Who Am I?
I am a professional with a passion that ignited in my sixth grade, where I was introduced to the captivating world of analog electronics. From building circuits on a breadboard with the NE555 timer IC, I evolved into a dedicated learner, progressing through my tenth grade.
The transition to microcontrollers marked a turning point, revealing the remarkable ability of a single microcontroller to execute tasks equivalent to 10 distinct analog circuits by simply altering the code. This revelation fueled my curiosity and passion for embedded systems.
Continuing on this path, my love for the digital realm expanded, leading me to explore the domain of software engineering. Today, I stand as a seasoned back-end web developer, an enthusiast in edge computing, a proficient home automation expert, and a skilled cloud engineer. My expertise spans across Cloud Technologies, IoT, and Embedded Systems, offering a unique fusion of skills that seamlessly merges computer science and electronics.
In the face of challenges within modern IT infrastructure, I bring forth a diverse skill set, ready to conquer and innovate.
If something doesn't work as expected, just sudo it!
Skills
- Programming Languages
- C/C++
- Python
- MicroPython/CircuitPython
- Bash
- Rust
- Frameworks & Tools
- Flask
- Django
- Django REST
- Scikit-Learn/Tensorflow
- Databases
- SQL/SQLite3 & PostgreSQL
- NoSQL
- Cloud
- AWS and Azure
- Linux-based OS
- Docker
- Kubernetes
- Ansible
- HashiCorp Terraform and Packer
- Git/GitHub
- Embedded (µC/µP)
- Atmel AVR
- ESP82xx
- Raspberry Pi
- Raspberry Pi Pico
- Zephyr RTOS
- Miscellaneous
- Exit VIM
- VS Code
- LaTeX
- Netlify
Certifications
Projects
AI Enhanced CCTV Surveillance
The objective of this project is to introduce intelligence to CCTV systems. Raspberry Pi based edge-devices are placed in places of interest. These edge-devices run a custom binary image classification model built upon the TFLite Runtime library. The image captured by a camera attached to the end-device is inferred by the model and the prediction results are uploaded to a website. The website is built on Flask WSGI. It provides a dashboard to monitor images uploaded by various edge-devices and add/remove/update users and devices. Users that are added will be informed via SMS and email the information (image, location, time and date) of theft immediately. Also, only the edge-devices that are registered with the website can upload data. A end-device is assigned a UUID when it is registered with the website to provide a layer of authentication. This solution is readily deployable and manageable at a large scale.
Glance Village Hackathon-2023: Wildlife Interference Monitoring System
This project focuses on reducing the number of deaths caused due to wildlife interference. Raspberry Pi based edge-devices are placed in forest areas around the village with camera that infer images with a custom multi-class CNN image classification model built upon the Tensorflow library. All the data is uploaded to a centralized Flask WSGI server. The forest department can access a dashboard that streams live image feeds from all the edge-devices, add/remove/update first responders (to whom messages will be sent during calamity), view and download reports. If any image is classified as containing a wild animal, the Flask server automatically alerts all the first responders (villagers and forest officers) via SMS so that necessary actions can be carried out by them.
Tensorflow Custom Object Detection
A Tensorflow object detection model based on the SSD MobileNet built for Smart India Hackathon-2022 as finalists.
Tensorflow Sentiment Analyser
A LSTM sentiment analyser that can analyse sentences based on six categories.
About This Website
This website is a single page application stylized using Bootstrap. Not a lot of effort has been put onto the front-end side, except for its responsiveness and consistency in design.
The website is served by an Nginx server running on AWS EC2. This project can be containerized if needed. The project also is completely automated with a CI/CD pipeline that is built using AWS's services. Provisioning of these various services is automated with the IaC tool called Terraform. This may sound a bit over-engineering, but I call it learning.
Speaking about the future of this website, I've planned to build the front-end with ReactJS and back-end with Django. These frameworks will integrate with AWS services to work seamlessly without shooting a hole in my pocket. The website will include a blog page where I'll post my articles about my work, a chat page where you can chat with me/others (after logging-in apparently), a services page which will be a all-in-one page to use the most common services available on the Internet.
Social Profiles/Contact Me
