More Project Information

Explore in-depth project details and discover the innovation behind my work in software and hardware integration, paving the way for a future of technological advancements. Click the GitHub icon below to see all my code!
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Jetson Nano Custom Kernel Modules

Developed custom kernel modules for the NVIDIA Jetson Nano, showcasing expertise in C programming and Linux kernel development.

  • Integrated a CPU-based Linux kernel module and a GPU-accelerated CUDA kernel into a unified user application. Combined the both CPU and GPU environments to create a comprehensive firmware solution for effective .ppm image manipulation on the NVIDIA Jetson Nano.
  • Implemented image processing solutions for the NVIDIA Jetson Nano, utilizing a custom CPU-based Linux kernel module. Developed algorithms within the Linux kernel environment to efficiently process image data, ensuring seamless integration with the overall firmware.
  • Independently executed optimization of CUDA kernels, leveraging NVIDIA GPUs' parallel processing capabilities to enhance image processing performance. Achieved substantial performance enhancements by parallelizing image manipulation algorithms and maximizing GPU utilization.
  • Demonstrated expertise in C programming and Linux kernel development, showcasing strong problem-solving skills in developing algorithms for image processing and manipulation.
  • Experimented with CUDA parrallelization techniques to streamline the computation of data that can benefit from simultaneous processing.
  • Here's a video demonstrating my project...

Arduino Weather Station

I designed and programmed a fully functional Arduino-based weather station, leveraging a diverse programming skill set that includes C++, Rust, HTML, CSS, JavaScript, and SQL. Utilized sensors and microcontrollers, to deliver real-time environmental monitoring.

  • Created C++ firmware for a NodeMCU ESP8266 module to send post requests to my RESTful based RUST API that contained weather information communicated serially from my Arduino.
  • Programmed C++ firmware for Arduino, devising data acquisition algorithms to efficiently gather information from temperature, humidity, pressure, altitude, light, and time sensors. development of the project.
  • Designed a robust back end in Rust using the Actix Web framework, implementing a high-performance RESTful API for the weather station. Leveraged SQL for efficient data storage and retrieval, ensuring seamless integration with the web interface.
  • Engineered a dynamic and user-friendly web interface using JavaScript, HTML, and CSS for the weather station project. Implemented responsive designs and interactive features, providing real-time weather information in an intuitive format.
  • Carefully curated a suite of essential sensors required for the weather station project, demonstrating meticulous research and procurement skills, and skillfully employed soldering techniques to add header pins to sensors lacking them, ensuring seamless integration into the Arduino-based system.
  • Developed and meticulously followed a comprehensive circuit diagram for the weather station project, orchestrating the intricate connection of diverse sensors on a breadboard. Ensured precise calculations of current and resistance values, guaranteeing the seamless integration of each sensor with the Arduino for optimal functionality and data accuracy.
  • Here's a video demonstrating my project...Arduino Weather Station

Image Identification Using Machine Learning

Constructed a robust image recognition system utilizing Python, TensorFlow, and Keras, implementing an optimized convolutional neural network (CNN) for accurate classification on the CIFAR-10 dataset. Incorporated an intuitive Flask-based web interface, enabling users to effortlessly upload images for instantaneous real-time predictions.

  • Developed a robust image recognition system using Tensorflow and Keras, implementing a convolutional neural network (CNN) for precise image classification based upon the CIFAR-10 dataset.
  • Crafted an intuitive local web interface with the Flask web-framework, combining HTML, CSS, and JavaScript to allow users to seamlessly upload images for class prediciton based on my models evaluation.
  • Iteratively fine-tuned the model and training parameters to enhance accuracy, showcasing expertise in Python-based deep learning and neural network architectures with a focus on TensorFlow and the Keras API.
  • Implemented a robust data augmentation pipeline to generate a diverse dataset, highlighting my expertise in data preprocessing and enhancing the model's ability to generalize.
  • Employed a Docker container with ROCm support provided by AMD to meticulously train the image recognition model using TensorFlow and Keras. This strategic utilization of containerization technology not only ensured a consistent and reproducible training environment but also optimized the training process for enhanced performance and efficiency.
  • Here's a link to a video demonstrating my project... Image Recognition Video

Raspberry Pi Gestrue Controlled LED System

Engineered a Raspberry Pi Gesture Controlled LED System, crafting Python-based firmware for GPIO pin control, developing a React Native mobile app for camera-initiated gesture recognition processed through Python and OpenCV, and implementing a responsive RESTful Rust-based API for real-time interaction with individually addressable RGB LED light strips.

  • Developed Python-based Raspberry Pi firmware to efficiently control GPIO pins, enabling interaction with RGB LED light strips that are individually addressable, allowing for pattern creation and manipulation.
  • Formulated a React Native (JavaScript based) mobile app to initiate camera and gesture recognition on the Raspberry Pi, allowing users to control LED light strips with recognized gestures that are processed through Python using the OpenCV library.
  • Implemented a RESTful Rust-based API to ensure communication between the React Native mobile app and the Raspberry Pi. This API facilitates real-time interaction with GPIO pins, providing responsive control over the LED light strips.
  • Explored innovative possibilities by experimenting with the recreation of recognized hand gesture shapes through the individually addressable RGB LED light strips, enhancing the interactive and visual aspects of the gesture-controlled system.
  • Conducted rigorous testing and executed the developed code remotely on the Raspberry Pi using SSH, ensuring the reliability and responsiveness of the gesture-controlled LED system in various real-world scenarios.
  • Utilized Bash to create scripts that were called at my API endpoints in order to start and stop my Gesture recognition program, ensuring that the program was always running when the API was called.
  • Here's a video demonstrating my project... Raspberry Pi Gesture Recognition Video

Precision Engineered DIY Microphone Circuit

I designed and implemented a precision-engineered DIY microphone circuit, showcasing my expertise in analog circuit design, understanding of basic electrical components, and schematic reading.

  • Developed a high-performance microphone circuit using a combination of electronic components, including resistors, capacitors, transistors, op-amps, and more, demonstrating expertise in analog circuit design.
  • Implemented rigorous testing and calibration procedures to validate the microphone's frequency response and sensitivity.
  • Leveraged knowledge of analog and digital electronics to troubleshoot and resolve any issues in the microphone circuit's performance.
  • Proficiently interpreted complex electrical schematics, demonstrating a keen understanding of electronic components and their interconnections.

Arduino Binary Converter

I designed and implemented a binary converter using an Arduino microcontroller, showcasing my expertise in C/C++ and microcontroller programming.

  • Developed the firmware for the Arduino binary converter using C/C++, demonstrating my proficiency in these languages.
  • Implemented rigorous testing and calibration procedures to validate the binary converter's performance.
  • Demonstrated strong problem-solving skills in developing algorithms to convert decimal numbers to binary representations.
  • Designed and implemented custom LED driver circuits to efficiently represent numbers between 0 and 15 using LEDs.

Digital Signal Processing Python Project

I designed and implemented a digital signal processing project using Python, showcasing my expertise in Python programming and digital signal processing.

  • Developed a Python script for signal processing using SciPy, showcasing proficiency in Python programming for scientific computing.
  • Implemented a Butterworth low-pass filter for signal filtering, demonstrating expertise in digital signal processing techniques.
  • Demonstrated strong mathematical skills in understanding and applying signal processing algorithms.
  • Utilized Matplotlib for data visualization, highlighting my proficiency in this Python library.