Study Material

  🚀 DSA with C – Day 1: Introduction & Setup 🎯 Goal: Understand what DSA is. Set up your C environment. Write your first C program. Learn about time & space complexity (theory). Practice basic input/output and loops. 🧠 Theory: What is DSA? Data Structures = Ways to organize and store data efficiently. Algorithms = Step-by-step instructions to solve problems. Why DSA matters: Faster apps, better problem-solving, cracking tech interviews. 🔧 Setup for C Programming Install a C compiler: Windows : Use Code::Blocks or install MinGW and use VS Code. Mac/Linux : Already comes with gcc . Use VS Code or terminal. Create your first .c file. 👨‍💻 Hello World Program # include <stdio.h> int main () { printf ( "Hello, World!\n" ); return 0 ; } Compile using: gcc hello.c -o hello ./hello 📚 Time & Space Complexity (Intro) Big O Notation (O(n), O(1), etc.) Example: Loop runs n times → ...

✅ Day 2: Arrays – Traversals, Operations & Basic Problems 🎯 Goals of the Day Understand 1D array declaration and initialization in Java Learn array traversal techniques Solve beginner-level problems on arrays 📘 1. Arrays in Java – The Basics An array is a collection of elements of the same type stored in contiguous memory. 🔹 Declaration: int [] arr = new int [ 5 ]; // creates an array of size 5 int [] nums = { 1 , 2 , 3 , 4 , 5 }; // directly initialized 🔹 Access: System.out.println(arr[ 0 ]); // prints the first element arr[ 2 ] = 10 ; // sets the third element to 10 🔁 2. Traversal of Arrays 🔹 Using For Loop: for ( int i = 0 ; i < arr.length; i++) { System.out.println(arr[i]); } 🔹 Using Enhanced For Loop: for ( int num : arr) { System.out.println(num); } ✍️ 3. Input in Arrays Scanner sc = new Scanner (System.in); int n = sc.nextInt(); int [] arr = new int [n]; for ( int i ...

  🎯 Goals of the Day Understand why Java is a great choice for DSA Set up your development environment Learn Java basics required for DSA Write your first Java program 📘 1. Why Choose Java for DSA? Object-Oriented : Easier to model real-world problems Rich Standard Library : Collections like ArrayList , HashMap , etc. Strong Typing & Readability : Prevents bugs and improves clarity Platform Independent : Write once, run anywhere (thanks to JVM) Used in Competitions & Interviews 🧰 2. Java Setup Install: JDK (Java Development Kit) – Download JDK 17+ IDE (Optional) : VS Code (with Java Extension Pack) IntelliJ IDEA (recommended) Eclipse Check Installation: bash java -version javac -version 🧠 3. Java Basics You Need for DSA Focus only on these essentials first: 🔹 Data Types & Variables: int , long , float , double , boolean , char , String 🔹 Conditionals: if , else if , else , switch 🔹 Loops: for , while , do - ...

 What is information security? Information security means protecting information and information systems from unauthorized access, use, disclosure, disruption, modification or destruction by ensuring the following security objectives:   Confidentiality  Makes sure that data remains private and confidential. It should not be viewed by unauthorized people through any means Information disclosure is a cyber-attack that reads all emails sent to/by the victim by eavesdropping into the communication network; hence, compromising confidentiality Integrity  Assures that data is protected from accidental or any deliberate modification Tampering is a cyber-attack where attacker modifies an incoming email before it reaches the intended recipient. Receiver would not know that the received message was modified; hence, compromising integrity  Availability Ensures timely and reliable access to information and its use Denial of service is a cyber-attack where the website becomes...

  UNIT 1: Image Representation and Modeling Q1: Explain the concept of digital image representation in detail. Answer : A digital image is a two-dimensional function that represents a physical object or scene. It is essentially a matrix where each element (pixel) contains intensity or color information. The size of the image is defined by its resolution (width × height), and each pixel has an intensity or color value. Pixel : The smallest unit of a digital image, typically represented as a square or rectangular cell. Each pixel has a value corresponding to its color or intensity. Resolution : Refers to the number of pixels in the image, which defines the level of detail. Higher resolution means more pixels and finer details. Color Models : Digital images can be grayscale (single intensity) or color (combining three channels for Red, Green, and Blue). Examples include RGB, CMYK, and YCbCr. Digital images are obtained by sampling and quantizing a continuous signal. S...

  Introduction to Data Compression Data Compression is the process of encoding information using fewer bits. It aims to reduce the size of the data while maintaining the necessary quality or information . Applications : Image, video, and audio compression (JPEG, MP3, video codecs). Goal : Reduce storage space and speed up transmission without losing essential information. 2. Data Compression vs Bandwidth Bandwidth refers to the data transmission capacity of a communication system (how much data can be transmitted per unit of time). Data Compression is a technique to reduce the size of data, leading to reduced transmission time, which increases effective bandwidth . Relation : Compressed data requires less bandwidth for transmission. Compression reduces storage and transmission costs , improving efficiency. Example: A 1MB image compressed to 100KB requires less bandwidth for transmission and storage. 3. Pixel Coding Pixel Coding involves ...

  Introduction to Image Restoration Image Restoration is the process of recovering an original image that has been degraded by known or unknown factors (such as blur, noise, or motion). It focuses on model-based correction , not just enhancing the image visually. Goal : Retrieve the most accurate version of the original image. 2. Image Formation Models The mathematical relationship between the original image, the degradation, and the observed image. General Model: g ( x , y ) = h ( x , y ) ∗ f ( x , y ) + η ( x , y ) g(x, y) = h(x, y) * f(x, y) + \eta(x, y) Where: g ( x , y ) g(x,y) g ( x , y ) = degraded image h ( x , y ) h(x,y) h ( x , y ) = degradation function (e.g., blur) f ( x , y ) f(x,y) f ( x , y ) = original image η ( x , y ) \eta(x,y) η ( x , y ) = noise ∗ * ∗ = convolution operation 3. Noise Models Describes how random variations corrupt an image. Common Noise Types: Gaussian Noise : Random values from a normal distribut...