Zobrazeno 1 - 10
of 44 719
pro vyhledávání: '"A, Santamaría"'
Autor:
Soleymani, Mohammad, Santamaria, Ignacio, Jorswieck, Eduard, Di Renzo, Marco, Schober, Robert, Hanzo, Lajos
The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interfer
Externí odkaz:
http://arxiv.org/abs/2411.11028
Autor:
Fronza, Ilenia, Ihantola, Petri, Riikola, Olli-Pekka, Iaccarino, Gennaro, Mikkonen, Tommi, Rytman, Linda García, Lappalainen, Vesa, Santamaría, Cristina Rebollo, Quintana, Inmaculada Remolar, Rossano, Veronica
Coding camps bring together individuals from diverse backgrounds to tackle given challenges within a limited timeframe. Such camps create a rich learning environment for various skills, some of which are directly associated with the camp, and some of
Externí odkaz:
http://arxiv.org/abs/2411.05390
The challenges in dense ultra-reliable low-latency communication networks to deliver the required service to multiple devices are addressed by three main technologies: multiple antennas at the base station (MISO), rate splitting multiple access (RSMA
Externí odkaz:
http://arxiv.org/abs/2411.04581
In this paper, we address the exponential stabilization of the linearized FitzHugh-Nagumo system using an event-triggered boundary control strategy. Employing the backstepping method, we derive a feedback control law that updates based on specific tr
Externí odkaz:
http://arxiv.org/abs/2410.22266
Autor:
Soleymani, Mohammad, Zappone, Alessio, Jorswieck, Eduard, Di Renzo, Marco, Santamaria, Ignacio
We analyze the finite-block-length rate region of wireless systems aided by reconfigurable intelligent surfaces (RISs), employing treating interference as noise. We consider three nearly passive RIS architectures, including locally passive (LP) diago
Externí odkaz:
http://arxiv.org/abs/2410.20827
Autor:
Sanchez-Manzano, D., Humbert, V., Gutiérrez-Llorente, A., Zhang, D., Santamaria, J., Bibes, M., Iglesias, L., Villegas, Javier E.
Characterizing the dimensionality of the superconducting state in the infinite-layer (IL) nickelates is crucial to understand its nature. Most studies have addressed the problem by studying the anisotropy of the upper critical fields. Yet, the domina
Externí odkaz:
http://arxiv.org/abs/2410.14341
Autor:
Codella, Noel C. F., Jin, Ying, Jain, Shrey, Gu, Yu, Lee, Ho Hin, Abacha, Asma Ben, Santamaria-Pang, Alberto, Guyman, Will, Sangani, Naiteek, Zhang, Sheng, Poon, Hoifung, Hyland, Stephanie, Bannur, Shruthi, Alvarez-Valle, Javier, Li, Xue, Garrett, John, McMillan, Alan, Rajguru, Gaurav, Maddi, Madhu, Vijayrania, Nilesh, Bhimai, Rehaan, Mecklenburg, Nick, Jain, Rupal, Holstein, Daniel, Gaur, Naveen, Aski, Vijay, Hwang, Jenq-Neng, Lin, Thomas, Tarapov, Ivan, Lungren, Matthew, Wei, Mu
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT,
Externí odkaz:
http://arxiv.org/abs/2410.06542
Autor:
Scomparin, Luca, Caselle, Michele, Garcia, Andrea Santamaria, Xu, Chenran, Blomley, Edmund, Dritschler, Timo, Mochihashi, Akira, Schuh, Marcel, Steinmann, Johannes L., Bründermann, Erik, Kopmann, Andreas, Becker, Jürgen, Müller, Anke-Susanne, Weber, Marc
The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution thanks to their capability of autonomously tackling a cont
Externí odkaz:
http://arxiv.org/abs/2409.16177
We demonstrate spectroscopy of incoherent light with sub-diffraction resolution. In a proof-of-principle experiment we analyze the spectrum of a pair of incoherent point-like sources whose separation is below the diffraction limit. The two sources mi
Externí odkaz:
http://arxiv.org/abs/2409.01190
Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS).
Externí odkaz:
http://arxiv.org/abs/2408.04692