Electronics and Communication Engineer

github

Naveen Gumaste • Peerlist

This project implements and evaluates an Autonomous Emergency Braking (AEB) system within the CARLA simulator. The system detects obstacles and autonomously applies braking to mitigate collisions. Braking initiation occurs at 8 meters, targeting a complete stop within 2 meters of the obstacle. System performance and collision avoidance were validated using CARLA's visualization tools

Autonomous Emergency Braking System with Carla Simulator

Developed a project using deep learning methods to enhance predictive accuracy and automate complex tasks. This project leveraged advanced neural networks to analyze data efficiently, leading to improved decision-making and performance. The application of deep learning in this context demonstrated significant advancements in automation and intelligence.

Collision Avoidance and Detection System

This study proposes a novel deep-learning solution for early detection of diabetic retinopathy, a major cause of vision loss. Utilizing Convolutional Neural Networks (CNNs), our model analyzes retinal images to predict the presence of DR with an impressive 93% accuracy. This approach empowers healthcare professionals with a valuable tool for early diagnosis, potentially leading to improved patient care and vision preservation.

Eye Disease Detection: Diabetic Retinopathy

Designed an autonomous quadcopter featuring GPS guidance for seamless tracking, equipped with a range of sensors for enhanced functionality. The drone is capable of autonomously following a user-defined flight path, reducing the need for advanced piloting skills. Furthermore, it incorporates a wireless camera system, providing live footage up to 40 feet away from the pilot or transmitter for an immersive aerial experience

Gps Guided Autonomous Drone [Quad-Copter]

We developed a Sensor-Driven HVAC System, integrating an LM35 temperature sensor with precise ADC calculations. Implemented real-time LCD and LED indicators for adaptive control of heating, ventilation, and air conditioning peripherals. Enhanced occupant comfort and energy efficiency through robust sensor integration, immediate feedback, and adaptive adjustments.

Hvac System on  "LPC1768"

Developed a user-friendly smart parking web app using JavaScript, HTML, and Tailwind CSS to combat urban challenges. By incorporating real-time availability data from IR sensors and servo motors for demonstration, the app reduces search time and traffic congestion. It optimizes parking and promotes smoother urban flow.

Intelligent Parking Space Management System

Developed an advanced image processing system utilizing the Jetson NVIDIA Orin board. By leveraging CUDA cores for parallel processing, this project efficiently converts RGB images to grayscale and applies image enhancement techniques, significantly improving processing speed and image quality.

Multithreaded Image Processing with Grayscale Conversion

Designed a Toll Booth System simulation in Proteus, incorporating RFID technology. The simulation comprises an RFID reader, a microcontroller (LPC 2148), and a vehicle detection sensor. When a vehicle nears the toll booth, the RFID reader scans its tag for identification. Subsequently, the microcontroller deducts the corresponding toll amount from the vehicle's account and activates the toll gate to allow passage. This system mimics an efficient and automated toll collection process, enhancing the overall functionality of toll booths.

Toll Collection System Simulation in Proteus: A Microcontrol

Developed a first-in-YOLOv8 system for traffic light detection in Intelligent Transportation Systems (ITS). This project addresses critical aspects of road safety and traffic management, employing a comprehensive analysis from traditional computer vision to advanced deep learning techniques. Leveraging datasets from ROBOFLOW UNIVERSE, our approach enhances precision and efficiency in detecting traffic lights, crucial for urban mobility challenges. This pioneering work contributes to the evolving landscape of ITS, ensuring real-world relevance and meaningful advancements.