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In the conventional transmitting coil array for wireless power transmission (WPT), magnetic fields generated through the gaps between coil elements often reduce the overall magnetic fluxes and therefore the power transfer efficiency due to their directions. To overcome the problem, an overlapping of coil elements is applied to develop a WPT coil array system. In this article, the printed spiral coil is used as the resonance unit with low profile, miniaturization, easy to manufacture, and high integration. The overlapping 2 × 2 coil array is able to generate an enhanced magnetic field without dead points. In comparisons with the conventional system, the presented transmitting system provides a larger and more uniform magnetic field distribution with higher wireless power transmission efficiency. Both the simulation and measured efficiency are 70%‐78% at nine different observation points. Both simulation and measurement results demonstrate the effectiveness and efficiency as well as feasibility of the proposed system.

Electromagnetic nondestructive evaluation of underground targets is of great significance for the safety of urban construction. Based on the accurate and efficient simulation of scattering, we can detect the underground targets successfully. As one of the most popular numerical methods in electromagnetics, surface integral equations solved by method of moments (MoM) are used to simulate the scattering from underground targets in this paper. The integral equation is discretized by RWG basis and Galerkin testing. Multilevel fast multipole algorithm (MLFMA) is used to decrease the computation complexity and memory cost. However, the octree used in MLFMA is not applied for rough surfaces and targets together; both the surface and target need to construct octree separately. Since the combination of MLFMA and ACA can build a more efficient method to compute scattering from underground targets, adaptive cross approximation (ACA) is used to compress the impedance matrix instead of MLFMA for the coupling action between the rough surface and target. That is to say that, when calculating the scattering of two targets, target self-interaction is suitable for MLFMA calculation and the coupling between targets is approximated by ACA. Numerical results demonstrate the accuracy and efficiency of our proposed method.

An accelerated algorithm that can efficiently calculate the light scattering of a single metal nanoparticle was proposed. According to the equivalent principle, the method of moment (MoM) transforms the Poggio–Miller–Chang–Harrington–Wu–Tsai (PMCHWT) integral equations into linear algebraic equations, which are solved by the flexible generalized minimal residual solver (FGMRES). Each element of near field MoM impedance matrix was described by Rao–Wilton–Glisson (RWG) basis functions and calculated by double surface integrals. Due to the low-rank property, the adaptive cross approximation (ACA) algorithm based on the octree data structure was applied to compress the MoM impedance matrix of far field action leading to the significant reduction of solution time and memory. Numerical results demonstrated that the proposed method is both accurate and efficient. Compared with the traditional MoM, the ACA algorithm can significantly reduce the impedance matrix filling time and accelerate the scattering field’s computation from actual metal nanoparticles using PMCHWT integral equations.

Small object detection remains a bottleneck because there is little visual information about them, especially in the deep layers. To improve the detection performance of small objects, here, Swin Transformer is introduced as the model backbone network to extract rich features of small objects. Then, a multilevel receptive field expansion network (MRFENet) is proposed based on the characteristics of different stages in the Swin Transformer. Specifically, a receptive field expansion block (RFEB) is designed to acquire contextual cues and extract detailed information. The RFEB is carefully designed to target the required receptive fields of different layers and further refine the features. MRFENet combined with RFEBs implements the retention of small object context cues and the acquisition of receptive fields for the adaptive detection tasks. Finally, a union loss function is designed to enhance the localization ability. Experiments on the MS COCO dataset demonstrate that the proposed MRFENet has a significant improvement against other state-of-theart methods, which further validates that MRFENet can effectively utilize small object information.