19 / 3 / 2026

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TASK 4: Working with the Command Line on Ubuntu

Introduction

In this task, I learned a few Ubuntu commands such as pwd, mkdir, cd, ls, cat, touch, which are used to

These commands help users navigate directories, create files and folders, manage file systems, and work efficiently using the terminal interface without a graphical environment.

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Commands Used

Command	Purpose
mkdir test	Create a folder named test
cd test	Enter folder
touch blank.txt	Create empty file
ls	List files
mkdir M{0001..2600}	Create 2600 folders
cat file1.txt file2.txt	Concatenate files

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Steps Performed

1. Created a new directory named test using the mkdir command.
2. Navigated into the directory using cd.
3. Created a blank file without using any text editor using the touch command.
4. Listed files using the ls command.
5. Generated 2600 folders using brace expansion in Ubuntu terminal. by using mkdir M{0001..2600} the command

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Screenshot of Execution

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TASK 3: Working with GitHub

Introduction

GitHub is a collaborative platform for version control and project hosting. It is a very great place to work and collaborate on a project or any work. Here I have forked a repository and after correcting the code I have send pull request to the main branch in repository In this task, I learned about the working of GitHub, such as how to create a fork, which essentially means creating a personal copy of a repository, and about pull requests (PR), a feature used to propose changes made in someone else’s repository so that they can review and merge it into the main branch.

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Steps Performed

• Steps Performed (Directly on GitHub)

• Opened the given repository on GitHub.

• Forked the repository to create a personal copy in my GitHub account.

• Opened the required file in the forked repository.

• Edited the code directly on GitHub using the Edit (✏️) option.

• Made the necessary corrections/changes in the file.

• Committed the changes using a commit message.

• Used the Contribute → Open Pull Request option.

• Created a Pull Request (PR) to the original repository.

• Submitted the pull request for review and merge by the repository maintainer.

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GitHub Repository Link

👉 Click here to view my GitHub Task Repository

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TASK 1: 3D Printing

3D Printing Overview

3D printing is an additive manufacturing process in which a three-dimensional object is created layer by layer from a digital model.

The process begins with a 3D design that is saved as an STL file.
This file is then processed in slicing software such as Cura, which converts the model into G-code, a set of instructions that guide the printer.

After setting parameters like:

• Nozzle temperature
• Bed temperature
• Infill density
• Print speed
• bed temperature

the printer melts the filament and deposits it layer by layer to form the final object.

This technology is widely used for prototyping, product design, and manufacturing.

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TASK 12: Soldering Prerequisites

Introduction

Soldering refers to the process of joining metal parts together using a filler material called solder, which has a low melting point. The solder is heated until it liquefies, allowing it to flow over the surfaces and create a connection when it cools and hardens.

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TASK 14: Karnaugh Maps and Deriving the Logic Circuit

Introduction

A Karnaugh map (K-map) is a visual technique used in digital logic design and Boolean algebra to simplify logical expressions and reduce the number of logic gates needed for implementing a digital circuit.

This task focuses on a Burglar Alarm system that identifies unauthorized access when the gate is open while the key remains unpressed.

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TASK 6: The Matrix Puzzle — Decode with NumPy & Reveal the Image

Introduction

Matrix Puzzle – Decoding a Scrambled Image using NumPy

Step 1: Introduction

The goal of this task is to decode a hidden image from a scrambled numerical matrix.
The matrix contains pixel intensity values that represent an image.

We use the following Python libraries:

• NumPy → for numerical computations and matrix manipulation
• Matplotlib → for visualizing the image

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Step 2: Concept of Image as a Matrix

A digital image is represented as a matrix of numbers.

Example of a grayscale image matrix:

255 255 255 255
255 0 0 255
255 0 0 255
255 255 255 255

Where:

• 0 represents black
• 255 represents white
• Values in between represent shades of gray

Therefore:

Image = Matrix of pixel values

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Step 3: Loading the Scrambled Matrix

The scrambled image data is stored in a NumPy file (.npy).

This file contains numerical values representing pixel intensities.

The data is loaded into the program so we can manipulate it.

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Step 4: Checking the Shape of the Data

Every NumPy array has a shape.

Shape represents:

(rows, columns)

Example:

(200, 50)

Meaning:

• 200 rows
• 50 columns

Understanding the shape helps us analyze the structure of the scrambled data.

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Step 5: Flattening the Matrix

Flattening converts a 2D matrix into a 1D array.

Example:

Before flatten:

1 2
3 4

After flatten:

[1 2 3 4]

This helps reorganize the data before reshaping.

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Step 6: Finding the Square Dimension

The puzzle suggests reshaping the data into a square matrix.

If the total number of elements is:

10000

Then:

√10000 = 100

So the correct matrix dimension becomes:

100 × 100

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Step 7: Reshaping the Array

Reshaping changes the structure of the array without changing the data.

Example:

Before reshape:

[1 2 3 4 5 6]

After reshape (2 × 3):

1 2 3
4 5 6

This step reconstructs the correct pixel grid of the image.

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Step 8: Displaying the Image

Once the matrix is correctly structured, it can be visualized.

Matplotlib converts matrix values into pixel intensities and displays the image.

This reveals the hidden picture encoded in the scrambled data.

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Step 9: Workflow Summary

Scrambled Matrix
↓
Load Data using NumPy
↓
Check Shape
↓
Flatten the Matrix
↓
Find Square Dimension
↓
Reshape into Square Matrix
↓
Display Image using Matplotlib

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Step 10: Conclusion

In this task, a scrambled numerical dataset representing pixel intensities was decoded using NumPy matrix operations.
After restructuring the data into a square matrix, the image was visualized using Matplotlib, revealing the hidden image.

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TASK 20: Notebook Ninja – Getting Started with Jupyter

Introduction

Familiarize yourself with Jupyter Notebook as a tool for both coding and communication. This task is designed to build confidence in writing clean, readable, and well-structured notebooks using both code and Markdown.

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Concepts Learned

• Understanding the Jupyter Notebook interface.
• Using code cells to write and execute programs.
• Using Markdown cells for documentation and explanations.
• Combining code and text for better communication.
• Organizing notebooks in a clean and structured way.

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Jupyter Notebook Link

👉 Click here to view the Jupyter Notebook

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TASK 18: Sad Servers - "Like LeetCode for Linux"

Introduction

Sadservers is a platform designed to practice and improve Linux troubleshooting skills. In this task, we explored a murder mystery located in the Clmystery directory and recorded the suspect’s name in the mysolution file.

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Commands Used

• cd [directory_path]
• ls [options] [directory_path]
• cat [options] [file_name]
• grep [options] "pattern" [file_name]

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TASK 15: Active Participation

Introduction

I participated in the event CODEATHON, conducted during the International-Level Annual Technical Symposium, Phase Shift 2024, held at BMSCE on December 5th and 6th, 2024.

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TASK 8: Writing a Resource Article Using Markdown

Markdown is a lightweight markup language used to format text using simple syntax.
It allows users to create structured documents with headings, lists, links, and images easily.
Markdown is widely used in GitHub, documentation, and technical writing.
It helps convert plain text into clean and readable formatted content.
Markdown files are usually saved with the .md extension.

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Resource Link for my article

👉 Click Here

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TASK 5: Linear Regression

Introduction

Linear Regression is a supervised machine learning algorithm used to model the relationship between input features and a continuous output variable. It works by fitting a straight line (best-fit line) through data points to predict values.

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Concepts Learned

• Understanding supervised learning.
• Difference between regression and classification.
• Linear relationship between variables.
• Concept of best-fit line.
• Error function and loss minimization.

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Steps Performed

• Loaded dataset and explored input features.
• Implemented linear regression without using machine learning libraries.
• Calculated slope and intercept for best-fit line.
• Predicted output values.
• Visualized results using graphs.

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Learning Outcome

• Practical understanding of regression models.
• Implemented algorithm using mathematical concepts.
• Visualized prediction results.

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housing price vs feature

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Maths beyond the linear regresion

TASK 10: Speed Control of DC Motor

Introduction

In this experiment, a DC motor was controlled using an Arduino UNO and the L298N H-bridge motor driver. The motor’s speed was varied through PWM signals sent from the Arduino, while the direction was controlled using digital pins.

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Components Used

• Arduino UNO
• potentiometer
• H-bridge motor driver
• L298N which acts as a acts like a power amplifier because Arduino cannot directly power a DC motor because its output pins provide very low current so we use these motor adaptor

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Working Principle

• PWM (Pulse Width Modulation) signals were used to control the speed of the DC motor.(The potentiometer is used to adjust the duty cycle of the PWM signal.)
• Digital pins were used to change the rotation direction.
• The H-Bridge is an important circuit used to control the direction of a DC motor

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TASK 9: Tinkercad Project

This task focuses on detecting unknown objects using an ultrasonic sensor, a servo motor, and an Arduino Uno board.

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Components Used

Ultrasonic Sensor

The ultrasonic sensor has two main parts: a transmitter and a receiver. The transmitter emits sound waves that travel through the air and reflect back when they hit an object. The receiver detects the returning echo signal.

Servo Motor

The servo motor rotates the ultrasonic sensor through an angle of 180°, allowing the system to scan a wider area.

Arduino Uno

The Arduino Uno acts as the main controller of the system and performs the following operations:

• Sends a trigger signal to activate the ultrasonic sensor.
• Calculates the distance based on the time taken for the echo to return.
• Rotates the servo motor step-by-step from 0° to 180°.
• Sends the calculated distance values to the Serial Monitor.

Without the Arduino, the ultrasonic sensor and servo motor cannot function together as a complete system.

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TASK 11: LED Toggle Using ESP32

Introduction

In this task, we used an ESP32 microcontroller to create a standalone web server using the Arduino IDE. The ESP32 connects to a Wi-Fi network and hosts a webpage with LED ON/OFF buttons. Clicking these buttons sends HTTP requests to the ESP32, which controls the LEDs connected to its GPIO pins.

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Connections

• ESP32 GPIO pin connected to LED (through resistor).
• LED cathode connected to GND.
• ESP32 powered via USB.
• Wi-Fi connection configured inside Arduino IDE.

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Working Principle

• ESP32 connects to Wi-Fi network.
• Hosts a local web server.
• Webpage contains ON/OFF buttons.
• Button click sends HTTP request.
• ESP32 reads request and toggles LED state.

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TASK 7: Create a Portfolio Webpage

Introduction

A portfolio is a collection of your work, skills, and achievements that showcases your abilities to potential employers, clients, or collaborators. In this task, I created my personal portfolio using HTML and CSS.

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Features of the Portfolio

• About Me section containing personal introduction.
• Education details.
• Hobbies and interests.
• Skills section highlighting technical abilities.
• Social media links.
• Responsive layout using HTML and CSS styling.

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Portfolio Link

👉 Click here to view my portfolio code

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