Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Zhongzhi Yu"'
Autor:
Zhongzhi Yu, Yemin Shi
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract In computer-aided diagnosis (CAD), diagnosing untrained diseases as known categories will cause serious medical accidents, which makes it crucial to distinguish the new class (open set) meanwhile preserving the known classes (closed set) per
Externí odkaz:
https://doaj.org/article/f6a153d71f254db69229c58286db0b91
Autor:
Zhongzhi Yu, Yemin Shi
Publikováno v:
IEEE Access, Vol 10, Pp 4063-4071 (2022)
This paper presents a novel network compression framework, Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version without significant performance
Externí odkaz:
https://doaj.org/article/d607107c553b41d49daca80a0a731f82
Autor:
Haoran You, Yang Zhao, Cheng Wan, Zhongzhi Yu, Yonggan Fu, Jiayi Yuan, Shang Wu, Shunyao Zhang, Yongan Zhang, Chaojian Li, Vivek Boominathan, Ashok Veeraraghavan, Ziyun Li, Yingyan Celine Lin
Publikováno v:
IEEE Micro. :1-9
Autor:
Haoran You, Cheng Wan, Yang Zhao, Zhongzhi Yu, Yonggan Fu, Jiayi Yuan, Shang Wu, Shunyao Zhang, Yongan Zhang, Chaojian Li, Vivek Boominathan, Ashok Veeraraghavan, Ziyun Li, Yingyan Lin
Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and enhanced visual privac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f41f683b18fb587add4b97ddb694b709
http://arxiv.org/abs/2206.00877
http://arxiv.org/abs/2206.00877
Publikováno v:
DAC
Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing demand to deploy DRL agents to empower daily-life intelligent devices, whil
Autor:
Yongyuan Liang, Zhangyang Wang, Yonggan Fu, Yingyan Lin, Zhongzhi Yu, Yongan Zhang, Mingchao Jiang, Chaojian Li, Yifan Jiang
Publikováno v:
DAC
The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::047599caf91ea0cd73c6c77693a19304
The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::075791b5b356b2781aacf7dcd10979d9
Publikováno v:
Journal of Modern Optics. 65:1188-1198
Parallel detection, which can use the additional information of a pinhole plane image taken at every excitation scan position, could be an efficient method to enhance the resolution of a confocal l...
Publikováno v:
Optics Communications. 404:139-146
A novel approach to achieve the image restoration is proposed in which each detector’s relative position in the detector array is no longer a necessity. We can identify each detector’s relative location by extracting a certain area from one of th
Autor:
Di Jiang, Yun Li, Lu Chang, Jian-Jun He, Zhongzhi Yu, Mingyu Li, Xiangjiang Liu, Dalin Tian, Longhua Tang
Publikováno v:
RSC Advances. 7:5063-5066
We report a new simple synthesis method for a DNA-anchored SERS nanoprobe encoded with a Raman reporter by a one-pot overgrowth process, allowing the nanostructure to be controllably tuned to provide improved Raman enhancement, and simultaneously int