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This paper is devoted to the calculation of ice content-dependent, refractive index of mixed ice–water particles in clouds and precipitation from Debye''s, Maxwell Garnet''s and other models and the effects of differently structured particles upon microwave absorption, scattering and backscattering cross-sections. Evidence suggests that while both the real and imaginary parts of the refractive index diminish with increased ice content of such a sphere, the radiation intensity variation differs from one model to another. The results from the commonly used Debye''s algorithm go to an extreme. Mie''s theoretical calculations show that for a fixed sized mixture its absorption cross-section may exceed that of a pure water particle of the same size. At a small physical parameter |2πmr/λ|, the scattering cross-section decreases with increased ice content f, and at a bigger value of the parameter, the cross-section may be greater at a certain f range than that of a pure water sphere of the same volume. The absorption, scattering and backscattering cross-sections of a mixture change with f in an oscillatory manner. However, differing algorithms of the refractive index give different oscillation''s features. Since these algorithms are based on particular assumptions of the physical properties of these mixtures, our conclusions achieved here can be utilized as a reference in the study of microwave transfer and atmospheric remote sensing. [Copyright &y& Elsevier]

This paper considers the problem of reconstructing true targets in a single-channel synthetic aperture radar (SAR) imaging system, which has been disturbed by deceptive jammings. Since the deceptive jammings are usually confined to the main lobe of an SAR antenna, their time-frequency distributions are different from those of the true echoes. This enables us to utilize a dynamic synthetic aperture (DSA) scheme to extract the characteristics of the true and false targets. Dictionaries about the true and false targets are constructed by taking interactions between scatterers into account. Then a sparsity-driven optimization problem is solved to reconstruct the true and false targets separately with super-resolution. Moreover, the deceptively jammed SAR data are divided into different areas to handle various scenarios efficiently, and strategies for DSA selection are addressed as well. Simulations are provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the 5G intelligent edge scenario, more and more accelerator-based single-board computers (SBCs) with low power consumption and high performance are being used as edge devices to run the inferencing part of the artificial intelligence (AI) model to deploy intelligent applications. In this paper, we investigate the inference workflow and performance of the You Only Look Once (YOLO) network, which is the most popular object detection model, in three different accelerator-based SBCs, which are NVIDIA Jetson Nano, NVIDIA Jetson Xavier NX and Raspberry Pi 4B (RPi) with Intel Neural Compute Stick2 (NCS2). Different video contents with different input resize windows are detected and benchmarked by using four different versions of the YOLO model across the above three SBCs. By comparing the inference performance of the three SBCs, the performance of RPi + NCS2 is more friendly to lightweight models. For example, the FPS of detected videos from RPi + NCS2 running YOLOv3-tiny is 7.6 times higher than that of YOLOv3. However, in terms of detection accuracy, we found that in the process of realizing edge intelligence, how to better adapt a AI model to run on RPi + NCS2 is much more complex than the process of Jetson devices. The analysis results indicate that Jetson Nano is a trade-off SBCs in terms of performance and cost; it achieves up to 15 FPSs of detected videos when running YOLOv4-tiny, and this result can be further increased by using TensorRT. [ABSTRACT FROM AUTHOR]