"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 work as a Physical Design Engineer at Qualcomm who helps a team of intelligent minds in designing cutting-edge chipsets that millions of people around the world use in their everyday life. My tech interests include asic design, physical design, applying machine learning in hardware design, web development and of course, automation.
In this 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
I love music and you can hear my contributions here.
In case you're wondering, this site
Mohan Raj, I. Gogul, M. Deepan Raj, V. Sathiesh Kumar, V. Vaidehi, S. Sibi Chakkaravarthy
CVIP-2017, Springer pp 317-330
I.Gogul, V. Sathiesh Kumar
ICSCN-2017, IEEE Xplore
M. Deepan Raj, I. Gogul, M. Thangaraja, V. Sathiesh Kumar
TIMA-2017, IEEE Xplore
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
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.
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
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
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
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
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.
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
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
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
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
Below I have listed some of the greatest books written by greatest minds that helped me evolve as a human being. Hope it helps you too!