aviralchharia
AI @ CMU. AI, Machine Learning, Deep Learning, Computer Vision & Biomedical Informatics.
Carnegie Mellon UniversityPittsburgh, PA
Pinned Repositories
Autonomous-Vehicle-based-on-Dublin-Luas-Light-Rail-System
To develop an autonomous vehicle using Arduino (Atmega-328p) & remote wireless supervisory control (using, XCTU & XBee or C#) with capability of ultrasonic obstacle detection & avoidance, self-parking, stopping at gantries in its path & safely co-existing with other vehicles. The Infra-Red module, Transmitter & Receiver circuits were designed & fabricated on a PCB from scratch to develop the vehicle.
Autonomous-Vehicle-Project
Developed a computer vision system for an autonomous vehicle capable of lane changing, avoiding vehicle collision by calculating relative vehicle speeds, etc. Used Hough Transform, Canny edge detection & ED-lines algorithm. Applied CNN on German traffic sign database for identifying traffic-signs & implemented behavioral cloning on a simulator. Tested the model’s performance in different weather conditions.
Behavioral-Cloning
An end-to-end Self-driving car using CNN to map pixels from front-camera to steering angles on a simulator. This deep learning approach required minimum training data & the system learned to steer, with or without lane markings, on both local roads & highways, even with unclear visual guidance in various weather conditions. The vehicle could identify traffic signs & avoid collisions. Implemented NVIDIA's End-to-End Deep Learning Model for Self-Driving Car.
Brain-Tumor-Detection-with-VGG-16-Transfer-Learning
Aim of this project is to use Computer Vision techniques of Deep Learning to correctly identify & map Brain Tumor for assistance in Robotic Surgery.
Breast-Cancer-Prognosis
Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion
cAPTured
cAPTured: Neural Reflex Arc-Inspired Fuzzy Continual Learning for Capturing in Silico Aptamer-Target Protein Interactions
COVID-19
Research Project for Detection of COVID-19 from X-Ray using Deep Learning methods. Implemented convolutional neural network for classification of X-Ray Images into COVID & non-COVID cases.
Deep_Precognitive_Diagnosis
We propose a new class of Computer-aided diagnosis models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future.
Neural-Image-Captioning
In this project, we use a Deep Recurrent Architecture, which uses CNN (VGG-16 Net) pretrained on ImageNet to extract 4096-Dimensional image feature Vector and an LSTM which generates a caption from these feature vectors.
Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips
This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.
aviralchharia's Repositories
aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips
This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.
aviralchharia/Brain-Tumor-Detection-with-VGG-16-Transfer-Learning
Aim of this project is to use Computer Vision techniques of Deep Learning to correctly identify & map Brain Tumor for assistance in Robotic Surgery.
aviralchharia/COVID-19
Research Project for Detection of COVID-19 from X-Ray using Deep Learning methods. Implemented convolutional neural network for classification of X-Ray Images into COVID & non-COVID cases.
aviralchharia/Behavioral-Cloning
An end-to-end Self-driving car using CNN to map pixels from front-camera to steering angles on a simulator. This deep learning approach required minimum training data & the system learned to steer, with or without lane markings, on both local roads & highways, even with unclear visual guidance in various weather conditions. The vehicle could identify traffic signs & avoid collisions. Implemented NVIDIA's End-to-End Deep Learning Model for Self-Driving Car.
aviralchharia/Neural-Image-Captioning
In this project, we use a Deep Recurrent Architecture, which uses CNN (VGG-16 Net) pretrained on ImageNet to extract 4096-Dimensional image feature Vector and an LSTM which generates a caption from these feature vectors.
aviralchharia/Autonomous-Vehicle-based-on-Dublin-Luas-Light-Rail-System
To develop an autonomous vehicle using Arduino (Atmega-328p) & remote wireless supervisory control (using, XCTU & XBee or C#) with capability of ultrasonic obstacle detection & avoidance, self-parking, stopping at gantries in its path & safely co-existing with other vehicles. The Infra-Red module, Transmitter & Receiver circuits were designed & fabricated on a PCB from scratch to develop the vehicle.
aviralchharia/Autonomous-Vehicle-Project
Developed a computer vision system for an autonomous vehicle capable of lane changing, avoiding vehicle collision by calculating relative vehicle speeds, etc. Used Hough Transform, Canny edge detection & ED-lines algorithm. Applied CNN on German traffic sign database for identifying traffic-signs & implemented behavioral cloning on a simulator. Tested the model’s performance in different weather conditions.
aviralchharia/cAPTured
cAPTured: Neural Reflex Arc-Inspired Fuzzy Continual Learning for Capturing in Silico Aptamer-Target Protein Interactions
aviralchharia/Deep_Precognitive_Diagnosis
We propose a new class of Computer-aided diagnosis models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future.
aviralchharia/Breast-Cancer-Prognosis
Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion
aviralchharia/OpenAI-Hackathon-on-Climate-Change