This 2025 Brochure of the Faculty of Engineering and Technology at Jashore University of Science and Technology (JUST) offers a dynamic glimpse into the vibrant academic, research, and practical learning environment the faculty provides. The cover showcases a collage of real-world engagement—featuring students and faculty immersed in laboratory work, hands-on technical training, collaborative research, and safety education.

Designed with bold geometric elements and a modern color scheme, the brochure reflects the innovation-driven ethos of the faculty. The visuals portray the faculty’s commitment to excellence in engineering education across multiple disciplines, while the inclusion of advanced lab equipment and group activities emphasizes experiential learning. This brochure serves as a gateway for prospective students, partners, and stakeholders to explore the diverse academic programs and forward-thinking initiatives that define JUST's Faculty of Engineering and Technology.

 Idea and Designed by Mursalin Biswas Emon.

The logo of  International Conference on Emerging Technology and Sustainable Development (ICETSD) represents a unified vision of seven distinct departments, each highlighted through unique icons and vibrant color segments. These departments symbolize key areas of innovation, including computing, electrical engineering, renewable energy, chemical sciences, biomedical technology, civil engineering, and materials science. The green skyline above the circle reflects the overarching theme of sustainability. Held at JUST (Jashore University of Science and Technology), this logo encapsulates the multidisciplinary and environmentally conscious spirit of the conference.

This logo is designed by Mursalin Biswas Emon

Department of Electrical and Electronic Engineering.

Jashore University of Science and Technology

 Introduction: 

 A line-following robot is an autonomous robot designed to follow a predefined path, typically marked by a black line on a white surface. It utilizes IR sensors to detect the path and makes real-time decisions to adjust its movement accordingly. This project implements a line-following robot using an Arduino Nano, motor driver, IR sensors, and a combination of buck and boost converters for stable power supply. [1] The line-following robot operates using an array of IR sensors that detect the black line on a white surface. These sensors provide input to the Arduino Nano, which processes the signals and determines the required movement. Based on the sensor readings: 

 • If only one sensor detects the line, the robot moves forward. 

 • If multiple sensors detect the line on one side, the robot turns in that direction.

 • If no sensor detects the line, the robot stops. 

 The motor driver (L298N) controls the DC motors based on Arduino's commands, while the buck and boost converters ensure stable voltage levels for different components. [3] Robotics technology is increasingly in demand across various human endeavors, particularly in the manufacturing, healthcare, service, defense, and consumer sectors. LFR can be utilized for a variety of industrial tasks. It can be used to transport hazardous and large goods. Transporting radioactive materials inside a factory poses a serious risk to human life. In that section, a line follower robot can be useful. It may also monitor patients and alert doctors in emergency scenarios in a hospital. This type of robot can be useful in many areas of the restaurant industry as well, such as food servers and order takers etc.

Apparatus:

Circuit diagram:

View angle:

Flow chart:

 The line-following robot (LFR) is designed to follow black lines on a lighter surface. The critical value for detecting black lines is when IR sensor readings fall below 512. In the code, the condition `s[i] = (s[i] > threshold) ? 0 : 1` assigns `1` to readings ≤ 512 (line detected) and `0` to readings > 512 (no line). The PID system utilizes this detection to adjust motor speeds, maintaining alignment with the line. The threshold of 512 is effective because black lines reflect less light, resulting in sensor readings ≤ 512, while the lighter surface produces readings > 512. This aligns with the LFR’s configuration for a darker line. The `readSensors()` function computes the line’s position via a weighted sum, feeding the error into the PID controller (`kp = 0.2`, `kd = 20`) to steer the robot. It turns left for a negative error (line left) and right for a positive error (line right).Variations in line reflectivity or lighting could require threshold adjustments. Currently, 512 ensures consistent line detection under the tested conditions. 

 1. Introduction

This project focuses on developing and integrating smart systems that leverage IoT, sensors, and Machine learning in urban infrastructure management which is a critical challenge in modern cities , requiring efficient monitoring , real time data analysis and automated decision maki. Recognizing the limitations of traditional, manual approaches in areas like traffic management and agriculture, this project emphasizes the critical role of IoT standards. By adhering to these standards, we ensure seamless interoperability between diverse devices and systems, facilitating scalability, enhancing reliability, and bolstering security within the evolving smart city ecosystem. This foundation of standardized technology enables the creation of robust and sustainable solutions that effectively address real-world challenges and contribute to a more efficient and interconnected urban environment. By integrating IoT, and advanced analytics, it optimizes infrastructure, services, and resource management. Smart cities aim to improve efficiency, sustainability, and quality of life, addressing challenges like traffic congestion, energy consumption, and public safety.

2. Objectives

• Optimize traffic flow using AI-driven smart traffic management.
• Enhance agricultural efficiency through automated, resource-efficient irrigation systems.
• Ensure industrial safety by detecting real-time faults in machinery.
•  Promote the use of sustainable energy via wireless EV charging technology.
•  Enable prompt emergency response through an accident detection and notification system.
• Establish a cohesive smart city framework to integrate these solutions into a sustainable, connected ecosystem.