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1,991 results on '"Zhang, Kuo"'

5. Flexible Coherent Optical Access: Architectures, Algorithms, and Demonstrations

Author

Zhou, Ji, Xing, Zhenping, Wang, Haide, Zhang, Kuo, Chen, Xi, Feng, Qiguang, Zheng, Keshuang, Zhao, Yijia, Dong, Zhen, Gui, Tao, Ye, Zhicheng, and Li, Liangchuan

Subjects
Computer Science - Networking and Internet Architecture
Abstract

To cope with the explosive bandwidth demand, significant progress has been made in the ITU-T standardization sector to define a higher-speed passive optical network (PON) with a 50Gb/s line rate. Recently, 50G PON becomes mature gradually, which means it is time to discuss beyond 50G PON. For ensuring an acceptable optical power budget, beyond 50G PON will potentially use coherent technologies, which can simultaneously promote the applications of flexible multiple access such as time/frequency-domain multiple access (TFDMA). In this paper, we will introduce the architectures, algorithms, and demonstrations for TFDMA-based coherent PON. The system architectures based on an ultra-simple coherent transceiver and specific signal spectra are designed to greatly reduce the cost of ONUs. Meanwhile, fast and low-complexity digital signal processing (DSP) algorithms are proposed for dealing with upstream and downstream signals. Based on the architectures and algorithms, we experimentally demonstrate the first real-time TFDMA-based coherent PON, which can support at most 256 end users, and peak line rates of 100Gb/s and 200Gb/s in the upstream and downstream scenarios, respectively. In conclusion, the proposed technologies for the coherent PON make it more possible to be applied in the future beyond 50G PON., Comment: The paper has been submitted to the Journal of Lightwave Technology

Published
2023

14. Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction

Author

Liu, Puze, Zhang, Kuo, Tateo, Davide, Jauhri, Snehal, Hu, Zhiyuan, Peters, Jan, and Chalvatzaki, Georgia

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinforcement learning, safety is even more fundamental for exploring an environment without causing any damage. While there are many proposed solutions to the safe exploration problem, only a few of them can deal with the complexity of the real world. This paper introduces a new formulation of safe exploration for reinforcement learning of various robotic tasks. Our approach applies to a wide class of robotic platforms and enforces safety even under complex collision constraints learned from data by exploring the tangent space of the constraint manifold. Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment. We show safe real-world deployment of our learned controller on a TIAGo++ robot, achieving remarkable performance in manipulation and human-robot interaction tasks., Comment: 6 pages

Published
2022

15. Regularized Deep Signed Distance Fields for Reactive Motion Generation

Author

Liu, Puze, Zhang, Kuo, Tateo, Davide, Jauhri, Snehal, Peters, Jan, and Chalvatzaki, Georgia

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which are computationally expensive and hinder their application in dynamic obstacle avoidance use-cases. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale, with fine-grained resolution over high-dimensional manifolds and articulated bodies like humans, thanks to our effective data generation and a simple inductive bias during training. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces. Finally, we provide proof of concept of a real-world application in a HRI handover task with a mobile manipulator robot., Comment: Accepted at 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

Published
2022

38. The application of large language models in medicine: A scoping review

Author

Meng, Xiangbin, Yan, Xiangyu, Zhang, Kuo, Liu, Da, Cui, Xiaojuan, Yang, Yaodong, Zhang, Muhan, Cao, Chunxia, Wang, Jingjia, Wang, Xuliang, Gao, Jun, Wang, Yuan-Geng-Shuo, Ji, Jia-ming, Qiu, Zifeng, Li, Muzi, Qian, Cheng, Guo, Tianze, Ma, Shuangquan, Wang, Zeying, Guo, Zexuan, Lei, Youlan, Shao, Chunli, Wang, Wenyao, Fan, Haojun, and Tang, Yi-Da

Published
2024
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42. Molecular Imaging for Early-Stage Disease Diagnosis

Author

Zhang, Kuo, Xu, Haiyan, Li, Kai, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, and Liu, Zhe, editor

Published
2023
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46. Analysis and modeling of the influence of gate leakage current on threshold voltage and subthreshold swing in p-GaN gate AlGaN/GaN high electron mobility transistors.

Author

Liu, Kai, Wang, Chong, Zhang, Kuo, Ma, Xiaohua, Bai, Junchun, Zheng, Xuefeng, Li, Ang, and Hao, Yue

Subjects
THRESHOLD voltage, MODULATION-doped field-effect transistors, STRAY currents, GALLIUM nitride, METALWORK, FIELD-effect transistors, SCHOTTKY barrier
Abstract

In this paper, the p-GaN gate AlGaN/GaN high electron mobility transistors (HEMTs) with varying combinations of gate metal work function and gate geometry are fabricated and investigate the influence of gate leakage current (IGS) on the threshold voltage (VTH) and subthreshold swing (SS). The unique dependence of VTH and SS on gate geometry for different metals is observed, which is different from traditional field-effect transistors. A novel hybrid physics model, consisting of the traditional capacitance divider model and hole injection model, is proposed to explain this phenomenon, and the results exhibit an excellent agreement with the experimental data. The holes traverse the gate/p-GaN Schottky barrier by thermal emission or tunneling and inject into the p-GaN layer, generating the IGS. Expanding upon the traditional capacitance divider model, a portion of the injected holes accumulate at the p-GaN/AlGaN interface and induce the corresponding electrons at the AlGaN/GaN heterojunction, which promotes channel conduction. Hence, the transfer curves display the correlation between IGS and VTH as well as SS. The results show that high IGS can alleviate the instability of VTH caused by the lithographic overlay error, and simultaneously optimize SS. This work offers a novel perspective for examining the turn-on mechanism of p-GaN HEMTs, thereby contributing to device design. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Rapid discovery of self-assembling peptides with one-bead one-compound peptide library.

Author

Yang, Pei-Pei, Li, Yi-Jing, Cao, Yan, Zhang, Lu, Wang, Jia-Qi, Lai, Ziwei, Zhang, Kuo, Shorty, Diedra, Xiao, Wenwu, Cao, Hui, Wang, Lei, Wang, Hao, Liu, Ruiwu, and Lam, Kit S

Subjects
Hela Cells, Humans, Peptides, Peptide Library, Microscopy, Electron, Transmission, X-Ray Diffraction, Spectroscopy, Fourier Transform Infrared, Circular Dichroism, Combinatorial Chemistry Techniques, Hydrogen Bonding, Nanostructures, Nanofibers, High-Throughput Screening Assays, HeLa Cells
Abstract

Self-assembling peptides have shown tremendous potential in the fields of material sciences, nanoscience, and medicine. Because of the vast combinatorial space of even short peptides, identification of self-assembling sequences remains a challenge. Herein, we develop an experimental method to rapidly screen a huge array of peptide sequences for self-assembling property, using the one-bead one-compound (OBOC) combinatorial library method. In this approach, peptides on beads are N-terminally capped with nitro-1,2,3-benzoxadiazole, a hydrophobicity-sensitive fluorescence molecule. Beads displaying self-assembling peptides would fluoresce under aqueous environment. Using this approach, we identify eight pentapeptides, all of which are able to self-assemble into nanoparticles or nanofibers. Some of them are able to interact with and are taken up efficiently by HeLa cells. Intracellular distribution varied among these non-toxic peptidic nanoparticles. This simple screening strategy has enabled rapid identification of self-assembling peptides suitable for the development of nanostructures for various biomedical and material applications.

Published
2021

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