Zobrazeno 81 - 90
of 1 622
pro vyhledávání: '"Phan, Huy"'
Autor:
Banluesombatkul, Nannapas, Ouppaphan, Pichayoot, Leelaarporn, Pitshaporn, Lakhan, Payongkit, Chaitusaney, Busarakum, Jaimchariyatam, Nattapong, Chuangsuwanich, Ekapol, Chen, Wei, Phan, Huy, Dilokthanakul, Nat, Wilaiprasitporn, Theerawit
Publikováno v:
IEEE Journal of Biomedical and Health Informatics (2020)
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficult
Externí odkaz:
http://arxiv.org/abs/2004.04157
This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings. The framework begins with front-end feature extr
Externí odkaz:
http://arxiv.org/abs/2004.04072
Autor:
Chén, Oliver Y., Saraiva, Raúl G., Nagels, Guy, Phan, Huy, Schwantje, Tom, Cao, Hengyi, Gou, Jiangtao, Reinen, Jenna M., Xiong, Bin, Zhi, Bangdong, Wang, Xiaojun, de Vos, Maarten
Since its debut in the 18th century, the P-value has been an important part of hypothesis testing-based scientific discoveries. As the statistical engine accelerates, questions are beginning to be raised, asking to what extent scientific discoveries
Externí odkaz:
http://arxiv.org/abs/2002.07270
In this work, we propose an approach that features deep feature embedding learning and hierarchical classification with triplet loss function for Acoustic Scene Classification (ASC). In the one hand, a deep convolutional neural network is firstly tra
Externí odkaz:
http://arxiv.org/abs/2002.04857
This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-e
Externí odkaz:
http://arxiv.org/abs/2002.04502
In this paper, we present an ambient backscatters-friendly 5G network that creates locations with large power (hot spots) for tags and good reception locations (quiet spots or coherent spots) for readers. The massive multiple input multiple output (M
Externí odkaz:
http://arxiv.org/abs/2002.00884
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into sp
Externí odkaz:
http://arxiv.org/abs/2002.03894
Autor:
Phan, Huy, McLoughlin, Ian V., Pham, Lam, Chén, Oliver Y., Koch, Philipp, De Vos, Maarten, Mertins, Alfred
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In thi
Externí odkaz:
http://arxiv.org/abs/2001.05532
Autor:
Jiafeng Huang, Hyeung-Sik Choi, Dong-Wook Jung, Hyunjoon Cho, Phan Huy Nam Anh, Ruochen Zhang, Jung-Hyeun Park, Chi-Ung Yun
Publikováno v:
Proceedings of Engineering and Technology Innovation (2023)
This study aims to design a new hybrid twin autonomous underwater vehicle (HTAUV) consisting of dual cylinder hulls and analyze its pitching motion. The kinematic model for the HTAUV is established, followed by the execution of hydrodynamic simulatio
Externí odkaz:
https://doaj.org/article/c90990f0826543168c873d787f791e01
Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many AI fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs.
Externí odkaz:
http://arxiv.org/abs/1912.07742