Reference: 1. Aho, J. Hopcroft, J. Ulman, ―Data Structures and Algorithms‖, Pearson Education, 1998,

ISBN-0-201-43578-0.

2. Michael J Folk, ―File Structures an Object Oriented Approach with C++‖, Pearson

Education, ISBN: 81-7758-373-5.

3. SartajSahani, ―Data Structures, Algorithms and Applications in C++‖, Second Edition,

University Press, ISBN:81-7371522 X.

4. G A V Pai, ―Data Structures and Algorithms‖, The McGraw-Hill Companies, ISBN -

9780070667266.

Goodrich, Tamassia, Goldwasser, ―Data Structures and Algorithms in Java‖, Wiley

Publication, ISBN: 9788126551903.

Sandip University



Applied Statistical Analysis



Course: Applied Statistical Analysis Course Code: YCA405

Theory: 03Hrs/ Week

Practical: 02 Hrs/Week

Credits: 03


CIA: 100 Marks

Prerequisite:-

Data Structures and algorithms

Basic Mathematics, Geometry, linear algebra, vectors and matrices.

Course Objectives: The course enables students to:

Learn how to analyze statistical data properly.

Understand the role of formal statistical theory and informal data analytic methods.


Gain an understanding of statistical methods relevant to upper division interdisciplinary

courses.

Sharpen students’ statistical intuition and abstract reasoning as well as their reasoning

from numerical data throughcommunity-based and other research.

Course Contents

Unit I Introduction to Statistical Analysis 08 Hours

Introduction, Meaning of Statistics, The Scientific Method, Basic Steps of the Research Process,

Experimental Data and Survey Data, Populations and Samples, Census and Sampling Method,

Parameter and Statistic, Independent and Dependent Variables, Examining Relationships,

Introduction to SPSS Statistics.

Unit II Describing Data 07 Hours

Introduction, Types of Data, Data Transformation, and Summarizing Data: Graphical Methods,

Summarizing Data: Measures of Central Tendency, Summarizing Data: Measures of Dispersion,

Levels of Measurement, Random Variables and Probability Distributions, Discrete and Continuous

Random Variable, Making Inferences about Populations from samples, Estimator and Estimate,

Confidence Interval for Population Mean (Large Sample).

Unit III Testing Hypothesis 08 Hours

Introduction, Null and Alternative Hypothesis, Type I and Type II Error, The Procedure of

Hypothesis Testing, Hypothesis Testing of a Population Mean: Large Sample, Hypothesis Testing of

a Population Mean: Small Sample, Hypothesis Test of a Proportion (One Sample), Hypothesis Test

of Population Variance, Hypothesis Test of Population Mean: Two Independent Samples(),

Hypothesis Test of Population Mean: Dependent Samples (Paired Samples), Hypothesis Test about

Two Population Proportion, Hypothesis Test about Two Population Variances, Analysis of Variance

(ANOVA), Nonparametric Test, Sign Test for Paired Data, Wilcoxon Matched Pairs Signed Ranks

Test (for n>10 pairs), Mann-Whitney U Test, Kruskal-wallis Tests (H Test).

Unit IV Examining Relationships 07 Hours

Introduction, Types of Correlation, Karl Pearson Coefficient Correlation, Spearman’s Rank Order

Correlation, Partial Correlation, Residuals and Plots, Simple Linear Regression, Multiple Regression

Model, Repeated Measures, Non-linear Regression, Polynomial Regression Models, Weighted Least

Squares, Two Stage Least Squares 1, Structural Equation Modeling.

Unit V Advanced Techniques 08 Hours

Identifying Groups: Classification, Profit Analysis, Discriminant Function Analysis, Proportional

Odds Models, Decision Trees, Neural Networks, Cluster Analysis, Factor Analysis,

Multidimensional Scaling.

Advanced Statistical Analysis Lab Exercises

Using the preexistingDrinks.sav data file

Exercise 1 : to create standardized (Z-) scores for several variables

Using the preexistingCensus.sav data file

Exercise 2 : To run Frequencies to explore the distributions of several variables.

Using the preexistingDrinks.sav data file

Exercise 3 : To obtain summary statistics for scale variables


Exercise 4 : To create two and three-way cross tabulations to explore the relationship between

several variables and touse the Chart Builder to visualize the relationship.


Exercise 5 : To run the Independent-Samples T Test, to interpret the output and visualize the

results with an error barchart.

Using the preexisting data file Census.sav.

Exercise 6 : To use One-Way ANOVA with post hoc tests to explore the relationship between

several variables. You willuse the PASW Statistics.

Using the preexisting data file Bank.sav.

Exercise 7 : To visualize the relationship between two scale variables creating scatterplots

and to quantify thisrelationship with the correlation coefficient.

Using the preexisting PASW Statistics data file Census.sav.

Exercise 8 : To run linear regressions and to interpret the output

Using the preexisting data file SPSS_CUST.SAV

Exercise 9 : To use nonparametric tests to explore the relationship between several

Books:

Text:

1. Applied Statistical Analysis (IBM ICE Publication)

Reference:

2. Statistical Data Analysis (Oxford Science Publications) by Glen Cowen

3. Statistical Analysis : an Introduction using R.Wikibooks

4. Multivariate Statistical Analysis A Conceptual Introduction, 2nd edition by Sam

KashKachigan

5. Handbook of Statistical Analysis and Data Mining Application by Robert Nisbet,

John, IV Elder, Gary Miner

Sandip University



Java Programming Laboratory



Course: Java Programming Laboratory Course Code: YCA411

Theory: --

PR: 04 Hours/Week

Credits: 02

Practical : 25 Marks

Term Work : 25 Marks

Guidelines for Assessment

Continuous assessment of laboratory work is done based on overall performance and lab assignments

performance of student. Each lab assignment assessment will assign grade/marks based on

parameters with appropriate weightage. Suggested parameters for overall assessment as well as each

lab assignment assessment include- timely completion, performance, innovation, efficient codes,

punctuality and neatness.

Guidelines for Laboratory Conduction

The instructor is expected to frame the assignments by understanding the prerequisites, technological

aspects, utility and recent trends related to the topic. The assignment framing policy need to address

the average students and inclusive of an element to attract and promote the intelligent students. The

instructor may set multiple sets of assignments and distribute among batches of students. It is

appreciated if the assignments are based on real world problems/applications. Encourage students for

appropriate use of Hungarian notation, Indentation and comments. Use of open source software is

encouraged.

In addition to these, instructor may assign one real life application in the form of a mini-project based

on the concepts learned. Instructor may also set one assignment or mini-project that is suitable to

respective branch beyond the scope of syllabus.

Set of suggested assignment list is provided in three groups. Each student must perform at least 13

assignments as at 05 compulsory from group A, 07 from group B and 01 from group C

Operating System recommended: 64-bit Open source Linux or its derivative.

Programming tools recommended: JDK.

Suggested List of Laboratory Assignments

Group A (Compulsory Assignments)

1. Write a java program for employee class to display basic information.

2. Design a class in java to perform various mathematical operations on given numbers.

3. Write a java program for calculating area of circle.

4. Write a program for implementing single inheritance for student class.

5. Write a program for implementing multilevel inheritance for employee class.

Group B (Any 7)

1. Implement java program to display content of array.

2. Write a java program to find the prime number from 1 to 20.

3. Write a java program to display Fibonacci series of any number.

4. Implement a java program to perform addition of two numbers, accept numbers form user.

5. Write a java program to find.

a. Length of given string.

b. Reverse the string.

c. Palindrome.

6. Implement a java program to count number of vowels from given string.

7. Design an applet program to perform addition of two numbers.

8. Write an exception handling program to handle divide by zero and Array Index OutOfBounds

errors.

9. Implement AWT program to design student admission form.

10. Write a java program to read and write the content of given "example.txt" file.

Group C (Any 1)

1. Design a calculator using AWT.

2. Implement Tic Tac Toe using AWT.

Sandip University



Advanced Data Structures Laboratory



Course: Advanced Data Structures Laboratory Course Code: YCA412

Theory: --

PR: 04 Hours/Week

Credits: 02

Practical : 25 Marks

Term Work : 25 Marks

Guidelines for Assessment

Continuous assessment of laboratory work is done based on overall performance and lab assignments

performance of student. Each lab assignment assessment will assign grade/marks based on

parameters with appropriate weightage. Suggested parameters for overall assessment as well as each

lab assignment assessment include- timely completion, performance, innovation, efficient codes,

punctuality and neatness.

Guidelines for Laboratory Conduction

The instructor is expected to frame the assignments by understanding the prerequisites, technological

aspects, utility and recent trends related to the topic. The assignment framing policy need to address

the average students and inclusive of an element to attract and promote the intelligent students. The

instructor may set multiple sets of assignments and distribute among batches of students. It is

appreciated if the assignments are based on real world problems/applications. Encourage students for

appropriate use of Hungarian notation, Indentation and comments. Use of open source software is

encouraged.

In addition to these, instructor may assign one real life application in the form of a mini-project based

on the concepts learned. Instructor may also set one assignment or mini-project that is suitable to

respective branch beyond the scope of syllabus.

Set of suggested assignment list is provided in three groups. Each student must perform at least 13

assignments as at 05 compulsory from group A, 07 from group B and 01 from group C

Operating System recommended: 64-bit Open source Linux or its derivative.

Programming tools recommended: gcc /g++ compiler

Suggested List of Laboratory Assignments 1. C Program to Construct a Tree & Perform Insertion, Deletion, Display 2. In-order, Pre-order and Post-order Tree Traversal using C Programming

3. C Program to Implement Binary Tree using Linked List

4. C program to implement Depth First Search(DFS) 5. C program to implement breadth First Search(BFS)

6. A C / C++ program for Prim's Minimum Spanning Tree (MST) algorithm. The program is for

adjacency matrix representation of the graph.

7. C++ program for Kruskal's algorithm to find Minimum Spanning Tree of a given connected, undirected and weighted graph

8. A C++ program for Dijkstra's single source shortest path algorithm. The program is for adjacency matrix representation of the graph

9. C Program To Perform Insertion, Deletion and Traversal In B-Tree.

10. C++ Program to Implement B+ Tree

11. Write a C Program to implement hashing. 12. C Program to Implement Hash Tables chaining with Singly Linked Lists.

13. C Program for the Implementation of a Symbol Table with functions to create, insert, modify, search

and display

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