Zobrazeno 1 - 10
of 8 362
pro vyhledávání: '"A. P. Yalin"'
In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then transmitted over a wireless channel. At the receiver, a decoder performs a
Externí odkaz:
http://arxiv.org/abs/2411.10385
Matrix factor models have been growing popular dimension reduction tools for large-dimensional matrix time series. However, the heteroscedasticity of the idiosyncratic components has barely received any attention. Starting from the pseudo likelihood
Externí odkaz:
http://arxiv.org/abs/2411.06423
Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the
Externí odkaz:
http://arxiv.org/abs/2411.01403
Autor:
Farazi, Mohammad, Wang, Yalin
Utilizing patch-based transformers for unstructured geometric data such as polygon meshes presents significant challenges, primarily due to the absence of a canonical ordering and variations in input sizes. Prior approaches to handling 3D meshes and
Externí odkaz:
http://arxiv.org/abs/2411.00164
Radio frequency (RF) communication has been an important part of civil and military communication for decades. With the increasing complexity of wireless environments and the growing number of devices sharing the spectrum, it has become critical to e
Externí odkaz:
http://arxiv.org/abs/2410.18283
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in comple
Externí odkaz:
http://arxiv.org/abs/2410.15036
Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliable wireless communications. However, traditional DRL methods are suscep
Externí odkaz:
http://arxiv.org/abs/2410.10521
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a U-shaped architecture (UNet) with skip connections have been adopted f
Externí odkaz:
http://arxiv.org/abs/2410.11578
Autor:
Demir, Utku, Davaslioglu, Kemal, Sagduyu, Yalin E., Erpek, Tugba, Anderson, Gustave, Kompella, Sastry
Integrated Sensing and Communication (ISAC) represents a transformative approach within 5G and beyond, aiming to merge wireless communication and sensing functionalities into a unified network infrastructure. This integration offers enhanced spectrum
Externí odkaz:
http://arxiv.org/abs/2410.08999
Autor:
Costa, Maice, Sagduyu, Yalin E.
Publikováno v:
Proc. 2024 IEEE International Conference on Communications Workshops, pp.554-559. %\thanks{Peer-reviewed version in Proc. 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 554-559
We consider the transfer of time-sensitive information in next-generation (NextG) communication systems in the presence of a deep learning based eavesdropper capable of jamming detected transmissions, subject to an average power budget. A decoy-based
Externí odkaz:
http://arxiv.org/abs/2410.08045