CS25C11 Operating Systems – Semester III – CSE / AI&DS / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSD – R-2025

Subject Code & Name: CS25C11 – Operating Systems

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML)) / B.E. Computer Science and Design (CSD)

Credits / L-T-P: 4 Credits | L-T-P: 3-0-2

Course Objectives

• This course presents core principles of modern operating systems and analyses components such as process management, CPU scheduling, synchronization, memory, file systems, and I/O.

Full Unit-wise Syllabus

Unit I – Basics Of Operating Systems

History and Evolution of Operating Systems – Types of Operating Systems – Operating Systems structures – Functions of Operating Systems – challenges of Operating Systems – Unix & Linux Operating Systems.

Practical: Installation of a Linux OS; Linux commands and shell scripting

Unit II – System Calls and Process Management

System Calls – Process Concepts and Lifecycle, Process Control Block (PCB) – Context Switching – Threading Models, Process vs Thread – Process Creation and Termination – Case study.

Practical: Process Creation using fork(), exec(), wait(); Multi-threading using POSIX threads

Unit III – CPU Scheduling

Scheduling Criteria and Objectives – Scheduling Algorithms – Preemptive and non-preemptive scheduling.

Practical: Implementation and generation of Gantt chart: FCFS and SJF; Round Robin, Priority and Multilevel Queue

Unit IV – Synchronization and Deadlocks

Concurrency Issues and Critical Section Problem – Synchronization Tools: Mutex, Semaphores – Deadlock: Necessary Conditions, Prevention, Avoidance, Detection, and Recovery.

Practical: Producer-Consumer and Readers-Writers problem; Dead Lock

Unit V – Memory Management

Memory Allocation: Contiguous, Paging, Segmentation – Virtual Memory, Demand Paging, Page Replacement Algorithms (FIFO, LRU, Optimal).

Practical: Page replacement algorithms; Dynamic memory allocation and deallocation

Unit VI – File Systems and I/O Management

File System Concepts – Directory and File Access Control – I/O Management – Disk Scheduling Buffering, Caching – Case study.

Practical: File operations and file buffering; Disk scheduling algorithms

Course Outcomes (COs)

• CO1: Describe the operating systems concepts in managing computer hardware and software resources.
• CO2: Analyze operating system components and file systems.
• CO3: Evaluate operating system algorithms in different computing environments.
• CO4: Design operating system solutions for managing system resources in real-world scenarios.
• CO5: Develop the ability to adapt to emerging trends, tools, and technologies.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

CS25C10 Object Oriented Software Engineering – Semester III – CSE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) – R-2025

Subject Code & Name: CS25C10 – Object Oriented Software Engineering

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML))

Credits / L-T-P: 3 Credits | L-T-P: 3-0-0

Course Objectives

• This course illustrates software engineering lifecycle models and discusses object-oriented software development process from requirements analysis to implementation with the perception of testing and maintenance.

Full Unit-wise Syllabus

Unit I – Software Process and Agile Development

Software Engineering – Software Process – Perspective and Specialized Process Models – Agile process – Extreme Programming (XP) Process.

Activities: Assignment on Software Process Models with suitable application contexts; Review of GATE Questions.

Unit II – Requirements Analysis and Specification

Software Requirement Specification (SRS) – Formal system specification – Finite State Machines – Petri nets – Object modeling using UML – Use case model – Class diagrams – Interaction diagrams – Activity diagrams – State chart diagrams – Functional modeling: Data Flow Diagram (DFD) – CASE Tools.

Activities: Project based experiential learning: Preparation of SRS using JRA Tool for any specific applications like Library Management System and so on; Object and functional modeling for any specific applications like Online Food Ordering and so on.

Unit III – Software Design

Software design – objectives and metrics – modularity – types of coupling and cohesion – Design strategies – Bottom up design and top down design – hybrid design – functional oriented design – object oriented design.

Activities: Quiz: Multiple choice or true/false questions on design principles, metrics, modularity, and strategies; Flipped Class Room: Analysis of code snippets for modularity, coupling and cohesion. Refactor to improve design.

Unit IV – Design Pattern

Model-View-Controller (MVC), Publish-Subscribe, Adapter, Command, Strategy, Observer, Proxy, Facade. Architectural styles: Layered, Client-Server, Tiered, Pipe and Filter. User Interface Design. Case Study.

Activities: Assignment: Identification of architectural style or pattern used in software descriptions; Review of GATE Questions.

Unit V – Software Testing and Maintenance

Testing techniques: Unit testing, Black box testing, White box testing, Integration and System testing, Regression testing. Debugging and Program analysis. Symbolic execution and Model Checking. Case Study.

Activities: Assignment: Creation of Test Plan for a specific application like Railway Reservation System and so on; Review of GATE Questions.

Unit VI – Project Management

Software Project Management and Software Configuration Management – Project Scheduling – DevOps: Cloud as a platform – Operations – Deployment Pipeline – Overall Architecture, Building and Testing, Deployment. Tools. Case Study.

Activities: Quiz: Project Scheduling Techniques – Gantt Charts – PERT/CPM; Project based experiential learning: Preparation of mile stone task break down and time line for a specific software applications like Chat App and so on.

Course Outcomes (COs)

• CO1: Describe the practices of object-oriented software engineering in developing reliable software systems.
• CO2: Analyze software development processes to understand system structure and behavior.
• CO3: Evaluate software engineering practices to assess the effectiveness in different project scenarios.
• CO4: Design appropriate object-oriented modeling techniques for real-world applications.
• CO5: Develop the ability to independently learn emerging object-oriented tools, frameworks, and software engineering practices.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Activities 30% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Examinations 70% (TWO tests)

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

CS25C09 Java Programming – Semester III – CSE / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSD – R-2025

Subject Code & Name: CS25C09 – Java Programming

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.Tech. Computer Science and Business Systems (CSBS) / B.E. Computer and Communication Engineering (CCE) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML)) / B.E. Computer Science and Design (CSD)

Credits / L-T-P: 5 Credits | L-T-P: 3-0-4

Course Objectives

• The objective of this course is to impart knowledge on the fundamentals of Java programming and explore conceptual capabilities of Java Language.

Full Unit-wise Syllabus

Unit I – Foundations of OOP and Java

Principles of OOP – Java Development Kit (JDK) Data Types, Variables and Arrays – Operators – Control Statements – Classes & Objects Constructors – Method Overriding – Access specifiers – Static Members – Inheritance.

Practical: Constructors and Static Members; Inheritance

Unit II – Java Conceptual Foundations and Capabilities

Packages and Interfaces – Exception Handling – Multithreaded Programming Enumerations, Autoboxing, and Annotations Generics – Lambda Expressions.

Practical: Packages and Interfaces with Access specifiers; Creation of user defined exception and Multiple threads

Unit III – Java Core Libraries

String Handling – java.lang – Utility Classes – java.io – Event Handling – Working with AWT Controls – Layout Managers.

Practical: String Handling; Event Handling

Unit IV – I/O and GUI Programming in Java

Reading and Writing Console I/O – Files – Swing – Menus.

Practical: Files I/O; Swing Application

Unit V – Database Connectivity Using Java

JDBC – Types of JDBC Drivers – JDBC Packages JDBC API and Driver – Associating the JDBC/ODBC bridge with the Database – Database Connections and Operations.

Practical: Database Connections and Operations

Unit VI – Java Collection Framework

Collection framework – Regex – java.util package – arrays and collections – Map interfaces – iterators – generics.

Practical: Array List and Map Interfaces

Course Outcomes (COs)

• CO1: Describe the object-oriented features of Java programming for developing robust applications.
• CO2: Analyze object-oriented constructs to understand program flow, data handling, and exception management.
• CO3: Evaluate appropriate Java programming constructs in different scenarios.
• CO4: Design Java applications to solve real-world problems.
• CO5: Develop the ability to adapt to emerging Java frameworks and tools.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

CS25C08 Data Structures – Semester III – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) / CSD – R-2025

Subject Code & Name: CS25C08 – Data Structures

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Information Technology (IT) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.Tech. Computer Science and Business Systems (CSBS) / B.E. Computer and Communication Engineering (CCE) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML)) / B.E. Computer Science and Design (CSD)

Credits / L-T-P: 5 Credits | L-T-P: 3-0-4

Course Objectives

• This course presents various data structures and their importance to provide a comprehensive view about problem solving skills.

Full Unit-wise Syllabus

Unit I – Linear Data Structures

Abstract Data Types – Internal Representation of Primitive Data Structures – One Dimensional and Multi-Dimensional Arrays – linear lists – Singly, doubly, Circular linked lists – Applications.

Practical: Single and Multidimensional arrays; Singly, Doubly and Circular Linked Lists

Unit II – Stacks and Queues

Stack: Representations – Operations – Implementations – Applications. Queue: Representations – Operations – Implementations – Types – Applications.

Practical: String reverse operations and Expression evaluation; Circular Queue and Priority Queue

Unit III – Trees

Representations – Types – Binary Search Trees (BSTs) – AVL Tree – Operations: Search, Traversals, Rotations – Balanced BSTs – Splay trees – B-trees – Binary Heaps.

Practical: Traversal operation; AVL Tree rotations; Query and Update operations on Balanced BSTs

Unit IV – Sorting, Searching & Hashing Techniques

Linear and Binary Search – Bubble Sort – Insertion Sort – Merge Sort – Bucket Sort – Quick Sort – Heap sort – Hashing techniques – Dictionaries – Hash function – Collision – Separate chaining – open addressing.

Practical: Quick and Heap Sort; Binary Search and Hashing

Unit V – Graphs

Representation – Types – Operations – Prim's, Kruskal algorithms – Dijikstra's algorithm – Connected and Biconnected Components.

Practical: BFS and DFS algorithms; Minimum Spanning Tree and shortest path algorithms

Course Outcomes (COs)

• CO1: Describe the concepts and operations of data structures for efficient data organization and manipulation.
• CO2: Analyze data structures to understand their performance and application suitability.
• CO3: Evaluate data structure algorithms in terms of time and space complexity for solving computational problems.
• CO4: Design appropriate data structures and algorithms for real-world problem scenarios.
• CO5: Develop the ability to apply emerging data structures through continuous self-learning and practice.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Activities 10% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Theory Examinations 30%, Internal Laboratory Examination 10%

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

MA25C14 Discrete Mathematics – Semester III – CSE / IT / AI&DS / CSBS / CCE / CSE(DS) / CSE(IoT) / CSE(Cyber) / CSE(AI&ML) – R-2025

Subject Code & Name: MA25C14 – Discrete Mathematics

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Information Technology (IT) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.Tech. Computer Science and Business Systems (CSBS) / B.E. Computer and Communication Engineering (CCE) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML))

Credits / L-T-P: 4 Credits | L-T-P: 3-1-0

Course Objectives

• To introduce foundational concepts of set theory, relations, functions, and recurrence relations relevant to modeling data and algorithmic structures.
• To explain principles of propositional and predicate logic, Boolean algebra, and lattice theory for reasoning and decision-making in AI systems.
• To present core ideas of graph theory and its applications to analyze and optimize networks, data structures, and intelligent systems.

Full Unit-wise Syllabus

Unit I – Set Theory, Relations and Functions

Set theory – inductive definition of sets and proof by induction – Peano postulates – Relations – equivalence relations and partitions. Functions – Type of functions: Injective, surjective and bijective functions – Composition of functions – Inverse functions – Permutation functions – Recurrence relations – Solving linear recurrence relations.

Activities: Create and present Venn diagrams to illustrate union, intersection. Categorize real-world mappings such as student-to-email ID, username-to-password, as injective, surjective, or bijective using role-play or sorting tasks.

Unit II – Logic

Propositions – Logical operators – Normal forms – Rules of inference – Consistency and inconsistency – Propositional logic – Proofs – Predicates – Quantifiers – Universe of discourse – Logical equivalences and implications for quantified statements – Rules of specification and generalization – Validity of arguments.

Activities: Conduct a truth table building competition for compound propositions using logical operators.

Unit III – Boolean Algebra and Lattice Theory

Partial ordering – Posets – Lattices as Posets – Properties of lattices – Lattices as algebraic systems – Sub lattices – Direct product and homomorphism – Some special lattices – Boolean algebra – Sub Boolean Algebra – Boolean Homomorphism.

Activities: Draw Hasse diagrams for lattices from a given set and identify sublattices and lattice operations.

Unit IV – Graph Theory

Graphs – Types of graphs – Matrix representation of graphs – Graph isomorphism – Walk – Path – Cycles – Eulerian graphs – Hamiltonian graphs – Planar graphs – Euler formula – Shortest path algorithm: Dijkstra's algorithm.

Activities: Implement Dijkstra's algorithm to find the shortest path in a weighted AI decision graph.

Course Outcomes (COs)

• CO1: Understand the concepts of sets, Functions, Recurrence Relations, Logic, Boolean algebra and graph theory.
• CO2: Apply principles of logic, functions, Boolean algebra, lattices, recurrence relations, and graph theory to solve real world engineering problems.
• CO3: Employ discrete mathematical structures such as relations, recurrence relations, logical statements, lattices and graphs to analyze engineering problems.
• CO4: Model engineering problems and provide discrete mathematics based solutions.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (20%), Solution to application-oriented problems using software (20%), Solving Competitive Examination questions (20%), Internal Examinations (40%)

Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

BS25206 Studio II - Foundation Design Studio – Semester II – BDes – R-2025

Subject Code & Name: BS25206 – Studio II - Foundation Design Studio

Regulation: R-2025

Semester: II (Second Semester)

Branch: B. Des. (BDes)

Credits / L-T-P: 5 Credits | L-T-P: 0-0-10

Course Objectives

• To foster creativity in generating innovative product ideas for daily use, focusing on personal products, homeware, gifts and stationery.
• To equip students with essential skills in sketching, prototyping, and using design software.
• To teach students how to conduct user research and incorporate feedback into their designs to meet real-world needs.
• To instill knowledge about sustainable design practices and material selection for everyday products through model making.
• To engage in discussion and analytical thinking through seminars/workshops.
• To develop effective presentation skills to communicate design concepts clearly to diverse audiences.

Full Unit-wise Syllabus

Prototype / Project Typology

Design of simple products – daily use, commonly used, stationaries, simple furniture like bookrack with prototype models.

Areas of Focus / Concern

Aesthetic and useability in terms of scale, colour, texture, etc. Function and need: user requirements, anthropometrics, and ergonomics. Detailing of the product image and symbolism.

Course Outcomes (COs)

• CO1: Ability to recognise product design concepts, aesthetics, and usability.
• CO2: Knowledge of sketching, prototyping, and user research methods.
• CO3: Awareness of sustainable practices, materials, and presentation techniques.
• CO4: Develop the ability to generate creative product ideas for everyday use.
• CO5: Build the ability to apply user feedback and research in product development.
• CO6: Develop confidence in presenting and communicating design concepts effectively.

Assessment Pattern (Quick Note)

• Weightage: Continuous Internal Assessment 50% | End Semester Examinations 50%
• Internal methodology: Three Assessments with equal weightage (approx. 33.33% each). Each assessment shall incorporate continuous marking of the work and performance during the particular assessment period.

Source: Official Anna University – B. Des. R-2025 Syllabus
Last Updated: September 2026

ANNA UNIVERSITY | SYLLABUS | UNIVERSITY QUESTION PAPER | NOTES

BS25205 Computer Modelling and Simulation Techniques – I – Semester II – BDes – R-2025

Subject Code & Name: BS25205 – Computer Modelling and Simulation Techniques – I

Regulation: R-2025

Semester: II (Second Semester)

Branch: B. Des. (BDes)

Credits / L-T-P: 3 Credits | L-T-P: 1-0-4

Course Objectives

• To introduce computer operation principles and explore image editing through software.
• To impart training in computer-aided 2D drafting and 3D modelling through projects.
• To enable the use of computer applications to develop a design from the initial stages to the outcome.
• To enable the rendering of a building to create a photo-realistic image.

Full Unit-wise Syllabus

Unit I – The Basics of Modelling

Introduction – Overview of CAD applications, Interface and basic drawing tools, Advanced Techniques, Project Work, Geometric Patterns, Stained Glass Window Design, Abstract Shapes, Mosaic Design, and Perspective Drawing.

Unit II Introduction to 3D Modelling

Interface overview and navigation, Basic drawing tools. Components and Materials, Managing components and group. Applying textures and materials for efficient 3D modeling. Using layers and scenes, importing models from 3D Warehouse and using plugins.

Unit III – Rendering

Rendering and scene setting to create a photo-realistic picture, understanding material mapping, Environment setting and image-filling. Understanding bitmap images and vector graphics, image size and resolution. Basic tools for editing and creating graphics.

Unit IV – Design and Detailing Using Software

Design and detailing exercise using software. Exercises – Illustrative Map – Sculptural Forms, Experiment with curves and shapes to make unique 3D forms. Interior Space Design, Model the interior paying attention to layout, furniture, and lighting. Use textures to enhance the atmosphere. Custom Furniture Design – Create a unique piece of furniture that incorporates both function and aesthetics. Consider ergonomic design principles. Visual Storytelling, Design a scene that tells a story. Art Installation Design, Create a conceptual design for an art installation.

Course Outcomes (COs)

• CO1: Basic understanding of, Ability to recognise CAD interfaces, tools, and modelling techniques.
• CO2: Knowledge of rendering methods, materials, and graphic editing concepts.
• CO3: Awareness of design applications from concept to final output using software.
• CO4: Develop the ability to create 2D drawings, 3D models, and rendered visualisations using computer software.

Assessment Pattern (Quick Note)

• Weightage: Continuous Internal Assessment 50% | End Semester Examinations 50%
• Internal methodology: Two Assessments with equal weightage – Internal written Test/Examination (50%), Assignment (50%) of any mode such as study, seminar, and or a combination of modes, etc.

Source: Official Anna University – B. Des. R-2025 Syllabus
Last Updated: September 2026