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pro vyhledávání: '"Gündüz A"'
Publikováno v:
Clinical Ophthalmology, Vol Volume 17, Pp 2665-2686 (2023)
Ahmet Kaan Gündüz,1,2,* Diğdem Tetik1,* 1Department of Ophthalmology, Ankara University Faculty of Medicine, Ankara, Turkey; 2Private Eye Clinic, Farilya Business Center 8/38, Ankara, 06510, Turkey*These authors contributed equal
Externí odkaz:
https://doaj.org/article/2f65c67c830d4b74a6669d2838cf7515
Autor:
Mao, Ruiqing, Wu, Haotian, Jia, Yukuan, Nan, Zhaojun, Sun, Yuxuan, Zhou, Sheng, Gündüz, Deniz, Niu, Zhisheng
Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unable to support feature-level and raw-level collaborative algorithms due
Externí odkaz:
http://arxiv.org/abs/2409.19592
Semantic- and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task
Externí odkaz:
http://arxiv.org/abs/2409.17557
Autor:
Gündüz, Ahmet, Kim, Yunsu, Yuksel, Kamer Ali, Al-Badrashiny, Mohamed, Ferreira, Thiago Castro, Sawaf, Hassan
We present AutoMode-ASR, a novel framework that effectively integrates multiple ASR systems to enhance the overall transcription quality while optimizing cost. The idea is to train a decision model to select the optimal ASR system for each segment ba
Externí odkaz:
http://arxiv.org/abs/2409.12476
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with
Externí odkaz:
http://arxiv.org/abs/2409.07850
Session-based recommendation systems aim to model users' interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in session-based recommendations (SBR
Externí odkaz:
http://arxiv.org/abs/2408.05051
Autor:
Turan, Nurettin, Böck, Benedikt, Fesl, Benedikt, Joham, Michael, Gündüz, Deniz, Utschick, Wolfgang
In this work, we propose a Gaussian mixture model (GMM)-based pilot design scheme for downlink (DL) channel estimation in single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. In an initial offline phas
Externí odkaz:
http://arxiv.org/abs/2408.03756
The modelling of memristive devices is an essential part of the development of novel in-memory computing systems. Models are needed to enable the accurate and efficient simulation of memristor device characteristics, for purposes of testing the perfo
Externí odkaz:
http://arxiv.org/abs/2408.01539
Channel simulation involves generating a sample $Y$ from the conditional distribution $P_{Y|X}$, where $X$ is a remote realization sampled from $P_X$. This paper introduces a novel approach to approximate Gaussian channel simulation using dithered qu
Externí odkaz:
http://arxiv.org/abs/2407.12970
Motivated by communication systems with constrained complexity, we consider the problem of input symbol selection for discrete memoryless channels (DMCs). Given a DMC, the goal is to find a subset of its input alphabet, so that the optimal input dist
Externí odkaz:
http://arxiv.org/abs/2407.01263