Number of co-authors:21
Number of publications with 3 favourite co-authors:Nicholas D. Lane:4Fan Li:2Andrew T. Campbell:2
Feng Zhao's 3 most productive colleagues in number of publications:Gaetano Borriello:37Jie Liu:15Keith I. Farkas:14
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Publications by Feng Zhao (bibliography)
Li, Fan, Zhao, Chunshui, Ding, Guanzhong, Gong, Jian, Liu, Chenxing and Zhao, Feng (2012): A reliable and accurate indoor localization method using phone inertial sensors. In: Proceedings of the 2012 International Conference on Uniquitous Computing 2012. pp. 421-430.
This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.
© All rights reserved Li et al. and/or ACM Press
Chon, Yohan, Lane, Nicholas D., Li, Fan, Cha, Hojung and Zhao, Feng (2012): Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proceedings of the 2012 International Conference on Uniquitous Computing 2012. pp. 481-490.
Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places
© All rights reserved Chon et al. and/or ACM Press
Lane, Nicholas D., Xu, Ye, Lu, Hong, Hu, Shaohan, Choudhury, Tanzeem, Campbell, Andrew T. and Zhao, Feng (2011): Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 355-364.
Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor-data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor-data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.
© All rights reserved Lane et al. and/or ACM Press
Lane, Nicholas D., Choudhury, Tanzeem and Zhao, Feng (2011): Mobile sensing: challenges, opportunities and future directions. In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 637-638.
The emerging field of mobile sensing has engaged computer scientists from a variety of existing communities, such as, mobile systems, machine learning and human computer interaction. Each community approaches the challenges of mobile sensing research with its own unique perspective. The purpose of this workshop is to provide a forum to discuss the state of the art in mobile sensing and promote increased cooperation and interaction among the participating research communities.
© All rights reserved Lane et al. and/or ACM Press
Lane, Nicholas D., Lymberopoulos, Dimitrios, Zhao, Feng and Campbell, Andrew T. (2010): Hapori: context-based local search for mobile phones using community behavioral modeling and similarity. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 109-118.
Local search engines are very popular but limited. We present Hapori, a next-generation local search technology for mobile phones that not only takes into account location in the search query but richer context such as the time, weather and the activity of the user. Hapori also builds behavioral models of users and exploits the similarity between users to tailor search results to personal tastes rather than provide static geo-driven points of interest. We discuss the design, implementation and evaluation of the Hapori framework which combines data mining, information preserving embedding and distance metric learning to address the challenge of creating efficient multidimensional models from context-rich local search logs. Our experimental results using 80,000 queries extracted from search logs show that contextual and behavioral similarity information can improve the relevance of local search results by up to ten times when compared to the results currently provided by commercially available search engine technology.
© All rights reserved Lane et al. and/or their publisher
Nath, Suman, Liu, Jie and Zhao, Feng (2008): SensorMap for Wide-Area Sensor Webs. In IEEE Computer, 40 (7) pp. 90-93.
Borriello, Gaetano, Farkas, Keith I., Reynolds, Franklin and Zhao, Feng (2007): Guest Editors' Introduction: Building a Sensor-Rich World. In IEEE Pervasive Computing, 6 (2) pp. 16-18.
Su, Hongsheng and Zhao, Feng (2006): An Improved PSO-Based Fuzzy Ensemble Classifier for Transformer Fault Diagnosis. In: Yao, Yiyu, Shi, Zhongzhi, Wang, Yingxu and Kinsner, Witold (eds.) Proceedings of the Firth IEEE International Conference on Cognitive Informatics ICCI 2006 July 17-19, 2006, Beijing, China. pp. 589-594.
Wang, Yanping and Zhao, Feng (2005): Electronic commerce project character and risk factor analyses. In: Li, Qi and Liang, Ting-Peng (eds.) Proceedings of the 7th International Conference on Electronic Commerce - ICEC 2005 August 15-17, 2005, Xian, China. pp. 896-899.
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