Ground Penetrating Radar (GPR) has become indispensable for subsurface reconstruction, especially in complex, multilayered infrastructure environments. Efficient and accurate estimation of permittivity and layer thickness, crucial for understanding these scenes, presents significant challenges. This paper explores the integration and interaction between the back projection (BP) algorithm and full-wave inversion (FWI) to optimize reconstruction outcomes. We propose a ray-based refraction method and a high-order moment-oriented local BP estimation, each aimed at improving efficiency from the critical perspectives of ’delay’ and ’summation’ in the BP algorithm, respectively, with a focus on the target layer. Crucially, the BP estimation results provide a reliable initial model for full-wave inversion, reducing the risk of local minima and decreasing the iteration count for more precise parameter estimation. FWI also compensates for the BP algorithm’s limitations in estimating parameters in non-target layers. Furthermore, full-wave modeling mitigates antenna effects prior to parameter estimation, thereby enhancing accuracy. During reconstruction, the BP algorithm primarily generates target imaging, while FWI provides detailed information about layer interfaces. This synergistic approach leverages the complementary strengths of both algorithms: the BP algorithm captures spatial information depicted by the targets observed in B-scans, while FWI incorporates detailed electromagnetic wave propagation along layer interfaces presented in A-scans via radar equations. Experiments systematically analyze the accuracy and effectiveness of the proposed approach using ideal simulation models, sandbox laboratory data, and road data from the Belgian Road Research Centre facilities. This comprehensive evaluation underscores the approach’s substantial potential in advanced geophysical surveys and environmental research.

With the expansion of the civil UAV (Unmanned Aerial Vehicle) market, UAVs are also increasingly being used in illegal activities such as espionage and snooping on privacy. Therefore, how to effectively control the activities of UAVs in cities has become an urgent problem to be solved. Considering the urban background and the radar performance of communication signals, a low-altitude target detection scheme based on 5G base stations is proposed in this paper. A 5G signal is used as the external radiation source, the method of transceiver separation is adopted, and the forward-scattered waves are used to complete the detection of UAV. This paper mainly analyzes the principle of forward scattering detection in an urban environment, where the forward-scattered wave of a target is stronger than the backward-reflected wave and contains both height difference and midline height information on the target. Based on the above theory, this paper proposes a forward-scattered wave recognition algorithm based on YOLOv3-FCWImageNet, which transforms the forward-scattered wave recognition problem into a target detection problem and accomplishes the recognition of forward-scattered waves by using the excellent performance of algorithms in the field of image recognition. Simulation results show that FCWImageNet can distinguish two different low-altitude targets effectively, and realize the monitoring and classification of UAVs.

Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural features of the original network graph, and uses a generative adversarial model to recursively and backwardly obtain the low-dimensional vector form of the vertices in each layer of the network graph as the initialization of the network graph in the previous layer. It then obtains the low-dimensional vector form of all the vertices in the original network graph for link prediction, and the problem of local minima that can be generated by random initialization is solved. The experimental results show that our method is superior to many state-of-the-art algorithms.

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. Specifically, MGs with surplus power can trade with those with deficit power. This paper presents a two-level hierarchical structure to manage the energy trading among MGs and also with the main grid. In lower level, individual MGs would operate as self-controlled entities; in upper level, an independent and nonprofit third party would coordinate the energy transaction among different MGs. To guarantee the fairness and autonomy, a local energy exchange market is established, in the presence of the main grid. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs’ bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power originally hidden in MGs. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.

This paper proposes a complementary frequency electromagnetic gyroscope. In this gyroscope, a voltage-controlled oscillator (VCO) is applied to generate complementary frequency signals and the angular rate detection is realized based on a phase-locked loop. The complementary frequency electromagnetic gyroscope can be fully realized by micro-electronic technology, which is beneficial to the miniaturization of the angular rate measurement system and has a wide application prospect. The complementary frequency electromagnetic gyroscope extends the application of the Sagnac effect and replaces the optical signal with the electromagnetic pulse signal. It can make full use of the advantages of microelectronic technology and microwave technology to realize the miniaturization of measuring components and even realize the system on a chip. In this paper, the mathematical model of the complementary frequency electromagnetic gyroscope is established and verified by simulation analysis. Theoretical analysis shows that the measured angular rate is proportional to the relative frequency difference of complementary VCO output. The simulation results indicate that the sensitivity of the complementary frequency gyroscope is 1 × 10⁻⁵ rad/s and is insensitive to noise. When the signal-to-noise ratio is lower than 0 dB, the gyroscope still has good angular rate measurement accuracy.