Zobrazeno 1 - 10
of 874
pro vyhledávání: '"Erdogmus Deniz"'
Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still
Externí odkaz:
http://arxiv.org/abs/2409.19518
Autor:
Potter, Michael, Tang, Shuo, Ghanem, Paul, Stojanovic, Milica, Closas, Pau, Akcakaya, Murat, Wright, Ben, Necsoiu, Marius, Erdogmus, Deniz, Everett, Michael, Imbiriba, Tales
Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measureme
Externí odkaz:
http://arxiv.org/abs/2405.18999
Autor:
Potter, Michael, Akcakaya, Murat, Necsoiu, Marius, Schirner, Gunar, Erdogmus, Deniz, Imbiriba, Tales
Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previo
Externí odkaz:
http://arxiv.org/abs/2402.17987
Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face im
Externí odkaz:
http://arxiv.org/abs/2402.15656
Autor:
Sunger, Elifnur, Kalkanli, Beyza, Yildiz, Veysi, Imbiriba, Tales, Campbell, Peter, Erdogmus, Deniz
Curvature estimation methods are important as they capture salient features for various applications in image processing, especially within medical domains where tortuosity of vascular structures is of significant interest. Existing methods based on
Externí odkaz:
http://arxiv.org/abs/2311.11931
Autor:
Smedemark-Margulies, Niklas, Wang, Ye, Koike-Akino, Toshiaki, Liu, Jing, Parsons, Kieran, Bicer, Yunus, Erdogmus, Deniz
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using new regularization techniques during model training. We propose several
Externí odkaz:
http://arxiv.org/abs/2310.08762
Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember variability negatively impact the performance of most existing algorith
Externí odkaz:
http://arxiv.org/abs/2310.02340
Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it has been a very challenging task due to non-standard fetal head pose, fetal movements during examination, and vastly heterogeneous appearanc
Externí odkaz:
http://arxiv.org/abs/2310.01523
Autor:
Smedemark-Margulies, Niklas, Bicer, Yunus, Sunger, Elifnur, Naufel, Stephanie, Imbiriba, Tales, Tunik, Eugene, Erdoğmuş, Deniz, Yarossi, Mathew
Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomecha
Externí odkaz:
http://arxiv.org/abs/2309.12217
Autor:
Bicer, Yunus, Smedemark-Margulies, Niklas, Celik, Basak, Sunger, Elifnur, Orendorff, Ryan, Naufel, Stephanie, Imbiriba, Tales, Erdoğmuş, Deniz, Tunik, Eugene, Yarossi, Mathew
Publikováno v:
in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 1187-1197, 2024
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. sEMG data were streamed into a machine-learning algorithm that clas
Externí odkaz:
http://arxiv.org/abs/2309.07289