Dr. Xin Li 李鑫

Dr. Xin Li 李鑫

Research Fellow at NTU

Publications

You can find all my articles on my Google Scholar profile; only a few are listed here.

Data-Driven Optimization Strategy of Microphone Array Configurations in Vehicle Environments

Published in IEEE Transactions on Instrumentation and Measurement, 2024

This study proposes a data-driven optimization strategy for constructing the optimal microphone array configuration in-vehicle environments.

Recommended citation: @article{liu2024data, title={{Data-Driven Optimization Strategy of Microphone Array Configurations in Vehicle Environments}}, author={Liu, Lehai and Bi, Fengrong and Lin, Jiewei and Qi, Tongtong and Li, Xin}, journal={IEEE Transactions on Instrumentation and Measurement}, volume={73}, pages={6506310}, year={2024}, publisher={IEEE} }
Download Paper

Beamforming Made Malicious: Manipulating Wi-Fi Traffic via Beamforming Feedback Forgery

Published in The 30th Annual International Conference On Mobile Computing And Networking, 2024

The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. Code

Recommended citation: @inproceedings{xu2024beamforming, title={{Beamforming Made Malicious: Manipulating Wi-Fi Traffic via Beamforming Feedback Forgery}}, author={Xu, Mingming and He, Yinghui and Li, Xin and Hu, Jingzhi and Chen, Zhe and Xiao, Fu and Luo, Jun}, booktitle={Proc. of the 30th ACM MobiCom}, pages={}, year={2024} }
Download Paper

Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection

Published in IEEE Wireless Communications Letters, 2024

In this letter, we first derive a mathematical model of BFI, characterizing its relationship with CSI explicitly, and then develop a closed-form expression of BFI for 2×2 MIMO systems. To enhance the efficiency of BFI-based sensing by selecting only the most informative features, we quantify the sensing capacity of BFI using the Cramer-Rao bound (CRB) and then propose an efficient CRB-based BFI feature selection algorithm.

Recommended citation: @article{li2024efficient, title={{Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection}}, author={Li, Xin and Hu, Jingzhi and Luo, Jun}, journal={IEEE Wireless Communications Letters}, year={2024}, publisher={IEEE} }
Download Paper

UWB-Fi Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing

Published in The 22nd ACM International Conference on Mobile Systems, Applications, and Services, 2024

We propose UWB-Fi as a novel Wi-Fi sensing system with ultra-wide bandwidth, leveraging only discrete and irregular channel sampling. Code

Recommended citation: @inproceedings{li2024uwb, title={UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing}, author={Li, Xin and Wang, Hongbo and Chen, Zhe and Jiang, Zhiping and Luo, Jun}, booktitle={Proc. of the 22nd ACM MobiSys}, pages={42--55}, year={2024} }
Download Paper

Rotating Machinery Faults Detection Method Based on Deep Echo State Network

Published in Applied Soft Computing, 2022

This paper aims to develop an accurate and efficient end-to-end fault detection model trained by small-scale data for the rotating machinery.

Recommended citation: @article{li2022rotating, title={{Rotating Machinery Faults Detection Method Based on Deep Echo State Network}}, author={Li, Xin and Bi, Fengrong and Zhang, Lipeng and Lin, Jiewei and Bi, Xiaobo and Yang, Xiao}, journal={Applied Soft Computing}, volume={127}, pages={109335}, year={2022}, publisher={Elsevier} }
Download Paper

An Echo State Network With Improved Topology for Time Series Prediction

Published in IEEE Sensors Journal, 2022

An echo state network with improved topology (IESN) is proposed for accurate and efficient time series prediction.

Recommended citation: @article{li2022echo, title={{An Echo State Network with Improved Topology for Time Series Prediction}}, author={Li, Xin and Bi, Fengrong and Yang, Xiao and Bi, Xiaoyang}, journal={IEEE Sensors Journal}, volume={22}, number={6}, pages={5869--5878}, year={2022}, publisher={IEEE} }
Download Paper

Knock Detection Based on the Optimized Variational Mode Decomposition

Published in SAE Technical Paper, 2020

This paper proposed a novel approach for detecting multiple engine faults based on block vibration signals using variational mode decomposition (VMD) and echo state network (ESN).

Recommended citation: @techreport{li2020multiple, title={Multiple Engine Faults Detection Based on Variational Mode Decomposition and Echo State Network}, author={Li, Xin and Bi, Fengrong and Ma, Xiaoqiang and Shen, Pengfei and Cheng, Jiangang}, year={2020}, institution={SAE Technical Paper} }
Download Paper

Knock Detection Based on the Optimized Variational Mode Decomposition

Published in Measurement, 2019

Combined with recursive model and energy difference tracking method, the optimized VMD can decompose signals into several modes based on stop criterion instead of artificial values set in advance.

Recommended citation: @article{bi2019knock, title={{Knock Detection Based on the Optimized Variational Mode Decomposition}}, author={Bi, Fengrong and Li, Xin and Liu, Chunchao and Tian, Congfeng and Ma, Teng and Yang, Xiao}, journal={Measurement}, volume={140}, pages={1--13}, year={2019}, publisher={Elsevier} }
Download Paper