Design And Implementation Of An IoT-Based Patient Health Monitoring And Emergency Notification System Using LPWAN Technology
In this project, we present an Internet of Things (IoT) based patient health monitoring system leveraging LoRa (Long Range) technology. The proposed system aims to provide a reliable, low-power, and long-range solution for continuous health monitoring, especially in remote or resource-limited areas. Key health parameters such as heart rate, blood pressure, and body temperature are collected using various sensors and transmitted to a centralized server via LoRa communication. The server processes and analyzes the data, providing real-time monitoring and alert mechanisms for healthcare providers and caregivers. This system enhances patient care by enabling timely medical intervention and reducing hospital visits, thereby optimizing healthcare resources. Our results demonstrate the efficacy of using LoRa for health monitoring, highlighting its potential for widespread application in the healthcare industry
License Plate Recognition using OpenCV
This project explores the implementation of a license plate recognition (LPR) system using OpenCV, an open-source computer vision and machine learning software library. The proposed system is designed to automatically detect and recognize vehicle license plates from real-time video streams or static images. The process involves several key stages: image acquisition, pre-processing, license plate detection, character segmentation, and optical character recognition (OCR). Utilizing OpenCV’s robust image processing and deep learning capabilities, we enhance the accuracy and efficiency of each stage. The system is evaluated on a diverse dataset, demonstrating high accuracy and reliability in various lighting conditions and complex backgrounds. This LPR system has significant potential for applications in traffic management, automated toll collection, parking lot management, and law enforcement. The results indicate that the OpenCV-based approach is a cost-effective and scalable solution for real-time license plate recognition.
Design Custom Boot Loader for STM32 MCU
I have developed custom bootloader for STM32F4, STM32F1 Cortex M4 Microcontroller.
Skin Cancer Detection using Deep Learning
This project presents a deep learning-based approach for the detection of skin cancer, aiming to improve early diagnosis and treatment outcomes. Leveraging convolutional neural networks (CNNs), the system is trained on a large dataset of dermoscopic images to identify various types of skin lesions, including benign and malignant cases. The proposed model employs advanced image preprocessing techniques and data augmentation to enhance its robustness and accuracy. Through rigorous training and validation, the system achieves high precision and recall rates, demonstrating its effectiveness in distinguishing between different skin cancer types. This approach not only accelerates the diagnostic process but also reduces the dependency on expert dermatologists, making skin cancer screening more accessible and cost-effective. The results highlight the potential of deep learning in revolutionizing dermatology and improving patient care through early and accurate skin cancer detection.
Face Recognition with Real-Time Database
This project investigates a face recognition system integrated with a real-time database, designed to provide efficient and accurate identification and authentication. Utilizing advanced deep learning algorithms, the system captures facial images and processes them using convolutional neural networks (CNNs) for feature extraction and recognition. The real-time database ensures immediate updating and retrieval of facial data, facilitating dynamic and scalable management of user profiles. The integration of real-time database capabilities allows for seamless synchronization across multiple devices and locations, enhancing the system’s applicability in various domains such as security, access control, and attendance tracking. Performance evaluation on a diverse dataset indicates high accuracy and swift processing speeds, demonstrating the system’s potential for practical deployment. This face recognition system represents a significant advancement in biometric technology, offering robust, real-time identification solutions for modern security challenges.
Design and Developed an IoT based Gateway Device for Industry
This project presents the design and development of an IoT-based gateway device tailored for industrial applications. The gateway serves as a critical hub, connecting various industrial sensors and actuators to the internet, enabling seamless data collection, monitoring, and control. Key features of the device include robust connectivity options (Wi-Fi, Ethernet, and cellular), support for multiple industrial communication protocols (such as Modbus, CAN, and OPC-UA), and edge computing capabilities for real-time data processing and analytics. The gateway device ensures secure data transmission through advanced encryption methods and provides a user-friendly interface for configuration and management. The proposed solution enhances operational efficiency, predictive maintenance, and remote monitoring in industrial environments, significantly reducing downtime and operational costs. Performance evaluations demonstrate the gateway’s reliability, scalability, and adaptability, highlighting its potential to drive the next wave of industrial automation and IoT integration.
Emergency Beacon System (Low Power Remote Zone Tracking Device)
This project introduces the development of an Emergency Beacon System tailored for remote zone tracking, specifically designed to address the challenges faced by the Bangladesh Army during patrolling in areas where cellular networks are unavailable. Operating in low-power environments, such as hill areas with limited infrastructure, the system provides a robust solution for tracking and locating personnel in emergency situations.
The Emergency Beacon System utilizes satellite communication technology to establish reliable and resilient communication links in areas with poor or no cellular coverage. Each beacon device is equipped with GPS modules for accurate positioning and distress buttons for emergency alerts. The system enables real-time tracking of personnel movements and facilitates immediate response to distress signals.
Key features of the system include low-power consumption, ruggedized design for durability in harsh environments, and encrypted communication protocols to ensure data security. The development process involves rigorous testing and optimization to meet the specific requirements and challenges of remote zone tracking operations.
Field trials and evaluations conducted in collaboration with the Bangladesh Army demonstrate the system’s effectiveness in enhancing patrolling and emergency response capabilities in remote and challenging terrain. The Emergency Beacon System represents a critical advancement in military technology, offering a reliable and resilient solution for personnel tracking and safety in areas with limited connectivity.
Developed a Color Detection System using Computer Vision
This project introduces the development of a color detection system using computer vision techniques, aimed at enabling accurate and efficient color recognition in various applications. Leveraging the capabilities of computer vision algorithms and image processing techniques, the system is designed to detect and identify colors present in digital images or video streams in real-time.
The proposed system employs color segmentation algorithms to isolate regions of interest corresponding to specific colors within the input image. Features such as hue, saturation, and intensity are extracted from these regions to characterize and classify the detected colors. Machine learning models may also be incorporated to improve the accuracy and robustness of color recognition.
The implementation of the color detection system involves the selection and optimization of appropriate computer vision algorithms, as well as the integration of software and hardware components for real-time processing. Performance evaluations demonstrate the system’s effectiveness in accurately identifying colors across a wide range of environmental conditions and input sources.
The developed color detection system has diverse applications across various domains, including industrial automation, robotics, image editing, and quality control. Its ability to provide rapid and reliable color recognition contributes to enhanced efficiency, automation, and decision-making processes in these domains. This research represents a significant advancement in computer vision technology, offering practical solutions for color detection and analysis in real-world scenarios.
Developed Humanoid Robot Sultana
This project presents the development of Sultana, a humanoid robot designed to provide assistance to healthcare professionals during the COVID-19 pandemic. With the unprecedented challenges posed by the pandemic, there has been an urgent need for innovative solutions to support frontline workers and mitigate the spread of the virus. Sultana is equipped with advanced artificial intelligence, machine learning algorithms, and a range of sensors to perform various tasks in a healthcare setting, including patient monitoring, communication, and disinfection.
The design and implementation of Sultana prioritize safety, versatility, and user-friendliness, ensuring seamless integration into existing healthcare infrastructure. The robot’s human-like appearance and interactive capabilities facilitate natural and intuitive interaction with both patients and medical staff, enhancing communication and rapport in clinical settings.
Key features of Sultana include autonomous navigation, vital signs monitoring, voice recognition, and disinfection capabilities. Its ability to operate autonomously reduces the risk of virus transmission and alleviates the burden on healthcare workers, allowing them to focus on critical tasks while Sultana assists with routine activities.
Performance evaluations and field tests demonstrate Sultana’s effectiveness in supporting healthcare professionals, improving patient care, and enhancing infection control measures. The development of Sultana represents a significant milestone in the application of robotics and AI technology to address healthcare challenges, particularly during public health crises such as the COVID-19 pandemic.
Design and Implementation of a Gait Specification Device
Gait analysis is one of the most used approaches to detecting walking patterns. Instruments for measuring body movements and body mechanics have broadened the study of human movement, using the eye and the brain of spectators. One of the most common approaches for gait detection systems is the non-approach method using the wearable sensors to calculate the gait parameter. In this report, a device that can measure gait specifications in a 5m straight path includes cadence, gait speed, step length, step time, stance time, swing time that detects a gait cycle accurately is designed to achieve these specifications by using Arduino nano and wearable sensors. The algorithm of pitch-yaw-roll axis is implemented to module MPU6050, and voltage detection function for Force sensor resistance is applied to enhance the accuracy of the system. These gait signal data processed by using Arduino nano micro-controller is then transferred to SD card to record the data. Also, the power consumption of the system has been calculated approximately. The validity of this system was confirmed through the accuracy in detection compared with real-time received signal.
Design and Implementation of LED Flasher PCB Board using KiCAD software
This project presents the design and implementation of a LED flasher printed circuit board (PCB) using KiCAD software, aimed at providing a practical and hands-on introduction to PCB design and fabrication. The LED flasher circuit is a simple yet versatile electronic project that involves blinking LEDs at adjustable frequencies, suitable for educational purposes and hobbyist experimentation. The design process encompasses schematic capture, component placement, routing, and Gerber file generation using KiCAD’s user-friendly interface and powerful design tools. The implemented PCB board is manufactured using standard fabrication processes, and the assembled components are tested for functionality and performance. The paper includes detailed instructions and guidelines for each step of the design and implementation process, catering to beginners and enthusiasts alike. Performance evaluations demonstrate the functionality and reliability of the LED flasher PCB board, serving as a practical learning tool for PCB design and electronics prototyping. This project contributes to the accessibility and affordability of PCB design education, fostering hands-on learning experiences in electronics engineering and related fields.
Design and Implementation of An Autonomous Fire Fighting Robot
This project introduces the design and implementation of an autonomous fire-fighting robot aimed at improving firefighting capabilities in hazardous environments. The robot is equipped with various sensors, including heat sensors, smoke detectors, and infrared cameras, to detect and locate fires autonomously. Upon detecting a fire, the robot navigates through the environment using obstacle avoidance algorithms and map-based navigation techniques. It carries a fire suppression system, such as water or foam sprayers, to extinguish the flames effectively. The control system incorporates decision-making algorithms to prioritize actions based on fire severity and environmental conditions. The design process involves selecting suitable hardware components, developing control algorithms, and integrating the system into a cohesive unit. Performance evaluations demonstrate the robot’s effectiveness in autonomously detecting and extinguishing fires, reducing response times, and minimizing risks to human firefighters. This autonomous fire-fighting robot represents a significant advancement in firefighting technology, offering a safer and more efficient approach to combating fires in challenging environments.
Design & implementation of a Gesture Controlled Wheelchair
This project presents the design and implementation of a gesture-controlled wheelchair system, aimed at providing individuals with limited mobility greater independence and ease of navigation. The system utilizes gesture recognition technology to interpret hand movements and gestures, enabling intuitive control of the wheelchair’s movements. A combination of sensors, such as accelerometers, gyroscopes, and cameras, captures and interprets the user’s gestures in real-time. These signals are then processed by a microcontroller or a computing unit, which translates them into commands for the wheelchair’s actuators, such as motors or servos. The design process encompasses the selection and integration of sensors, development of gesture recognition algorithms, and interfacing with the wheelchair’s control system. Performance evaluations demonstrate the system’s accuracy, responsiveness, and usability in controlling the wheelchair’s speed, direction, and other functionalities. This gesture-controlled wheelchair system offers a user-friendly, hands-free alternative to traditional joystick-based controls, empowering individuals with mobility impairments to navigate their environment with greater freedom and autonomy.
Design & Implementation of a Water Level Monitoring System using PIC Microcontroller
This project presents the design and implementation of a water level monitoring system using a PIC microcontroller, aimed at providing accurate and real-time water level data for various applications such as water tanks, reservoirs, and agricultural fields. The system employs ultrasonic sensors to measure the water level, with the data being processed by a PIC microcontroller. The microcontroller converts the sensor data into meaningful water level information and displays it on an LCD screen. Additionally, the system includes alert mechanisms such as LEDs and buzzers to notify users when water levels reach critical thresholds. The design process encompasses the selection and interfacing of hardware components, development of the firmware, and integration of the system. Performance evaluations indicate that the system provides reliable and precise water level measurements, demonstrating quick response times and robustness in different environmental conditions. This water level monitoring system offers a cost-effective, scalable, and efficient solution for real-time water management, contributing to resource conservation and effective water usage.
Design & Implementation of a PID Control Line Follower Robot
This project details the design and implementation of a PID (Proportional-Integral-Derivative) control-based line follower robot, aimed at achieving precise and efficient path tracking. The robot is equipped with infrared sensors to detect and follow a predetermined path marked by a line on the surface. The PID control algorithm is employed to continuously adjust the robot’s steering to maintain alignment with the path, minimizing deviations and ensuring smooth navigation. Key components include a microcontroller for processing sensor input, motor drivers for actuating wheel movements, and a set of infrared sensors for line detection. The design process covers the selection of hardware components, development of control algorithms, and integration of the system. The implementation is tested in various scenarios to evaluate the robot’s performance in terms of accuracy, response time, and robustness. Results demonstrate that the PID control enhances the robot’s ability to follow the line with high precision, effectively handling sharp turns and varying speeds. This project showcases the effectiveness of PID control in robotics applications, offering insights into its potential for more complex autonomous navigation tasks.
Design & Implementation of Home Automation Device
This project presents the design and implementation of a home automation device aimed at enhancing convenience, energy efficiency, and security in residential settings. The system integrates various smart devices and appliances through a central hub, which is controlled via a user-friendly mobile application or voice commands. Key features include automated lighting, climate control, security monitoring, and energy management. The device utilizes IoT technology to enable seamless communication between sensors, actuators, and the central hub, supporting wireless protocols such as Zigbee, Z-Wave, and Wi-Fi. Advanced features like machine learning algorithms are incorporated to adapt to user preferences and optimize energy consumption. The implementation process covers hardware design, software development, and system integration, ensuring robustness and scalability. Performance evaluations demonstrate the system’s reliability, ease of use, and effectiveness in automating daily household tasks. This home automation device represents a significant step towards smarter, more efficient living environments, providing users with increased comfort, security, and energy savings.
Automated Car Parking System
This project presents the development of an automated car parking system designed to optimize space utilization and enhance user convenience in urban environments. The system integrates advanced technologies including IoT, sensors, and machine learning to provide a seamless parking experience. Ultrasonic and infrared sensors are employed to detect vehicle presence and measure available parking space dimensions. An IoT-based network facilitates real-time communication between sensors, the central control unit, and user interfaces. The system includes a mobile application that guides users to available parking spots, provides reservation options, and enables automated payment processing. Machine learning algorithms analyze parking patterns and predict future availability, further optimizing the parking process. Performance evaluations in various scenarios demonstrate high accuracy in vehicle detection and space allocation, significantly reducing the time spent searching for parking. This automated car parking system offers a smart, efficient, and user-friendly solution to modern parking challenges, enhancing urban mobility and reducing traffic congestion.
Design & Implementation IoT Based Solar Power Monitoring and Data Logger System
This project details the design and implementation of an IoT-based solar power monitoring and data logger system. The proposed system aims to enhance the efficiency and reliability of solar power installations by providing real-time monitoring and data analysis. Utilizing various sensors, the system measures key parameters such as voltage, current, temperature, and irradiance. These data points are transmitted to a central server via an IoT gateway, employing wireless communication protocols such as Wi-Fi or LoRa. The data logger component stores and processes the collected information, enabling detailed analysis and performance tracking over time. A user-friendly web interface offers real-time visualization, historical data access, and alert mechanisms for maintenance needs. The system’s design emphasizes low power consumption, scalability, and ease of deployment. Field tests confirm its accuracy and robustness, demonstrating significant potential for improving the management and optimization of solar power systems.