Download Intelligent sensor networks : the integration of sensor by Fei Hu, Qi Hao PDF
By Fei Hu, Qi Hao
''In the decade, instant or stressed sensor networks have attracted a lot cognizance. even though, so much designs aim common sensor community matters together with protocol stack (routing, MAC, etc.) and safeguard concerns. This booklet specializes in the shut integration of sensing, networking, and clever sign processing through desktop studying. in line with their world-class study, the authors current the basics of intelligent sensor networks. They hide sensing and sampling, dispensed sign processing, and clever sign studying. furthermore, they current state-of-the-art study effects from top experts''-- Read more...
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Additional resources for Intelligent sensor networks : the integration of sensor networks, signal processing and machine learning
Detecting time-related changes in wireless sensor networks using symbol compression and probabilistic suﬃx trees. IEEE International Conference on Intelligent Robots and Systems (IROS ‘10), Taipei, 2010, pp. 2946–2951. , K. D. Wong, Y. H. Hu, and A. M. Sayeed. Detection, classiﬁcation, and tracking of targets. IEEE Signal Processing Magazine 19, March 2001: 17–29. Lioyd, S. Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), March 1982: 129–137. , J. Healey, M. Philipose, and E.
2009, van Kasteren et al. 2010). HSMMs were also applied to behavior understanding from video streams in a nursing center (Chung and Liu 2008). The proposed approach infers human behaviors through three contexts: spatial, activities, and temporal. HSMM were also used in a mobility tracking application for cellular networks (Mark and Zaidi 2002). The activity recognition accuracy achieved by HSMM is compared to that of HMM (van Kasteren et al. 2010). The authors evaluate the recognition performance of these models using two fully annotated real-world datasets consisting of several weeks of data.
The dynamic Bayes classiﬁer is shown to achieve higher activity recognition accuracy than the static model. A dynamic Bayesian ﬁlter was successfully applied to the simultaneous tracking and activity recognition problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring (Wilson and Atkenson 2005). , the conditional probability distribution of future states of the process depends only on the present state, and not on the events that preceded it.