Junqing Zhang

Junqing Zhang

A passionate wireless security researcher at University of Liverpool

Recommended Reading List

Published:

  1. Guolin Yin, Junqing Zhang*, Guanxiong Shen, and Yingying Chen, “FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning”, IEEE Transactions on Mobile Computing, accepted arXiv link,[IEEE], [code]

    This paper used few shot-Learning for wifi sensing. The code is online available.

  2. Zhang, Jie, et al. “CrossSense: Towards cross-site and large-scale WiFi sensing.” Proceedings of the 24th annual international conference on mobile computing and networking. 2018. [ACM]

    CrossSense: Introduces a system enhancing WiFi sensing in new environments by using machine learning for synthetic training sample generation and a mixture-of-experts approach. It significantly boosts accuracy in applications like gait and gesture recognition​​.

  3. Sameera Palipana, David Rojas, Piyush Agrawal, and Dirk Pesch. 2018. FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 155 (December 2017), 25 pages.[ACM][Code]

    FallDeFi: Focuses on fall detection among the elderly using WiFi CSI. It employs time-frequency analysis and feature selection for high-accuracy fall detection, showing substantial improvements over previous methods, especially in changing environments​​.

  4. Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using WiFi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018).[Code][ACM]

    SignFi: Proposes using WiFi CSI and a CNN for sign language gesture recognition. Unlike previous methods, SignFi can recognize a broad range of gestures with high accuracy in different settings, representing a significant advancement in WiFi-based gesture recognition​​.

  5. W. Wang, A. X. Liu, M. Shahzad, K. Ling and S. Lu, “Device-Free Human Activity Recognition Using Commercial WiFi Devices,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1118-1131, May 2017, doi: 10.1109/JSAC.2017.2679658.[IEEE]

    CARM: Develops a CSI-based human activity recognition and monitoring system. It introduces a CSI-speed and CSI-activity model to quantitatively correlate CSI dynamics with human activities, achieving high accuracy in various environments​​.

  6. Hong Li, Wei Yang, Jianxin Wang, Yang Xu, and Liusheng Huang. 2016. WiFinger: talk to your smart devices with finger-grained gesture. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ‘16). [ACM]

    WiFinger: Presents a system for number text input in WiFi devices using finger-grained gestures. It captures unique CSI patterns generated by finger movements, achieving high classification accuracy for continuous text input without requiring wearable sensors​​.

  7. Y. Zhang et al., “Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 8671-8688, 1 Nov. 2022, doi: 10.1109/TPAMI.2021.3105387. [IEEE]. [Code and Dataset]

    Widar3.0: Offers a zero-effort cross-domain gesture recognition system using WiFi, focusing on domain-independent features at a lower signal level. It requires only one-time training and adapts to different data domains, significantly outperforming existing solutions in gesture recognition accuracy across various environments​​.

  8. F. Meneghello, D. Garlisi, N. D. Fabbro, I. Tinnirello and M. Rossi, “SHARP: Environment and Person Independent Activity Recognition With Commodity IEEE 802.11 Access Points,” in IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 6160-6175, 1 Oct. 2023, doi: 10.1109/TMC.2022.3185681.[IEEE].[SHARP]

    SHARP: This study presents SHARP, a technique for human activity recognition (HAR) using commercial WiFi devices. SHARP processes the WiFi channel’s frequency response phase to estimate Doppler shifts, identifying movements with high accuracy in varying conditions, environments, and individuals​​.

  9. Yongsen Ma, Gang Zhou, and Shuangquan Wang. 2019. WiFi Sensing with Channel State Information: A Survey. ACM Comput. Surv. 52, 3, Article 46 (May 2020). [ACM]

    WiFi Sensing with CSI: This paper reviews WiFi sensing technologies using Channel State Information (CSI), categorizing applications into detection, recognition, and estimation. Key challenges include robustness, privacy, and coexistence with networking. Future trends involve cross-layer, cross-device, and cross-sensor WiFi sensing​​.

  10. S. Yousefi, H. Narui, S. Dayal, S. Ermon and S. Valaee, “A Survey on Behavior Recognition Using WiFi Channel State Information,” in IEEE Communications Magazine, vol. 55, no. 10, pp. 98-104, Oct. 2017.[IEEE]

    Behavior Recognition using WiFi CSI: This survey discusses human behavior recognition using WiFi CSI. Techniques include histogram-based methods and deep learning approaches, particularly LSTM for feature extraction and activity recognition. Challenges include using CSI phase information, robustness in dynamic environments, and multi-user behavior identification​​.

  11. F. Meneghello, C. Chen, C. Cordeiro and F. Restuccia, “Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks,” in IEEE Communications Magazine, vol. 61, no. 7, pp. 128-133, July 2023 [IEEE]

    Integrated Sensing and Communications in IE: This paper explores the integration of sensing into WiFi networks, focusing on the IEEE 802.11bf Task Group’s efforts. It discusses the impact of communication parameters on sensing performance and outlines main research challenges in the field​​.

  12. Yang, Zheng, Yi Zhang, Guoxuan Chi, and Guidong Zhang. “Hands-on wireless sensing with wi-fi: A tutorial.” arXiv preprint arXiv:2206.09532 (2022).[ARXIV]

    Wireless Sensing with Wi-Fi: This tutorial introduces wireless sensing using Wi-Fi, covering theoretical principles and practical code implementation. It highlights deep learning models for wireless sensing applications in various fields, including health care, smart homes, and security surveillance​​.