Publications

2025

1. ACM TOSN

   freeDoppler: A Doppler Effect Learning Network for Accurate RF-based Velocity Estimation

   Dawei Yan, Feiyu Han, Shang Gao, and 3 more authors

   ACM Trans. Sen. Netw., Oct 2025

   Just Accepted

   DOI Bib PDF

   @article{freeDoppler,
     author = {Yan, Dawei and Han, Feiyu and Gao, Shang and Shang, Fei and Yang, Panlong and Yan, Yubo},
     title = {freeDoppler: A Doppler Effect Learning Network for Accurate RF-based Velocity Estimation},
     year = {2025},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     issn = {1550-4859},
     url = {https://doi.org/10.1145/3772369},
     doi = {10.1145/3772369},
     note = {Just Accepted},
     journal = {ACM Trans. Sen. Netw.},
     month = oct,
     keywords = {WiFi-based Sensing, Doppler Frequency Shift, Velocity Estimation, Deep Learning Network.},
   }

2. ICPADS

   RainfalLTE: A Zero-Effect Rainfall Sensing System Utilizing Existing LTE Infrastructure

   Pengfei Shi, Fei Shang, and Haohua Du

   In 2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS), Oct 2025

   DOI Bib

   @inproceedings{RainfalLTE,
     author = {Shi, Pengfei and Shang, Fei and Du, Haohua},
     booktitle = {2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)},
     title = {RainfalLTE: A Zero-Effect Rainfall Sensing System Utilizing Existing LTE Infrastructure},
     year = {2025},
     month = oct,
     volume = {},
     number = {},
     pages = {1-10},
     keywords = {Wireless communication;Base stations;Wireless sensor networks;Rain;Interference;Integrated sensing and communication;Feature extraction;Downlink;Hardware;Long Term Evolution;ISAC;rainfall sensing;wireless sensing;LTE},
     doi = {10.1109/ICPADS67057.2025.11322988}
   }

3. SCIS

   The Field-Based Model: A New Perspective on RF-Based Material Sensing

   Shang Fei, Jiang Haocheng, Yang Panlong, and 3 more authors

   Science China Information Sciences, May 2025

   DOI Bib

   @article{shang2024fieldbasedmodelnewperspective,
     author = {Fei, Shang and Haocheng, Jiang and Panlong, Yang and Dawei, Yan and Haohua, Du and Xiang-Yang, Li},
     title = {The Field-Based Model: A New Perspective on RF-Based Material Sensing},
     journal = {Science China Information Sciences},
     year = {2025},
     month = may,
     doi = {https://doi.org/10.1007/s11432-024-4444-8},
     url = {http://www.sciengine.com/publisher/Science China Press/journal/SCIENCE CHINA Information Sciences///10.1007/s11432-024-4444-8},
     publisher = {Springer Science and Business Media LLC},
   }

4. SCIS

   Measuring discrete sensing capability for ISAC via task mutual information

   Fei Shang, Haohua Du, Panlong Yang, and 3 more authors

   SCIENCE CHINA Information Sciences, Apr 2025

   DOI Bib PDF

   @article{shang2025measuringdiscretesensingcapability,
     title = {Measuring discrete sensing capability for ISAC via task mutual information},
     author = {Shang, Fei and Du, Haohua and Yang, Panlong and He, Xin and Wang, Jingjing and Li, Xiang-Yang},
     journal = {SCIENCE CHINA Information Sciences},
     publisher = {Springer Science and Business Media LLC},
     issn = {1869-1919},
     year = {2025},
     volume = {68},
     number = {5},
     month = apr,
     pages = {150308-},
     url = {http://www.sciengine.com/publisher/Science China Press/journal/SCIENCE CHINA Information Sciences/68/5/10.1007/s11432-024-4374-y},
     doi = {https://doi.org/10.1007/s11432-024-4374-y},
   }

5. ICASSP

   GRACED: A Plug-and-Play Solution for Certifiable Graph Classification

   Xiaoyu Liang, Haohua Du, He Lu, and 1 more author

   In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2025

   DOI Bib PDF Poster Slides

   @inproceedings{graced,
     author = {Liang, Xiaoyu and Du, Haohua and Lu, He and Shang, Fei},
     booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     title = {GRACED: A Plug-and-Play Solution for Certifiable Graph Classification},
     year = {2025},
     volume = {},
     number = {},
     pages = {1-5},
     keywords = {Accuracy;Smoothing methods;Purification;Biological system modeling;Noise reduction;Signal processing;Feature extraction;Diffusion models;Robustness;Speech processing;graph classification;adversarial attack;diffusion model},
     doi = {10.1109/ICASSP49660.2025.10889569},
     month = apr
   }

6. IEEE TMC

   Non-Intrusive and Efficient Estimation of Antenna 3-D Orientation for WiFi APs

   Dawei Yan, Panlong Yang, Fei Shang, and 2 more authors

   IEEE Transactions on Mobile Computing, Mar 2025

   Abs DOI Bib PDF

   The effectiveness of WiFi-based localization systems heavily relies on the spatial accuracy of WiFi AP. In real-world scenarios, factors such as AP rotation and irregular antenna tilt contribute significantly to inaccuracies, surpassing the impact of imprecise AP location and antenna separation. In this paper, we propose Anteumbler, a non-invasive, accurate, and efficient system for measuring the orientation of each antenna in physical space. By leveraging the fact that maximum received power occurs when a Tx-Rx antenna pair is perfectly aligned, we build a spatial angle model capable of determining antennas’ orientations without prior knowledge. However, achieving comprehensive coverage across the spatial angle necessitates extensive sampling points. To enhance efficiency, we exploit the orthogonality of antenna directivity and polarization, and adopt an iterative algorithm, thereby reducing the number of sampling points by several orders of magnitude. Additionally, to attain the required antenna orientation accuracy, we mitigate the influence of propagation distance using a dual plane intersection model while filtering out ambient noise. Our real-world experiments, covering six antenna types, two antenna layouts, two antenna separations (λ/2λ/2 and \lambdaλ ), and three AP heights, demonstrate that Anteumbler achieves median errors below \text6^\circ6∘ for both elevation and azimuth angles, and exhibits robustness in NLoS and dynamic environments. Moreover, when integrated into the reverse localization system, Anteumbler deployed over LocAP reduces antenna separation error by 10 \,\mathrmmm10 mm , while for user localization system, its integration over SpotFi reduces user localization error by more than 1 \,\mathrmm1m.

   @article{yan2025anteumbler,
     author = {Yan, Dawei and Yang, Panlong and Shang, Fei and Freris, Nikolaos M. and Yan, Yubo},
     journal = {IEEE Transactions on Mobile Computing},
     title = {Non-Intrusive and Efficient Estimation of Antenna 3-D Orientation for WiFi APs},
     year = {2025},
     volume = {24},
     number = {3},
     pages = {1453-1468},
     keywords = {Antenna measurements;Antennas;Location awareness;Wireless fidelity;Receiving antennas;Accuracy;Antenna arrays;Sensors;Manuals;Dipole antennas;Antenna orientation;polarization matching;WiFi localization},
     doi = {10.1109/TMC.2024.3485228},
     issn = {1558-0660},
     month = mar
   }

7. IEEE TMC

   Pushing the Limits of WiFi-Based Gait Recognition Towards Non-Gait Human Behaviors

   Dawei Yan, Panlong Yang, Fei Shang, and 3 more authors

   IEEE Transactions on Mobile Computing , Feb 2025

   Abs DOI Bib PDF

   WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body posture. This poses a substantial challenge when users engage in non-periodic or discontinuous behaviors (e.g., stopping, starting, or turning mid-walk), which can disrupt the extraction of gait-related features and degrade recognition performance. To address this issue, we propose freeGait, a novel approach designed to mitigate the impact of non-gait behaviors in WiFi-based gait recognition systems. Our solution models this problem as domain adaptation, where we learn domain-independent representations to isolate gait features from behavior-dependent noise. We treat human behaviors with labeled user data as source domains and behaviors without user labels as target domains. However, applying domain adaptation directly is challenging due to the ambiguous classification boundaries in the target domains for WiFi signals. To overcome this, we align the posterior distributions between the source and target domains and constrain the conditional distribution within the target domains to enhance gait classification accuracy. Additionally, we implement a data augmentation module to generate data resembling the labeled data, while supervised learning ensures distinctiveness between users. Our experiments, conducted with 20 participants across 3 different scenarios, demonstrate that freeGait can accurately predict data across 15 domains by labeling only a small subset from 6 source domains, achieving up to a 45% improvement in user classification accuracy compared to existing methods.

   @article{freegait-tmc,
     author = {Yan, Dawei and Yang, Panlong and Shang, Fei and Han, Feiyu and Yan, Yubo and Li, Xiang-Yang},
     journal = { IEEE Transactions on Mobile Computing },
     title = {{ Pushing the Limits of WiFi-Based Gait Recognition Towards Non-Gait Human Behaviors }},
     year = {2025},
     volume = {},
     number = {01},
     issn = {1558-0660},
     pages = {1-17},
     keywords = {Behavioral sciences;Legged locomotion;Gait recognition;Wireless fidelity;Feature extraction;Transceivers;Accuracy;Noise;Working environment noise;Sensors},
     doi = {10.1109/TMC.2025.3540863},
     url = {https://doi.ieeecomputersociety.org/10.1109/TMC.2025.3540863},
     publisher = {IEEE Computer Society},
     address = {Los Alamitos, CA, USA},
     month = feb
   }

8. arXiv

   RINN: One Sample Radio Frequency Imaging based on Physics Informed Neural Network

   Fei Shang, Haohua Du, Dawei Yan, and 2 more authors

   Feb 2025

   arXiv Bib

   @misc{shang2025rinnsampleradiofrequency,
     title = {RINN: One Sample Radio Frequency Imaging based on Physics Informed Neural Network},
     author = {Shang, Fei and Du, Haohua and Yan, Dawei and Yang, Panlong and Li, Xiang-Yang},
     year = {2025},
     mouth = {feb},
     eprint = {2504.15311},
     archiveprefix = {arXiv},
     primaryclass = {eess.IV},
     url = {https://arxiv.org/abs/2504.15311},
   }

9. IEEE IoTJ

   Multi-Modal Device-to-Device Ranging and Joint Localization

   Xiao Li, Kaiwen Guo, Shicheng Zheng, and 4 more authors

   IEEE Internet of Things Journal, Feb 2025

   DOI Bib

   @article{jointlocalization,
     author = {Li, Xiao and Guo, Kaiwen and Zheng, Shicheng and Shang, Fei and He, Chunyu and Du, Haohua and Li, Xiang-Yang},
     journal = {IEEE Internet of Things Journal},
     title = {Multi-Modal Device-to-Device Ranging and Joint Localization},
     year = {2025},
     volume = {},
     number = {},
     pages = {1-1},
     keywords = {Distance measurement;Location awareness;Real-time systems;Sensors;Accuracy;Frequency measurement;Mobile handsets;Meters;Ultrasonic imaging;Optimization;Ranging;localization;wireless sensing;multi-modality;mobile device;smartphone},
     doi = {10.1109/JIOT.2025.3623287},
   }

2024

1. MSN

   WiSR: Sparse Recovery for Wi-Fi Signal via Generative Adversarial Network

   Mingzhu Yang, Yubo Yan, Dawei Yan, and 2 more authors

   In MSN 2024 - The 20th International Conference on Mobility, Sensing and Networking (MSN), Dec 2024

   Bib PDF

   @inproceedings{wisr,
     author = {Yang, Mingzhu and Yan, Yubo and Yan, Dawei and Gao, Shang and Shang, Fei},
     booktitle = {MSN 2024 - The 20th International Conference on Mobility, Sensing and Networking (MSN)},
     title = {WiSR: Sparse Recovery for Wi-Fi Signal via Generative Adversarial Network},
     year = {2024},
     month = dec,
   }

2. MSN

   BAIR: A Fine-Grained Real-Time Multi-Modal Ranging System on Smartphones

   Xiao Li, Feiyu Han, Fei Shang, and 4 more authors

   In MSN 2024 - The 20th International Conference on Mobility, Sensing and Networking (MSN), Dec 2024

   Bib PDF

   @inproceedings{bair,
     author = {Li, Xiao and Han, Feiyu and Shang, Fei and Zheng, Shicheng and He, Chunyu and Du, Haohua and Li, Xiang-Yang},
     booktitle = {MSN 2024 - The 20th International Conference on Mobility, Sensing and Networking (MSN)},
     title = {BAIR: A Fine-Grained Real-Time Multi-Modal Ranging System on Smartphones},
     year = {2024},
     month = dec,
   }

3. JCRD

   Survey on Low Power Sensing of AIoT

   Xiangyang Li, Fei Shang, Yubo Yan, and 5 more authors

   Jisuanji Yanjiu yu Fazhan/Computer Research and Development, Nov 2024

   Artificial intelligence of thing;Digital analog drive;Internet of thing;Large-scales;Low-power consumption;Low-power sensing;Lower-power consumption;Mobile-computing;Sensing;Sensing modalities;

   Abs DOI Bib PDF

   With the deepening integration of human-machine-object fusion, an increasing number of lightweight and large-scale sensing demands are emerging. To meet the deployment needs of multiple scenarios and large scales, low-cost and low-power sensing solutions are becoming increasingly favored. However, there are still some common and specific challenges in the field of low-power sensing that hinder their further development and practical application. Although many excellent reviews have been conducted on a specific sensing modality or application, there is still a lack of work that sorts out the entire field of low-power sensing. In this paper, we summarize recent low-power sensing, introduce three types of sensing modalities including inertial measurement unit (IMU), microphone, and radio frequency signals, summarize their related challenges, and introduce relevant solutions from hardware and software levels. Finally, we introduce the applications of sensing schemes in different scenarios from four aspects: surface sensing, property sensing, physiological sensing, and anti-sensing, along the direction from surface to inside, from object to human body, and from sensing to safety, and summarize several prospects for exploration.

   @article{iotsurvey,
     language = {Chinese},
     copyright = {Compendex},
     title = {Survey on Low Power Sensing of AIoT},
     journal = {Jisuanji Yanjiu yu Fazhan/Computer Research and Development},
     author = {Li, Xiangyang and Shang, Fei and Yan, Yubo and Wang, Shanyue and Han, Feiyu and Chi, Guoxuan and Yang, Zheng and Chen, Xiaojiang},
     volume = {61},
     number = {11},
     year = {2024},
     pages = {2754 - 2775},
     issn = {10001239},
     note = {Artificial intelligence of thing;Digital analog drive;Internet of thing;Large-scales;Low-power consumption;Low-power sensing;Lower-power consumption;Mobile-computing;Sensing;Sensing modalities;},
     url = {http://dx.doi.org/10.7544/issn1000-1239.202440396},
     month = nov,
     doi = {10.7544/issn1000-1239.202440396}
   }

4. RFCom’24

   RF-Monitor: Smart RFID Solution for Industrial Electrical Cabinet Door Monitoring

   Haocheng Jiang, Fei Shang, Yubo Yan, and 3 more authors

   In Proceedings of the First International Workshop on Radio Frequency (RF) Computing, Hangzhou, China, Nov 2024

   Abs DOI Bib PDF

   In industrial settings, detecting electrical cabinet door states is essential for safety and security. This paper introduces a novel RFID-based system that utilizes the multipath environment within electrical cabinets. Changes in RSSI and phase data occur when the door opens or closes, allowing accurate detection of door state transitions. By employing a multi-tag strategy and robust feature construction, our system ensures reliable signal reception and precise detection, even in challenging environments. Extensive long-term experiments demonstrate its accuracy and effectiveness, proving its suitability for practical applications.

   @inproceedings{RF-Monitor,
     author = {Jiang, Haocheng and Shang, Fei and Yan, Yubo and Du, Haohua and Pan, Xianrui and Li, Xiangyang},
     title = {RF-Monitor: Smart RFID Solution for Industrial Electrical Cabinet Door Monitoring},
     year = {2024},
     isbn = {9798400712982},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3698386.3699987},
     doi = {10.1145/3698386.3699987},
     booktitle = {Proceedings of the First International Workshop on Radio Frequency (RF) Computing},
     pages = {20–24},
     numpages = {5},
     keywords = {Door state detection, Industrial electrical cabinet, Multipath environment, RFID, RSSI, phase},
     location = {Hangzhou, China},
     series = {RFCom '24},
     month = nov,
   }

5. SenSys

   Ph.D. Forum: Field Sensing Model, A New Foundation for RF Sensing

   Fei Shang

   In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems, Hangzhou, China, Nov 2024

   Abs DOI Bib PDF

   In recent years, radio frequency (RF) signal-based sensing has garnered significant attention due to its ubiquity, with numerous applications emerging in areas such as target localization, material recognition, and health monitoring. However, current sensing models are often based on ray tracing, which, although computationally convenient, can become severely distorted when the target size is not much larger than the wavelength. Additionally, using signals with smaller wavelengths to mitigate this issue is not always feasible. Noting that RF signals are a form of electromagnetic waves, we have explored the development of field sensing models directly based on Maxwell’s equations. These models can finely characterize phenomena such as diffraction and multiple scattering, thereby enhancing the upper limits of sensing system capabilities. Based on this approach, we have achieved integrated material recognition and imaging of centimeter-scale targets using WiFi signals. This work has been accepted for presentation at Ubicomp 2024.

   @inproceedings{Forum,
     author = {Shang, Fei},
     title = {Ph.D. Forum: Field Sensing Model, A New Foundation for RF Sensing},
     year = {2024},
     isbn = {9798400706974},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3666025.3699668},
     doi = {10.1145/3666025.3699668},
     booktitle = {Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems},
     pages = {920–922},
     numpages = {3},
     keywords = {wireless sensing, radio frequency, field model},
     location = {Hangzhou, China},
     series = {SenSys '24},
     month = nov
   }

6. Ubicomp

   EarSpeech: Exploring In-Ear Occlusion Effect on Earphones for Data-efficient Airborne Speech Enhancement

   Feiyu Han, Panlong Yang, You Zuo, and 3 more authors

   Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Sep 2024

   Abs DOI Bib PDF

   Earphones have become a popular voice input and interaction device. However, airborne speech is susceptible to ambient noise, making it necessary to improve the quality and intelligibility of speech on earphones in noisy conditions. As the dual-microphone structure (i.e., outer and in-ear microphones) has been widely adopted in earphones (especially ANC earphones), we design EarSpeech which exploits in-ear acoustic sensory as the complementary modality to enable airborne speech enhancement. The key idea of EarSpeech is that in-ear speech is less sensitive to ambient noise and exhibits a correlation with airborne speech. However, due to the occlusion effect, in-ear speech has limited bandwidth, making it challenging to directly correlate with full-band airborne speech. Therefore, we exploit the occlusion effect to carry out theoretical modeling and quantitative analysis of this cross-channel correlation and study how to leverage such cross-channel correlation for speech enhancement. Specifically, we design a series of methodologies including data augmentation, deep learning-based fusion, and noise mixture scheme, to improve the generalization, effectiveness, and robustness of EarSpeech, respectively. Lastly, we conduct real-world experiments to evaluate the performance of our system. Specifically, EarSpeech achieves an average improvement ratio of 27.23% and 13.92% in terms of PESQ and STOI, respectively, and significantly improves SI-SDR by 8.91 dB. Benefiting from data augmentation, EarSpeech can achieve comparable performance with a small-scale dataset that is 40 times less than the original dataset. In addition, we validate the generalization of different users, speech content, and language types, respectively, as well as robustness in the real world via comprehensive experiments. The audio demo of EarSpeech is available on https://github.com/EarSpeech/earspeech.github.io/.

   @article{EarSpeech,
     author = {Han, Feiyu and Yang, Panlong and Zuo, You and Shang, Fei and Xu, Fenglei and Li, Xiang-Yang},
     title = {EarSpeech: Exploring In-Ear Occlusion Effect on Earphones for Data-efficient Airborne Speech Enhancement},
     year = {2024},
     issue_date = {September 2024},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {8},
     number = {3},
     url = {https://doi.org/10.1145/3678594},
     doi = {10.1145/3678594},
     journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
     month = sep,
     articleno = {104},
     numpages = {30},
     keywords = {Earphone-based Sensing and Computing, In-ear Acoustic Sensing, Occlusion Effect, Speech Enhancement}
   }

7. IEEE IoTJ

   freeLoc: Wireless-Based Cross-Domain Device-Free Fingerprints Localization to Free User’s Motions

   Dawei Yan, Fei Shang, Panlong Yang, and 3 more authors

   IEEE Internet of Things Journal, Jul 2024

   Abs DOI Bib PDF

   Due to contactless and convenient experiences, WiFi-based device-free fingerprints localization technologies have extensively attracted research attention. However, they are studied based on an assumption that the user is stationary and face a major challenge in the presence of users’ motions. That is, because users’ motions induced CSI WiFi variations results in inconsistent location fingerprints during training and prediction, leading to system ineffective. To solve this problem, in this article, we propose freeLoc, which aims to free users’ motions (even unseen) while maintaining accurate localization. Specifically, we construct a domain adaptation network that defines different users and motions as different domains, and learns domain-independent representations to extract location fingerprints independent of users’ motions. Unfortunately, collecting sufficient amounts of WiFi data is difficult. To reduce the cost of labeling data and ensure the performance of domain adaptation network, we utilize adversarial autoencoder to build a data augmentation module to introduce data diversity. We deploy experiments in a real scenario, and the results show that only by labeling three motions of three users, we can achieve accurate localization (the nearest locations are about one meter away) for a total of 36 domains, including 6 users and 6 motions. Compared to other existing technologies, freeLoc can improve location prediction accuracy by up to 35%.

   @article{freeLoc,
     author = {Yan, Dawei and Shang, Fei and Yang, Panlong and Han, Feiyu and Yan, Yubo and Li, Xiang-Yang},
     journal = {IEEE Internet of Things Journal},
     title = {freeLoc: Wireless-Based Cross-Domain Device-Free Fingerprints Localization to Free User’s Motions},
     year = {2024},
     volume = {11},
     number = {14},
     pages = {25099-25110},
     keywords = {Fingerprint recognition;Location awareness;Wireless fidelity;Labeling;Feature extraction;Internet of Things;Indexes;Data augmentation;device-free localization;domain adaptation;fingerprints inconsistency},
     doi = {10.1109/JIOT.2024.3390748},
     issn = {2327-4662},
     month = jul
   }

8. MobiHoc

   freeGait: Liberalizing Wireless-based Gait Recognition to Mitigate Non-gait Human Behaviors

   Dawei Yan, Panlong Yang, Fei Shang, and 3 more authors

   In Proceedings of the Twenty-Fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Athens, Greece, Jun 2024

   Abs DOI Bib PDF

   Recently, WiFi-based gait recognition technologies have been widely studied. However, most of them work on a strong assumption that users need to walk continuously and periodically under a constant body posture. Thus, a significant challenge arises when users engage in non-periodic or discontinuous behaviors (e.g., stopping and going, turning around during walking). This is because variations of non-gait behaviors interfere with the extraction of gait-related features, resulting in recognition performance degradation. To solve this problem, we propose freeGait, which aims to mitigate the user’s non-gait behaviors of WiFi-based gait recognition system. Specifically, we model this problem as domain adaptation, by learning domain-independent representations to extract behavior-independent gait features. We consider human behaviors with labels of users as source domains, and human behaviors without labels of users as target domains. However, directly applying domain adaptation to our specific problem is challenging, because the classification boundaries of the unknown target domains are unclear for WiFi signals. We align the posterior distributions of the source and target domains, and constrain the conditional distribution of the target domains to optimize the gait classification accuracy. To obtain enough source domains data, we build a data augmentation module to generate data similar to the labeled data, and use supervised learning to make the data different between users. We conduct experiments with 20 people and 3 different scenarios, and the results show that accurate predictions of a total of 15 domains data can be achieved by only collecting and labeling a small amount of data from 6 source domains, and user classification accuracy can be improved by up to 45% compared to other existing techniques.

   @inproceedings{freeGait,
     author = {Yan, Dawei and Yang, Panlong and Shang, Fei and Han, Feiyu and Yan, Yubo and Li, Xiang-Yang},
     title = {freeGait: Liberalizing Wireless-based Gait Recognition to Mitigate Non-gait Human Behaviors},
     year = {2024},
     isbn = {9798400705212},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3641512.3686362},
     doi = {10.1145/3641512.3686362},
     booktitle = {Proceedings of the Twenty-Fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},
     pages = {241–250},
     numpages = {10},
     keywords = {wifi-based sensing, gait recognition, domain adaptation, data augmentation},
     location = {Athens, Greece},
     series = {MobiHoc '24},
     month = jun
   }

9. IWQoS

   Anteumbler: Non-Invasive Antenna Orientation Error Measurement for WiFi APs

   Dawei Yan, Panlong Yang, Fei Shang, and 2 more authors

   In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS), Jun 2024

   Abs DOI Bib PDF

   The performance of WiFi-based localization systems is affected by the spatial accuracy of WiFi AP. Compared with the imprecision of AP location and antenna separation, the imprecision of AP’s or antenna’s orientation is more important in real scenarios, including AP rotation and antenna irregular tilt. In this paper, we propose Anteumbler that non-invasively, accurately and efficiently measures the orientation of each antenna in physical space. Based on the fact that the received power is maximized when a Tx-Rx antenna pair is perfectly aligned, we construct a spatial angle model that can obtain the antennas’ orientations without prior knowledge. However, the sampling points of traversing the spatial angle need to cover the entire space. We use the orthogonality of antenna directivity and polarization and adopt an iterative algorithm to reduce the sampling points by hundreds of times, which greatly improves the efficiency. To achieve the required antenna orientation accuracy, we eliminate the influence of propagation distance using a dual plane intersection model and filter out ambient noise. Our real-world experiments with six antenna types, two antenna layouts and two antenna separations show that Anteumbler achieves median errors below 6 ° for both elevation and azimuth angles, and is robust to NLoS and dynamic environments. Last but not least, for the reverse localization system, we deploy Anteumbler over LocAP and reduce the antenna separation error by 10 mm, while for the user localization system, we deploy Anteumbler over SpotFi and reduce the user localization error by more than 1 m.

   @inproceedings{Anteumbler,
     author = {Yan, Dawei and Yang, Panlong and Shang, Fei and Freris, Nikolaos M. and Yan, Yubo},
     booktitle = {2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)},
     title = {Anteumbler: Non-Invasive Antenna Orientation Error Measurement for WiFi APs},
     year = {2024},
     volume = {},
     number = {},
     pages = {1-10},
     keywords = {Antenna measurements;Location awareness;Semiconductor device measurement;Accuracy;Measurement uncertainty;Receiving antennas;Extraterrestrial measurements},
     doi = {10.1109/IWQoS61813.2024.10682937},
     issn = {2766-8568},
     month = jun
   }

10. IEEE TMC

    Contactless and Fine-Grained Liquid Identification Utilizing Sub-6 GHz Signals

    Fei Shang, Panlong Yang, Yubo Yan, and 1 more author

    IEEE Transactions on Mobile Computing, May 2024

    Abs DOI Bib PDF

    The existing RF-based liquid identification systems usually rely on prior knowledge, such as pre-build database or the material and width information of the vessel. Furthermore, existing methods may not work in scenarios where the height of liquid is smaller than that of antenna. In this paper, we proposes LiqRay^++, a contactless system which can identify liquids in a fine-grained level without prior knowledge. To remove the effect of vessel, we build a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. To eliminate the effect of different height, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents, LiqRay^++ can identify alcohol solutions with a concentration difference of 1% with 92.9% accuracy. Even if the liquid height is about 4 cm, which is fairly lower than that of most antennas’ heights, the accuracy is more than 85%.

    @article{LiqRay-TMC,
      author = {Shang, Fei and Yang, Panlong and Yan, Yubo and Li, Xiang-Yang},
      journal = {IEEE Transactions on Mobile Computing},
      title = {Contactless and Fine-Grained Liquid Identification Utilizing Sub-6 GHz Signals},
      year = {2024},
      volume = {23},
      number = {5},
      pages = {4992-5008},
      keywords = {Liquids;Transmitting antennas;Attenuation;Antennas;Receiving antennas;Electric fields;Electromagnetic scattering;Contactless;fine-grained;liquid identification;wireless sensing},
      doi = {10.1109/TMC.2023.3300356},
      issn = {1558-0660},
      month = may
    }

11. Ubicomp

    LiquImager: Fine-grained Liquid Identification and Container Imaging System with COTS WiFi Devices

    Fei Shang, Panlong Yang, Dawei Yan, and 2 more authors

    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Mar 2024

    Abs DOI Bib PDF Slides

    WiFi has gradually developed into one of the main candidate technologies for ubiquitous sensing. Based on commercial off-the-shelf (COTS) WiFi devices, this paper proposes LiquImager, which can simultaneously identify liquid and image container regardless of container shape and position. Since the container size is close to the wavelength, diffraction makes the effect of the liquid on the signal difficult to approximate with a simple geometric model (such as ray tracking). Based on Maxwell’s equations, we construct an electric field scattering sensing model. Using few measurements provided by COTS WiFi devices, we solve the scattering model to obtain the medium distribution of the sensing domain, which is used for identifing and imaging liquids. To suppress the signal noise, we propose LiqU-Net for image enhancement. For the centimeter-scale container that is randomly placed in an area of 25 cm \texttimes 25 cm, LiquImager can identify the liquid more than 90% accuracy. In terms of container imaging, LiquImager can accurately find the edge of the container for 4 types of containers with a volume less than 500 ml.

    @article{LiquImager,
      author = {Shang, Fei and Yang, Panlong and Yan, Dawei and Zhang, Sijia and Li, Xiang-Yang},
      title = {LiquImager: Fine-grained Liquid Identification and Container Imaging System with COTS WiFi Devices},
      year = {2024},
      issue_date = {March 2024},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      volume = {8},
      number = {1},
      url = {https://doi.org/10.1145/3643509},
      doi = {10.1145/3643509},
      journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
      month = mar,
      articleno = {15},
      numpages = {29},
      keywords = {Complex permittivity, Electromagnetic waves, Liquid identification, Wireless sensing}
    }

12. Ubicomp

    Wi-Painter: Fine-grained Material Identification and Image Delineation Using COTS WiFi Devices

    Dawei Yan, Panlong Yang, Fei Shang, and 2 more authors

    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Jan 2024

    Abs DOI Bib PDF Slides

    WiFi has gradually developed into one of the main candidate technologies for indoor environment sensing. In this paper, we are interested in using COTS WiFi devices to identify material details, including location, material type, and shape, of stationary objects in the surrounding environment, which may open up new opportunities for many applications. Specifically, we present Wi-Painter, a model-driven system that can accurately detects smooth-surfaced material types and their edges using COTS WiFi devices without modification. Different from previous arts for material identification, Wi-Painter subdivides the target into individual 2D pixels, and simultaneously forms a 2D image based on identifying the material type of each pixel. The key idea of Wi-Painter is to exploit the complex permittivity of the object surface which can be estimated by the different reflectivity of signals with different polarization directions. In particular, we construct the multi-incident angle model to characterize the material, using only the power ratios of the vertically and horizontally polarized signals measured at several different incident angles, which avoids the use of inaccurate WiFi signal phases. We implement and evaluate Wi-Painter in the real world, showing an average classification accuracy of 93.4% for different material types including metal, wood, rubber and plastic of different sizes and thicknesses, and across different environments. In addition, Wi-Painter can accurately detect the material type and edge of the word "LOVE" spliced with different materials, with an average size of 60cm \texttimes 80cm, and material edges with different orientations.

    @article{Wi-Painter,
      author = {Yan, Dawei and Yang, Panlong and Shang, Fei and Jiang, Weiwei and Li, Xiang-Yang},
      title = {Wi-Painter: Fine-grained Material Identification and Image Delineation Using COTS WiFi Devices},
      year = {2024},
      issue_date = {December 2023},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      volume = {7},
      number = {4},
      url = {https://doi.org/10.1145/3633809},
      doi = {10.1145/3633809},
      journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
      month = jan,
      articleno = {203},
      numpages = {25},
      keywords = {Complex permittivity, Material identification, Object imaging, Wireless sensing}
    }

2023

1. LAR: a low-power, high-precision mobile phone-based AR system

   Xiaoming Dai, Fei Shang, Tianzhang Xing, and 2 more authors

   Personal and Ubiquitous Computing, Jun 2023

   Bib

   @article{dai2023lar,
     title = {LAR: a low-power, high-precision mobile phone-based AR system},
     author = {Dai, Xiaoming and Shang, Fei and Xing, Tianzhang and Chen, Feng and Liu, Baoying},
     journal = {Personal and Ubiquitous Computing},
     pages = {1--13},
     year = {2023},
     publisher = {Springer},
     month = jun,
   }

2. ACM TOSN

   Tamera: Contactless Commodity Tracking, Material and Shopping Behavior Recognition Using COTS RFIDs

   Fei Shang, Panlong Yang, Jie Xiong, and 2 more authors

   ACM Trans. Sen. Netw., Feb 2023

   Abs DOI Bib PDF

   RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m \texttimes 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.

   @article{Tamera,
     author = {Shang, Fei and Yang, Panlong and Xiong, Jie and Feng, Yuanhao and Li, Xiangyang},
     title = {Tamera: Contactless Commodity Tracking, Material and Shopping Behavior Recognition Using COTS RFIDs},
     year = {2023},
     issue_date = {May 2023},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {19},
     number = {2},
     issn = {1550-4859},
     url = {https://doi.org/10.1145/3563777},
     doi = {10.1145/3563777},
     journal = {ACM Trans. Sen. Netw.},
     month = feb,
     articleno = {43},
     numpages = {24},
     keywords = {shopping behavior, material, sensing, tracking, contactless, RFID}
   }

3. Ubicomp

   PackquID: In-packet Liquid Identification Using RF Signals

   Fei Shang, Panlong Yang, Yubo Yan, and 1 more author

   Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Jan 2023

   Abs DOI Bib PDF

   There are many scenarios where the liquid is occluded by other items (e.g. books in a packet), in which existing RF-based liquid identification methods are generally not suitable. Moreover, status methods are not applicable when the height of the liquid to be tested changes. This paper proposes PackquID, an RF-based in-packet liquid identification system, which can identify liquid without prior knowledge. In dealing with the obstruction of other items and the unknown container, we utilize a dual-antenna model and craft a relative frequency response factor, exploring the diversity of the permittivity in the frequency domain. In tackling the variable liquid height, we extend our model to 3D scope by analyzing the electric field distribution and solving the height effect via spatial-differential model. With 500 pages of printer paper obscured, PackquID can identify 9 common liquids, including Coca-Cola and Pepsi, with an accuracy of over 86% for 4 different packets (canvas bag, paper bag, backpack, and box) and 4 different containers. Nevertheless, PackquID can still identify liquids with an accuracy rate of over 87%, even when the liquid height changes from 4 cm to 12 cm.

   @article{PackquID,
     author = {Shang, Fei and Yang, Panlong and Yan, Yubo and Li, Xiang-Yang},
     title = {PackquID: In-packet Liquid Identification Using RF Signals},
     year = {2023},
     issue_date = {December 2022},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {6},
     number = {4},
     url = {https://doi.org/10.1145/3569469},
     doi = {10.1145/3569469},
     journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
     month = jan,
     articleno = {181},
     numpages = {27},
     keywords = {Complex permittivity, Electromagnetic waves, Liquid identification, Wireless sensing}
   }

2022

1. MobiCom

   LiqRay: non-invasive and fine-grained liquid recognition system

   Fei Shang, Panlong Yang, Yubo Yan, and 1 more author

   In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, Sydney, NSW, Australia, Oct 2022

   Abs DOI Bib PDF Slides

   The existing RF-based liquid identification methods commonly require a training network of liquid or the container information, such as material and width. Moreover, status quo methods are inapplicable when the solution height is lower than that of the antenna, which is generally unknown either. This paper proposes LiqRay, an RF-based solution, retaining non-invasive and fine-grained liquid recognition abilities, thus can recognize unknown solutions without prior knowledge. In dealing with the unknown container material and width, we utilize a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. In tackling the unknown heights of solution and antenna, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents, LiqRay can identify alcohol solutions with a concentration difference of 1% with 94.92% accuracy. Nevertheless, LiqRay can obtain the relative frequency response factor with a relative error of 6.7% without being affected by the height of the solution. Even if it is merely 4 cm, this is fairly lower than that of most antennas’ heights, since the operating frequency is around 2 GHz.

   @inproceedings{liqray,
     author = {Shang, Fei and Yang, Panlong and Yan, Yubo and Li, Xiang-Yang},
     title = {LiqRay: non-invasive and fine-grained liquid recognition system},
     year = {2022},
     month = oct,
     isbn = {9781450391818},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3495243.3560540},
     doi = {10.1145/3495243.3560540},
     booktitle = {Proceedings of the 28th Annual International Conference on Mobile Computing And Networking},
     pages = {296–309},
     numpages = {14},
     keywords = {wireless sensing, material identification, liquid identification, contactless sensing, complex permittivity},
     location = {Sydney, NSW, Australia},
     series = {MobiCom '22},
   }

2021

1. A vibration-based multi-user concurrent communication system with commercial devices

   Tianzhang Xing, Chase Wu, Jie Wang, and 3 more authors

   Computer Networks, Jul 2021

   Bib

   @article{xing2021vibration,
     title = {A vibration-based multi-user concurrent communication system with commercial devices},
     author = {Xing, Tianzhang and Wu, Chase and Wang, Jie and Shang, Fei and Li, Ruilin and Chen, Xiaojiang},
     journal = {Computer Networks},
     volume = {194},
     pages = {108169},
     year = {2021},
     publisher = {Elsevier},
     month = jul,
   }