Carbon fiber reinforced polymers (CFRPs) are composite materials in which carbon provides strength and stiffness, whereas polymers provide cohesiveness and toughness. The electrical impedance of CFRP laminates is changed due to different kinds of damages. Electrical impedance tomography (EIT) has significant advantages such as non-intrusion, portability, low cost, and quick response and has widely been used as a nondestructive testing method. Therefore, EIT has great potential in structural health monitoring of CFRPs. Regularization can solve the ill-posed inverse problem of EIT. However, conventional regularization algorithms have their own limitations, such as over-smoothness of reconstructed edges and unstable solution caused by measurement noise. In addition, the anisotropic property of CFRPs also affects the image quality based on traditional methods. In this paper, the sorted L1-norm regularization is proposed. It promotes grouping highly correlated variables while encouraging sparsity by using more effective penalty terms. The sharp edges between different materials can be obtained, and the obtained solution is more stable. The image quality of different objects, especially the image quality of multi-targets, can be significantly improved with this new method. In addition, the sorted L1 norm can generate adaptive regularization parameters without empirical selection. The new regularization problem is solved by the alternating direction method of multipliers. Both experimental and simulation results demonstrate that the sorted L1 norm improves the quality of reconstructed images under various noise levels. The proposed method is comprehensively evaluated with three image quality criteria by numerical simulation quantitatively.

Consider a millimeter‐wave (mmWave) massive multiple‐input multiple‐output (MIMO) system, where a base station (BS) relying on discrete lens antenna (DLA) array transmits scalable video code (SVC) video to multiple single‐antenna vehicles. Taking into account both the video quality for vehicles and the communication resource consumption of BS, we define a new metric given by the quality of received video to the power consumption ratio (QPR) in order to measure the system efficiency. The objective of this article is to maximize the minimum QPR among vehicles by designing the antenna selection and power allocation scheme, while ensuring the constraints of total transmit power and DLA array structure. Due to the piecewise form of the video quality function, it is intractable to solve the formulated problem, which is therefore decomposed into antenna selection and power allocation subproblems. A maximum equivalent channel gain (MECG) algorithm and an iteration algorithm are proposed for antenna selection subproblem with different complexities, respectively; while an optimal power allocation (OPA) algorithm is designed by analyzing three cases of transmit power constraints. Simulation results indicate that the proposed MECG algorithm has moderate performance with low‐complexity, while the OPA algorithm can help to achieve the optimal QPR in contrast to other algorithms.

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