Zobrazeno 1 - 10
of 6 917
pro vyhledávání: '"Tales, A."'
Autor:
Ghanem, Paul, Demirkaya, Ahmet, Imbiriba, Tales, Ramezani, Alireza, Danziger, Zachary, Erdogmus, Deniz
Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the problem of learning dynamics governing these systems when parts of th
Externí odkaz:
http://arxiv.org/abs/2412.08681
Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.15996
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced datasets and use of different data sources across clients. Knowledge sh
Externí odkaz:
http://arxiv.org/abs/2410.15473
Autor:
Shlezinger, Nir, Revach, Guy, Ghosh, Anubhab, Chatterjee, Saikat, Tang, Shuo, Imbiriba, Tales, Dunik, Jindrich, Straka, Ondrej, Closas, Pau, Eldar, Yonina C.
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) mo
Externí odkaz:
http://arxiv.org/abs/2410.12289
Recent works have introduced LEAPS and HPRL, systems that learn latent spaces of domain-specific languages, which are used to define programmatic policies for partially observable Markov decision processes (POMDPs). These systems induce a latent spac
Externí odkaz:
http://arxiv.org/abs/2410.12166
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:
Calatrava, Helena, Ranganathan, Aanjhan, Imbiriba, Tales, Schirner, Gunar, Akcakaya, Murat, Closas, Pau
This paper presents a radar target tracking framework for addressing main-beam range deception jamming attacks using random finite sets (RFSs). Our system handles false alarms and detections with false range information through multiple hypothesis tr
Externí odkaz:
http://arxiv.org/abs/2408.11361
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