-قطعه‌بندی جریان داده حسگرها در محیط‌های هوشمند فراگیر

 Bingham, E. (2010). Finding segmentations of sequences. In Inductive Databases and Constraint-Based
Data Mining (pp. 177-197). Springer, New York, NY.
 Cho, H., An, J., Hong, I., & Lee, Y. (2015, May). Automatic Sensor Data Stream Segmentation for
Real-time Activity Prediction in Smart Spaces. In Proceedings of the 2015 Workshop on IoT
challenges in Mobile and Industrial Systems (pp. 13-18). ACM.
 Cohen, C. J., Scott, K. A., Huber, M. J., Rowe, S. C., & Morelli, F. (2008, October). Behavior
recognition architecture for surveillance applications. In 2008 37th IEEE Applied Imagery
Pattern Recognition Workshop (pp. 1-8). IEEE.
 Fahad, L. G., Khan, A., & Rajarajan, M. (2015). Activity recognition in smart homes with self
verification of assignments. Neurocomputing, 149, 1286-1298.
 Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., & Duman, H. (2004). Creating
an ambient-intelligence environment using embedded agents. IEEE Intelligent Systems, 19(6),
12-20
 Hong, X., & Nugent, C. D. (2013). Segmenting sensor data for activity monitoring in smart
environments. Personal and ubiquitous computing, 17(3), 545-559.
 Koskimaki, H., Huikari, V., Siirtola, P., Laurinen, P., & Roning, J. (2009, June). Activity recognition
using a wrist-worn inertial measurement unit: A case study for industrial assembly lines. In 2009
17th Mediterranean Conference on Control and Automation (pp. 401-405). IEEE.
 Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and
mobile computing, 10, 138-154.
 Liao, J., Bi, Y., & Nugent, C. (2010, July). Activity recognition for Smart Homes using DempsterShafer

theory of Evidence based on a revised lattice structure. In 2010 Sixth International
Conference on Intelligent Environments (pp. 46-51). IEEE.
 Liao, J., Bi, Y., & Nugent, C. (2011). Using the Dempster–Shafer theory of evidence with a revised
lattice structure for activity recognition. IEEE Transactions on Information Technology in
Biomedicine, 15(1), 74-82
 Lipp, T., & Boyd, S. (2016). Variations and extension of the convex–concave procedure. Optimization
and Engineering, 17(2), 263-287.
 Manning, C., Raghavan, P., & Schütze, H. (2010). Introduction to information retrieval. Natural
Language Engineering, 16(1), 100-103.
 Mendez-Vazquez, A., Helal, A., & Cook, D. (2009, April). Simulating events to generate synthetic data
for pervasive spaces. In Workshop on Developing Shared Home Behavior Datasets to Advance
HCI and Ubiquitous Computing Research. Citeseer.
 Okeyo, G., Chen, L., Wang, H., & Sterritt, R. (2014). Dynamic sensor data segmentation for real-time
knowledge-driven activity recognition. Pervasive and Mobile Computing, 10, 155-172.
 Singla, G., Cook, D. J., & Schmitter-Edgecombe, M. (2010). Recognizing independent and joint
activities among multiple residents in smart environments. Journal of ambient intelligence and
humanized computing, 1(1), 57-63.
 van Kasteren, T. L., Englebienne, G., & Kröse, B. J. (2011). Human activity recognition from wireless
sensor network data: Benchmark and software. In Activity recognition in pervasive intelligent
environments (pp. 165-186). Atlantis Press.
 Wan, J., O'grady, M. J., & O'hare, G. M. (2015). Dynamic sensor event segmentation for real-time
activity recognition in a smart home context. Personal and Ubiquitous Computing, 19(2), 287301.

 Wang, L., Gu, T., Tao, X., Chen, H., & Lu, J. (2011). Recognizing multi-user activities using wearable
sensors in a smart home. Pervasive and Mobile Computing, 7(3), 287-298.
 Weiser, M. (1991). The Computer for the 21 st Century. Scientific american, 265(3), 94-105.
 Yala, N., Fergani, B., & Fleury, A. (2017). Towards improving feature extraction and classification for
activity recognition on streaming data. Journal of Ambient Intelligence and Humanized
Computing, 8(2), 177-189