In medical applications, implant devices are used to measure and remotely transmit the human biological signals to off-body devices. To date, providing the implantable medical devices (IMDs) with a constant and perpetual energy source remains an ongoing challenge. Accordingly, a far-field radio-frequency powering, represented by an access point (AP), in conjunction with energy-harvesting capability is deployed in this paper for continuous powering of the IMDs. In this respect, theoretical analysis is used to establish safe powering conditions in order to comply with the safety limits established by the Federal Communications Commission. The feasibility of the wireless power transfer to the IMDs is investigated by deriving the analytical closed-form expressions for outage probability and average harvested energy, both of which are validated with Monte Carlo simulations. The findings of this paper suggest not to exceed a distance of 0.5 m between the AP and the body surface, as the system performance has experienced high outage probability beyond this value, while the minimum allowable distance is 17 cm at a powering frequency of 403 MHz. It is also presented that the AP should be equipped with a minimum transmit power of 0.4 W in order to maintain an outage probability for the energy harvesting to be less than 10 −1 .INDEX TERMS Energy harvesting, implantable medical devices, outage probability, RF wireless power transfer.

Empirical mode decomposition is a nonparametric adaptive tool that decomposes signals into a set of zero-mean modes called intrinsic mode functions (IMFs) that can be used to denoise a signal by selecting the relevant (noise-free) modes. In this paper, the statistical properties of IMFs, produced by a range of signal distributions, are examined. Insight from the statistical analysis is used in a null hypothesis test that validates a best fit model distribution of the IMFs. This test is then used in a de-noising scheme, which is evaluated using different test signals corrupted with white and colored noise. Simulations under different scenarios demonstrate the efficacy of the proposed method as compared to other EMD-based de-noising techniques. Index Terms-Empirical mode decomposition (EMD), intrinsic mode function (IMF), null hypothesis, signal de-noising.1070-9908

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