By Essaïd Sabir, Hicham Medromi, Mohamed Sadik
This quantity publishes new developments and findings in sizzling subject matters on the topic of ubiquitous computing/networking. it's the consequence of UNet - ainternational medical occasion that came about onSeptember 08-10, 2015, within the attention-grabbing urban of Casablanca, Morocco. UNet’15 is technically subsidized by way of IEEE Morocco part and IEEE COMSOC Morocco Chapter.
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This quantity publishes new developments and findings in scorching themes regarding ubiquitous computing/networking. it's the final result of UNet - ainternational medical occasion that happened onSeptember 08-10, 2015, within the interesting urban of Casablanca, Morocco. UNet’15 is technically subsidized by way of IEEE Morocco part and IEEE COMSOC Morocco bankruptcy.
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Extra info for Advances in Ubiquitous Networking: Proceedings of the UNet’15
El Mourabit() · A. Sahel · A. Badri · A. com © Springer Science+Business Media Singapore 2016 E. Sabir et al. E. Mourabit et al. calculate the position estimation. As an example of these techniques we find the Global Positioning System GPS, the Time of Arrival TOA and the fingerprint method. Network-based: The network measures all data needed for the handset location and calculates it. The handset is passive in the whole progress, this type is the more suitable for emergency positioning services.
Handouf(B) · E. ma M. ma © Springer Science+Business Media Singapore 2016 E. Sabir et al. 1007/978-981-287-990-5_4 39 40 S. Handouf et al. 1 Literature Review In this section we review some notable works on spectrum leasing and automatic learning, Authors in  look for optimizing opportunistic sensing in cognitive radio when SU has the choice between accessing licensed or unlicensed channels.  proposes a pricing based spectrum leasing framework. In ,  and  cooperative relaying for cognitive radio networks is investigated with a leasing strategy in exchange of SUs’ power.
The correction applied to the weight vector w(n) at iteration n+1 is “normalized” with respect to the squared Euclidian norm of the input vector r(n) at iteration n. We may view the NLMS algorithm as a time-varying step size algorithm , defining the convergence factor μ as μ= α ( ) (8) Enhanced Hybrid Uplink Time Difference of Arrival 21 Where α is the NLMS adaption constant, which optimize the convergence rate of the algorithm and should satisfy the condition 0< α<2, and c is the constant term for normalization and is always less than 1.