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Wireless power transfer (WPT) has been widely used in IoT applications, such as mobile device charging, biomedical implants communication, and RFID field. Maximizing the power transfer efficiency (PTE) becomes one of the most crucial problems for designing the WPT systems. Magnetic induction (MI) beamforming has been proposed recently to maximize the PTE for the near field MIMO WPT systems. However, conventional magnetic beamforming in WPT systems usually requires accurate magnetic channel estimation, both amplitude and phase control of the charging source, which can not be achieved in an extreme environment. In this paper, we propose a novel magnetic induction beamforming scheme in MIMO WPT system using a reconfigurable metasurface. Instead of controlling the source currents or voltages, the reconfigurable metasurface can achieve near field beamforming only by varying the capacitor and resistance in specific coil array units. The beamforming is modeled as a discrete optimization problem and solved by using the Simulate Anneal (SA) method. Through the analytical and COMSOL simulation results, our proposed beamforming scheme can achieve approximately two times PTE of the conventional beamforming method in a 40 cm charging distance. 

Water resource has become one of the most precious resources in recent decades. Agriculture accounts for about 80\% of the total water usage in US. There is a demanding need for efficient irrigation and water management systems built for sustainable water utilization in smart agriculture. Real time in-situ soil moisture sensing is a vital part for smart agriculture. Traditional electromagnetic (EM) based soil moisture sensing relies on EM based wireless sensor or ground penetrating radar (GPR) system. Based on the receiving signal strength and delay, tomographic techniques are used to derive the dielectric parameters of the soil, which are then into soil moisture distribution using empirical model. However, the EM signal attenuate sharply during underground propagation because of high operating frequency and lossy medium. In order to counter the disadvantage for underground sensing, we propose a Magnetic Induction (MI) based large range soil moisture sensing scheme in inhomogeneous environments. Here, we present the topology of the sensing system and analyze the channel model. The sensing process is based on transformed model, the conductivity and permittivity distribution are derived using SIRT algorithm. Through COMSOL simulation and analytical results, our proposed soil moisture sensing method achieves a root mean square error (RMSE) of 0.06 m^3/m^3 in 40 m 2D scale inhomogeneous environment range. 

Magnetic induction (MI) communication are widely used in applications in extreme environments, including environment surveillance, past disaster rescue, and resource detection since it does not su↵er from high material absorption in lossy media. However, existing MI systems rely on high transmitting power and large antenna to reach practical communication range. Recently, metamaterial enhanced MI (M2I) communication was proposed, which can increase the signal strength of the original MI system to 30 dB in theory. However the latest practical implementation of M2I system only achieves an 8 dB gain due to the metamaterial loss. In this paper, the active metamaterial unit is introduced to the current M2I communication system to close the performance gap between theoretical and practical results. The antenna system is optimized based on the rigorously model of circuit, coil array structure and channel. Through analytical deduction and COMSOL simulations, the proposed active M2I antenna system shows significant power gain and improvement in communication range compared with the passive M2I system and the original MI system.