no code implementations • 17 Jul 2023 • Mustafa Yıldırım, Niyazi Ulas Dinc, Ilker Oguz, Demetri Psaltis, Christophe Moser
In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field.
1 code implementation • 3 Jul 2023 • Iman Sharifi, Mustafa Yıldırım, Saber Fallah
The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving.
no code implementations • 30 May 2023 • Ilker Oguz, Junjie Ke, Qifei Wang, Feng Yang, Mustafa Yıldırım, Niyazi Ulas Dinc, Jih-Liang Hsieh, Christophe Moser, Demetri Psaltis
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
no code implementations • 5 Sep 2022 • Mustafa Yıldırım, Sajjad Mozaffari, Luc McCutcheon, Mehrdad Dianati, Alireza Tamaddoni-Nezhad Saber Fallah
This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving.
no code implementations • 19 Aug 2022 • Mustafa Yıldırım, Ilker Oguz, Fabian Kaufmann, Marc Reig Escale, Rachel Grange, Demetri Psaltis, Christophe Moser
A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide.
1 code implementation • 22 Dec 2020 • Uğur Teğin, Mustafa Yıldırım, İlker Oğuz, Christophe Moser, Demetri Psaltis
Today's heavy machine learning tasks are fueled by large datasets.