Neural Neworks

A Deap Learning based method that aligns the Myocardial T1-weighted images of CRM (Cardiac Magnetic Resonance)

Training a YOLOv4 network for drone detection and implementation on an NVIDIA Jetson TX2 installed on a UAV

Build video dataset of flying drones for drone detection network

Eye super-resolution from synthetic data for improved gaze estimation in unconstrained settings

Building models to improve transfer learning techniques for breast cancer detection using small data sets of mammograms and US scans

Segmentation of corals using data augmentation and training of existing ConvNet

Collect data base and train a CNN to detect drones in images

Create data set and train Resnet for smoke classification in images

Recognition and identification of obstacles around USV using YOLO network

Improve the STGV algorithm for depth recovery using state of the art Deep Learning based segmentation techniques

Identify nerve structure in ultrasound images using Deep Learning.

Using CNN neural network for classification of human \ non human objects in thermal images

The purpose of this project is to identify and classify Event Related Potential using competitive Neural Net architecture.