Ultra wideband (UWB) communications is a promising technology for wireless body area networks (WBAN) due to its very low power emission and robustness against multipath fading characteristics. The use of WBANs in the areas of healthcare and telemedicine is being seriously considered as a way of increasing the quality of medical services and of keeping under control the associated costs. Because the human body has a complex shape and consists of different tissues it is expected that the propagation of electromagnetic signals will have different characteristics than the ones found in other environments, e.g., offices, streets, etc. The contribution of the work described in this paper is to expand the knowledge of the UWB channel, for WBAN applications, in the frequency range of 3-11 GHz under scenarios expected to be found in the medical care field. The experimental measurements are used to develop UWB channel models which can then be applied to the design of efficient communications protocols.Keywords: wireless medical communications; wearable computing; medical wireless sensors; health monitoring.Reference to this paper should be made as follows: Taparugssanagorn, A., Pomalaza-Ráez, C., Isola, A., Tesi, R., Hämäläinen, M. and Iinatti, J. (2011) 'Preliminary UWB channel study for wireless body area networks in medical applications', Int. J. Ultra Wideband Communications and Systems, Vol. 2, No. 1, pp.14-22.
Preliminary UWB channel study for wireless body area networks in medical applications15 Matti Hämäläinen received his MSc, LicTech and DrTech in Electrical Engineering from the
In this paper, we investigate machine learning methods for enabling high performance noncooperative spectrum sensing, for future cognitive radio systems. The fulfillment of sensing requirements is crucial for ensuring an efficient reuse of the scarce spectrum by unlicensed users, without causing harmful interference to primary users. In this work, we propose a deep convolutional neural network-based transfer learning framework for non-cooperative spectrum sensing in TV bands, applicable across various locations, wireless environments and even frequency assignments. Specifically, we design a four-layer convolutional neural network for limiting the computational costs while satisfying the sensing requirements, and apply transfer learning by freezing the first two convolutional layers. The performance of the proposed method is evaluated against benchmarks, based on over 29,000 spectrograms collected in UHF TV band from a recent measurement campaign. The experiments show that thanks to transfer learning, the proposed method is able to detect TV signals with high accuracy despite a significantly reduced amount of data, thereby providing a high adaptability to various locations, environments, and frequencies. Furthermore, the proposed method with transfer learning not only guarantees the sensing requirements but also realizes up to 94% reduction of training time of the network, as well as 20% reduction of the required sensing time, compared to the case without transfer learning.
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