Zobrazeno 1 - 10
of 31 383
pro vyhledávání: '"Junhui, An"'
Autor:
Gill, Sukhpal Singh, Golec, Muhammed, Hu, Jianmin, Xu, Minxian, Du, Junhui, Wu, Huaming, Walia, Guneet Kaur, Murugesan, Subramaniam Subramanian, Ali, Babar, Kumar, Mohit, Ye, Kejiang, Verma, Prabal, Kumar, Surendra, Cuadrado, Felix, Uhlig, Steve
Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements
Externí odkaz:
http://arxiv.org/abs/2407.04053
Autor:
New, Wee Kiat, Wong, Kai-Kit, Xu, Hao, Wang, Chao, Ghadi, Farshad Rostami, Zhang, Jichen, Rao, Junhui, Murch, Ross, Ramírez-Espinosa, Pablo, Morales-Jimenez, David, Chae, Chan-Byoung, Tong, Kin-Fai
The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capab
Externí odkaz:
http://arxiv.org/abs/2407.03449
Autor:
Hu, Jiangbei, Li, Yanggeng, Hou, Fei, Hou, Junhui, Zhang, Zhebin, Wang, Shengfa, Lei, Na, He, Ying
Unsigned distance fields (UDFs) provide a versatile framework for representing a diverse array of 3D shapes, encompassing both watertight and non-watertight geometries. Traditional UDF learning methods typically require extensive training on large da
Externí odkaz:
http://arxiv.org/abs/2407.01330
As network data has become increasingly prevalent, a substantial amount of attention has been paid to the privacy issue in publishing network data. One of the critical challenges for data publishers is to preserve the topological structures of the or
Externí odkaz:
http://arxiv.org/abs/2406.14772
With the development of Multimodal Large Language Models (MLLMs) technology, its general capabilities are increasingly powerful. To evaluate the various abilities of MLLMs, numerous evaluation systems have emerged. But now there is still a lack of a
Externí odkaz:
http://arxiv.org/abs/2406.10057
The audio denoising technique has captured widespread attention in the deep neural network field. Recently, the audio denoising problem has been converted into an image generation task, and deep learning-based approaches have been applied to tackle t
Externí odkaz:
http://arxiv.org/abs/2406.09161
Recent diffusion models have achieved promising performances in audio-denoising tasks. The unique property of the reverse process could recover clean signals. However, the distribution of real-world noises does not comply with a single Gaussian distr
Externí odkaz:
http://arxiv.org/abs/2406.09154
Vocoders reconstruct speech waveforms from acoustic features and play a pivotal role in modern TTS systems. Frequent-domain GAN vocoders like Vocos and APNet2 have recently seen rapid advancements, outperforming time-domain models in inference speed
Externí odkaz:
http://arxiv.org/abs/2406.08196
Autor:
Zhang, Jichen, Rao, Junhui, Ming, Zhaoyang, Li, Zan, Chiu, Chi-Yuk, Wong, Kai-Kit, Tong, Kin-Fai, Murch, Ross
Fluid Antenna Systems (FASs) have recently been proposed for enhancing the performance of wireless communication. Previous antenna designs to meet the requirements of FAS have been based on mechanically movable or liquid antennas and therefore have l
Externí odkaz:
http://arxiv.org/abs/2406.05499
Deep learning-based image compression algorithms typically focus on designing encoding and decoding networks and improving the accuracy of entropy model estimation to enhance the rate-distortion (RD) performance. However, few algorithms leverage the
Externí odkaz:
http://arxiv.org/abs/2406.03961