a tiny little space for me in this mighty universe
"There are no constraints on the human mind, no walls around the human spirit, no barriers to our progress except those we ourselves erect"
I'm Gogul Ilango. I have strong passion towards creating useful products that make people's life better with focus on Aesthetic, Creative Design and Engineering.
Currently, I work as a Physical Design Engineer + Full Stack Web Developer, who helps a team of intelligent minds at Qualcomm in designing cutting-edge chipsets (7nm, 14nm, 28nm) that millions of people around the world use it in their everyday life.
I'm one of the Deans at Chennai School of AI which is a learning community of AI enthusiasts, college students, developers, industry experts and entrepreneurs where we discuss, analyze, prototype, learn and share AI related stuff with the world for free under the guidance of our director Siraj Raval.
I completed my masters in VLSI Design and Embedded Systems at Anna University, MIT Campus, Chennai with a gold medal 🥇 and CGPA 9.96/10.
I completed my bachelors in Electronics and Communication Engineering at Thiagarajar College of Engineering, Madurai with a CGPA 9.05/10.
I have diverse technical areas of interest. Believe it or not, I really love each and every one of my interests listed below.
- ASIC Design
- Physical Design
- Static Timing Analysis
- Power Analysis
- Full-Stack Web Development
- Machine Learning
- Deep Learning
- Computer Vision
- Natural Language Processing
- User Interface Design
- Web Design
- Poster Design
- Music Production
- Music Composition
- Music Arrangement
- Music Editing
In this personal website, you will find collection of my thoughts, notes, tutorials and resources based on my experience in technology. I still learn by myself about the technical topics that I write here so that I get a clear understanding of it.
I do this mainly during my free time because
- It helps me learn these topics better by making me read, write and evaluate myself first before sharing it here.
- It provides me a chance to organize my technical interests so that I can refer to it later.
- It gives me a chance to share my knowledge with the world where it might help someone somewhere.
I also share resources to help students find useful information in the internet. If you like my posts, kindly share it with people interested in these topics. It really mean so much to me. Hope you find useful content in my blog 😉
Apart from tech, I love music and art. I like creating tunes, producing and arranging music using Digital Audio Workstations (DAW), playing keyboard, guitar, ukulele and writing lyrics. You can listen to my music here.
Analyzing ConvNets Depth for Deep Face Recognition
Mohan Raj, I. Gogul, M. Deepan Raj, V. Sathiesh Kumar, V. Vaidehi, S. Sibi Chakkaravarthy
CVIP-2017, Springer pp 317-330[paper]
Flower species recognition system using convolution neural networks and transfer learning
I.Gogul, V. Sathiesh Kumar
ICSCN-2017, IEEE Xplore[paper]
Static gesture recognition based precise positioning of 5-DOF robotic arm using FPGA
M. Deepan Raj, I. Gogul, M. Thangaraja, V. Sathiesh Kumar
TIMA-2017, IEEE Xplore[paper]
Smart Autonomous Gardening Rover with Plant Recognition using Neural Networks
V. Sathiesh Kumar, I.Gogul, M. Deepan Raj, S.K.Pragadesh, J. Sarathkumar Sebastin
ICACC-2016, Elsevier Procedia Computer Science Volume 93, 2016, Pages 975-981[paper]
DeepDrum & DeepArp
Emotion Recognizer using Deep Neural Network
A real-time implementation of emotion recognition using two deep neural networks (extractor and classifier) using Google's TensorFlow.js in the browser. Model is created, trained and inferred in real-time with data acquisition happening in client's device.
Recognizing Digits using Deep Neural Network in Google Chrome
Recognize handwritten digits drawn by a user in a canvas in real-time using Deep Neural Network such as Multi-Layer Perceptron (MLP) or Convolutional Neural Network (CNN) in the browser (specifically Google Chrome).
Dataset: MNIST Handwritten Digitsvideo | tutorial
Classifying images using Keras MobileNet in Google Chrome
Perform image classification in real-time using Keras MobileNet, deploy it in Google Chrome using TensorFlow.js and use it to make live predictions in the browser (specifically Google Chrome).
Dataset: IMAGENET (1000 categories)tutorial
Flower Species Recognition System
Recognize different flower species using state-of-the-art Deep Neural Networks such as VGG16, VGG19, ResNet50, Inception-V3, Xception, MobileNet in Keras and Python. Also, a detailed comparison between Global Feature Descriptors and data-driven approach for this fine-grained classification problem was studied.
Tools used: Keras, Python.
Dataset: FLOWERS17 (University of Oxford)video | tutorial 1 | tutorial 2
Sound Classification using Neural Networks
An environment sound classification example that shows how Deep Learning could be applied for audio samples.
Tools used: Keras, Python.
Dataset: ESC-50 - Environmental Sound Classificationvideo
Monocular Visual Odometry using OpenCV and Python
Feature based Monocular Visual Odometry using FAST corner detector, KLT Tracker, Nister's five point algorithm and RANSAC algorithm with the help of OpenCV and Python.
Tools used: OpenCV, Python.
SMART Home - Continuous Voice Recognition using Android
A SMART Home automation system using off-the-shelf technologies such as Android and Arduino to control home appliances such as Fan, Light bulbs and other electronic appliances with the help of relay and your voice.
Tools used: Arduino Uno micro-controller, Android smartphone, 8-channel relay module, HC-05 Bluetooth module, Jumper wires, Batteries, Arduino IDE, Android Studio 2.2, Philips Wireless speaker.video
SLAMINOR - Parallel DC and Servo Control using Xilinx Zedboard
Parallel control of 2 DC motors and a servo motor using Xilinx Zedboard.
Tools used: FPGA - Xilinx Zedboard, IDE - Vivado Design Suite 2014.2, Clock Frequency - 50 MHz, DC motors - 500 RPM 12V, Servo motor - Futaba S3003, Battery - 12V 1.3A, Motor Driver - L293D.video
Hand Gesture Recognition and Servo Control
Recognize hand gestures using OpenCV and Python, and control a servo motor based on the gestures using Odroid-XU4 and Arduino Mega.
Tools used: Ardunio Mega, Odroid-XU4, Python, Arduino IDE, Servo motor - Futaba S3003, Battery - 12V 1.3A.video | tutorial 1 | tutorial 2
A standard Quadcopter for medical applications.
Tools used: Flight Controller - APM 2.6, Electronic Speed Controllers - 30A, Brushless DC Motors - 1000KV, Power Source - Turnigy 3000 mAh 3S 20C LiPo battery, Quad Copter Frame - F450, Turnigy 6 channel FHSS 2.4Ghz Tx/Rx.video
Autonomous LOTA Robot
A small robotic vehicle that can follow a line, detect obstacles, manages to run on the top of a table without falling down and could control its speed with the help of sensors and ADC.
Tools used: Microcontroller - ATmega16, DC Motors - 100 RPM, Power source - 12V battery, Sensors - 4 Infrared sensors, Other parts - Potentiometer, NOT gate, chassis, wheels.video
June 19, 2017 - Present
ASIC Physical Design & Signoff
Automation, Machine Learning, Deep Learning, Full-Stack Web Development, Tool Development
Graduate Technical Intern
Dec 05, 2016 - Jun 05, 2017
Automation, Tool Development, Machine Learning, Web Development
Assistant Systems Engineer
Jun 26, 2014 - Aug 15, 2015
Front-End Web Development
Web Development, Android App Development, Hybrid Mobile App Development, User Interface Design