TOP PROJECTS

Project | 01

Ensemble Learning for Model Prediction

By using different predictive methods, such as random forest, support vector machines, and neural networks, and hyperparameter tuning, I have proposed a model for CNT-FET transistor's behavior prediction. This paper is published in the springer Journal of Computational Electronics.

Project | 02

Lucas Kanade Optical Flow Based on Memory Efficient Histogram Creation for Event-Based Data

Event-based data is inherently sparse and asynchronous, and histogram creation helps to have sharp edges and better tracking capability. My initial work got published in the ASAP 2020 conference.

Project | 03

Event-Based Object Tracking for Autonomous Vehicles

The sparseness of AER processing makes tracking mechanisms for event-based sensors completely difficult. This is different from the frame-based methods where we have a concrete scene for target estimation and object tracking.

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