Search Results for author: You Li

Found 20 papers, 3 papers with code

Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

no code implementations2 Jul 2023 Ziyue Li, Yuchen Fang, You Li, Kan Ren, Yansen Wang, Xufang Luo, Juanyong Duan, Congrui Huang, Dongsheng Li, Lili Qiu

A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).

EEG Electroencephalogram (EEG) +1

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models

no code implementations9 Jun 2023 Xingchen Zhou, Ying He, F. Richard Yu, Jianqiang Li, You Li

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world.

Multi-View Graph Representation Learning Beyond Homophily

1 code implementation15 Apr 2023 Bei Lin, You Li, Ning Gui, Zhuopeng Xu, Zhiwu Yu

However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily.

Graph Representation Learning Self-Supervised Learning

MDOE: A Spatiotemporal Event Representation Considering the Magnitude and Density of Events

no code implementations RA-L 2022 Fuqiang Gu, Yong Lee, Yuan Zhuang, You Li, Jingbin Liu, Fangwen Yu, Ruiyuan Li, Chao Chen

Event-based sensors (e. g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e. g., RGB cameras).

Incentivizing Federated Learning

no code implementations22 May 2022 Shuyu Kong, You Li, Hai Zhou

We theoretically prove that clients will use as much data as they can possibly possess to participate in federated learning under certain conditions with our incentive mechanism

Federated Learning

Emergent Visual Sensors for Autonomous Vehicles

no code implementations19 May 2022 You Li, Julien Moreau, Javier Ibanez-Guzman

Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars.

Autonomous Driving object-detection +1

Graph Representation Learning Beyond Node and Homophily

1 code implementation3 Mar 2022 You Li, Bei Lin, Binli Luo, Ning Gui

Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.

Edge Classification Graph Embedding +2

Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration

no code implementations4 Nov 2021 Yiran Luo, You Li, Jin Wang, Naser El-Sheimy

A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused.

OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer

no code implementations7 Sep 2021 Hailiang Tang, Xiaoji Niu, Tisheng Zhang, You Li, Jingnan Liu

The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet.

Fusion of neural networks, for LIDAR-based evidential road mapping

no code implementations5 Feb 2021 Edouard Capellier, Franck Davoine, Veronique Cherfaoui, You Li

So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.

Autonomous Vehicles

Pair-view Unsupervised Graph Representation Learning

no code implementations11 Dec 2020 You Li, Binli Luo, Ning Gui

Low-dimension graph embeddings have proved extremely useful in various downstream tasks in large graphs, e. g., link-related content recommendation and node classification tasks, etc.

Graph Representation Learning Link Prediction +1

KNN-enhanced Deep Learning Against Noisy Labels

no code implementations8 Dec 2020 Shuyu Kong, You Li, Jia Wang, Amin Rezaei, Hai Zhou

Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for label cleanup.

Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

no code implementations10 Aug 2020 Abdelhak Loukkal, Yves GRANDVALET, Tom Drummond, You Li

Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands.

Motion Forecasting Trajectory Planning

Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?

no code implementations13 Jul 2020 You Li, Ruizhi Chen, Xiaoji Niu, Yuan Zhuang, Zhouzheng Gao, Xin Hu, Naser El-Sheimy

Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data.

Motion Estimation

A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network

no code implementations6 Jun 2020 Xin Hu, Zhijun Liu, You Li, Lexi Xu, Sun Zhang, Qinlong Li, Jia Hu, WenHua Chen, Weidong Wang, Mohamed Helaoui, Fadhel M. Ghannouchi

In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier.

Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

no code implementations9 Apr 2020 You Li, Xin Hu, Yuan Zhuang, Zhouzheng Gao, Peng Zhang, Naser El-Sheimy

However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i. e., localization without training data that have known location labels).

reinforcement-learning Reinforcement Learning (RL)

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

no code implementations7 Apr 2020 You Li, Yuan Zhuang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Naser El-Sheimy

Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.

Networking and Internet Architecture Signal Processing

What happens to a ToF LiDAR in fog?

no code implementations14 Mar 2020 You Li, Pierre Duthon, Michèle Colomb, Javier Ibanez-Guzman

This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment.

BIG-bench Machine Learning

Research of Segmented 8bit Voltage-Mode R-2R Ladder DAC

no code implementations19 May 2013 You Li

Random mismatch errors in the resistor networks are one of the dominant nonlinearity sources for high resolution and high accuracy resistor DACs.

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