Search Results for author: Yunus Bicer

Found 7 papers, 2 papers with code

User Training with Error Augmentation for Electromyogram-based Gesture Classification

1 code implementation13 Sep 2023 Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik, Elifnur Sunger, Ryan Orendorff, Stephanie Naufel, Tales Imbiriba, Deniz Erdoğmuş, Eugene Tunik, Mathew Yarossi

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration.

Gesture Recognition

A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition

no code implementations13 Sep 2023 Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdoğmuş, Mathew Yarossi

Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time.

Gesture Recognition Multi-Label Classification +1

Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules

no code implementations3 Oct 2021 Paul Ghanem, Yunus Bicer, Deniz Erdogmus, Alireza Ramezani

We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions.

Numerical Integration

Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation

no code implementations29 Jul 2020 Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure, Ahmetcan Erdogan, Orkun Kizilirmak

The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy.

Imitation Learning Self-Driving Cars

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

no code implementations18 Sep 2019 Ali Alizadeh, Majid Moghadam, Yunus Bicer, Nazim Kemal Ure, Ugur Yavas, Can Kurtulus

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility.

Autonomous Driving Decision Making +2

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