Radiofrequency Energy Harvesting for Wireless Sensor Node: Design Guidelines and Current Circuits Performance

Given their omnipresence, electromagnetic energy offers the most attractive and recent energy supply solutions for low consumption power devices. The most targeted application is the wireless Sensor (WS) node, which is indispensable in all computing systems. This work proposes the design guideline for harvesting radiofrequency (RF) energy using the Rectifying Antenna circuit known as rectenna. The rectenna design issues are then developed to introduce new solutions for optimizing the performance of the circuits. Note that the end-to-end efficiency analysis must incorporate both receiving antenna characteristics, rectifying diode parameters, and matching filter components. However, in most studies, only one or at most two of these aspects are treated. We then want to overcome this lack by offering a global view highlighting all the design issues for optimal RF/DC conversion efficiency. The specific case of rectennas based on patch antennas and Schottky diodes, easily integrated into the circuit boards, is considered. The results of this chapter show that although the harvestable energy levels of ambient RF waves are low, some recent designs offer solutions to take advantage of these ambient waves.

Vibration and Noise Control Technology for High-speed Railway

The operation of high-speed railway will cause environmental noise and vibration problems, which will directly affect the sustainable development of railway station's comprehensive development. In solving this problem, the traditional method generally considers the three aspects of sound source, vibration source and propagation path, but often fail to achieve satisfactory control effect. This paper presents a high-speed railway vibration and noise control technology based on linear feedback matching filter. Firstly, the propagation path model of noise in high-speed railway station is constructed and the noise signal is separated. At the same time, linear feedback matching filter technique is used to make in-phase addition to the propagating sound. The linear feedback filter is designed by combining the binary wavelet technique to suppress the noise and vibration. The results show that the proposed technique overcomes the weakness of conventional control model and the noise vibration control is smoother than that of the traditional method.

The application of an optimal transport to a preconditioned data matching function for robust waveform inversion

Full-waveform inversion (FWI) promises a high-resolution model of the earth. It is, however, a highly nonlinear inverse problem; thus, we iteratively update the subsurface model by minimizing a misfit function that measures the difference between the measured and the predicted data. The conventional [Formula: see text]-norm misfit function is widely used because it provides a simple, high-resolution misfit function with a sample-by-sample comparison. However, it is susceptible to local minima if the low-wavenumber components of the initial model are not accurate. Deconvolution of the predicted and measured data offers an extended space comparison, which is more global. The matching filter calculated from the deconvolution has energy focused at zero lag, like an approximated Dirac delta function, when the predicted data matches the measured one. We have introduced a framework for designing misfit functions by measuring the distance between the matching filter and a representation of the Dirac delta function using optimal transport theory. We have used the Wasserstein [Formula: see text] distance, which provides us with the optimal transport between two probability distribution functions. Unlike data, the matching filter can be easily transformed to a probability distribution satisfying the requirement of the optimal transport theory. Though in one form, it admits the conventional normalized penalty applied to the nonzero-lag energy in the matching filter, the proposed misfit function is metric and extracts its form from solid mathematical foundations based on optimal transport theory. Explicitly, we can derive the adaptive waveform inversion (AWI) misfit function based on our framework, and the critical “normalization” for AWI occurs naturally per the requirement of a probability distribution. We use a modified Marmousi model and the BP salt model to verify the features of the proposed method in avoiding cycle skipping. We use the Chevron 2014 FWI benchmark data set to further highlight the effectiveness of the proposed approach.