Xingmei Li

Dr. Xingmei Li


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
18 February 2021 in Remote Sensing
Verified
Journal Article
Open access
PDF file
XNXiaodong Na
Xiaodong Na
XLXingmei Li
Xingmei Li
Will LeeWill Lee
Will Lee
Changshan WuChangshan Wu
Changshan Wu
In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.

27 December 2022 in Sensors
Verified
Journal Article
Open access
PDF file
MUMahib Ullah
Mahib Ullah
XLXingmei Li
Xingmei Li
MHMuhammad Abul Hassan
Muhammad Abul Hassan
FUFarhat Ullah
Farhat Ullah
Yar MuhammadYar Muhammad
Yar Muhammad
TSTariq Sadad
Tariq Sadad
Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model’s performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.

26 August 2024 in IEEE Communications Letters
Verified
Journal Article
Closed access
HHHongliang He
Hongliang He
NCNengcheng Chen
Nengcheng Chen
DGDi Gao
Di Gao
XLXingmei Li
Xingmei Li
MYMin Yang
Min Yang
This paper investigates the physical-layer security of multi-user networks and proposes a secure and interference-free transmission scheme. The scheme constructs the transmitted signals of different users by using the characteristics of the Hadamard matrix, and injects self-interference and artificial noise that can be eliminated by legitimate users but not by eavesdroppers, thereby significantly improving security. We analyze the asymptotic security performance and obtain the secrecy outage probability when the number of users is large enough. Finally, simulation results exhibit the security performance of the proposed scheme.