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Surface characterization of metal nanoparticles is a critical need in nanocatalysis for in-depth understanding of the structure-function relationships. The surface structure of nanoparticles is often different from the subsurface, and it is challenging to separately characterize the surface and the subsurface. In this work, theoretical calculations and extended X-ray absorption fine structure (EXAFS) analysis illustrate that the surface atoms of noble metals (Pt and Pd) are oxidized in the air, while the subsurface atoms are not easily oxidized. Taking advantage of the oxidation properties, we suggest a stepwise reduction-oxidation approach to determine the surface atomic arrangement of noble metal nanoparticles, and confirm the rationality of this approach by identifying the surface structure of typical 2–3 nm Pt and Pd nanoparticles. The reduction-oxidation approach is applied to characterize the surface structure of model Pd-Sb bimetallic catalyst, which illustrates that the surface Pd is well isolated by Sb atoms with short bond distance at 2.70 A, while there are still Pd-Pd bonds in the subsurface. Density functional theory (DFT) calculations and Pd L edge X-ray absorption near edge structure (XANES) indicate that the isolation of surface Pd significantly decreases the adsorption energies of Pd-hydrocarbon, which leads to the high propylene selectivity and turnover frequency Pd-Sb bimetallic catalyst for propane dehydrogenation.

Hydrogen generation through electrochemical water splitting plays a significant role in solving the energy crisis and preventing the rapid depletion of fossil fuels, and requires efficient and robust electrocatalysts for both the hydrogen and oxygen evolution reactions (HER & OER). Transition-metal phosphides with a similar structure to the hydrogenase enzyme are emerging as promising electrocatalysts for the HER, but their catalytic activity for overall water splitting is kinetically retarded. Herein, a facile and effective strategy is provided for fabricating hierarchical porous nanorods composed of ultrathin CoP nanosheets on nickel foam (NF) by direct phosphidation of MOF-derived Co(OH)2 nanosheets. The unique porous and ultrathin nanosheet structure endows the resulting catalyst with abundant exposed active sites and boundary defects, short ion diffusion paths, and improved conductivity, which are favorable for improving its catalytic performance. Consequently, the hierarchical CoP/NF electrode shows highly efficient electrocatalytic activity with long-term durability for the HER under universal-pH conditions. Surprisingly, the CoP/NF also displays superior OER performance in alkaline solutions. Furthermore, the practical application of CoP/NF as a bifunctional catalyst for the overall water splitting reaction yields a low cell voltage of only 1.54 V to reach a current density of 10 mA cm−2, much better than those for many reported bifunctional catalysts.

Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets' areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets' areas according to the coarsely possible targets' areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets' areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.

Conventional radar cross section (RCS) measurements require far-field or compact-antenna-test-range (CATR) conditions, which have strict restrictions and high implementation costs. By contrast, RCS extrapolation methods in the near zone are more convenient and practical, which become the research emphasis in recent years. This paper presents a novel RCS extrapolation method based on the combination of near-field 3D synthetic aperture radar (SAR) technique and planar projective transforms (PPT) algorithm. Firstly, to extract the target's reflectivity distribution, we apply near-field 3D SAR imaging and obtain 3D near-field RCS (NFRCS) images. Secondly, to meet the requirement of RCS measurements, we derive a novel correction factor used to precisely expand 3D NFRCS images. Finally, we extrapolate the plane-wave responses in the azimuth-vertical dimensions, which presents as planar patterns. The proposed method utilizes the complete 3D image-based information to overcome the inherent constraints of classical RCS extrapolation methods on application scenarios, which has the advantages of broad applicability and high flexibility. The detailed derivation and implementation of this method are described in this paper. The simulation and experiment results verify that the proposed method can provide the equivalent CATR result within ±11° azimuth and elevation angles, and it is applicable to precisely measuring the point, surface, and complex scatterers.

As an important applications of synthetic aperture radar (SAR), slow moving target detection is causing more concern from people. Commonly, with the increase of computer computational efficiency and utilization of GPU, convolutional neural network has been becoming an efficient approach for target detection and classification. Here we propose a method using Faster R-CNN to detect the slow moving target in SAR images. When using existing datasets to detect moving targets, the detection accuracy is low due to the small defocusing of slow moving targets. So we use the bidirectional imaging mode to create the dataset. By increasing the displacement, it is more conducive to detect slow moving targets. At the same time, neural network provides a feasible way for target detection in this mode. In order to close to the reality echo, we use FEKO to simulate the target echo and use measured ground data to generate the ground echo. Deep learning combined with the forward and backward beams can detect slow moving target more effectively. The simulation results validate the effectiveness of the proposed method.

It is difficult for multichannel maneuvering synthetic aperture radar (SAR) to achieve ground moving target 2D velocity estimation and refocusing. In this paper, a novel method based on back projection (BP) and velocity SAR (VSAR) is proposed to cope with the issues. First, the static scene is reconstructed by BP to solve the imaging problem of multichannel maneuvering SAR. Then, the static clutter is suppressed, and the range velocity is estimated via VSAR processing. As for azimuth velocity estimation and refocusing, a velocity search method based on velocity-aided BP (VA-BP) and VSAR is proposed to accomplish them. First, each azimuth velocity in the search and the estimated range velocity are used to image the moving target in a small-sized subimage space by VA-BP, i.e., matching the range history and the Doppler phase of the moving target in the image processing. Then, multiple sets of multichannel SAR subimages corresponding to different azimuth velocities are generated, and the clutter of each set of multichannel SAR subimages is also suppressed by VSAR processing. After that, the azimuth velocity is estimated by searching the clutter-suppressed subimage of the first spatial receiving channel in each set of multichannel SAR subimages with the best refocusing quality measured by the minimum entropy. Simulation results show the proposed method can reach high accuracy in moving target 2D velocity estimation and refocusing with the absolute error of 2D velocity estimation smaller than 0.1 m/s.

It’s difficult to detect and estimate parameters for the slow moving target in clutter using conventional methods.To solve the problem, this paper proposes a method based on dual-beam interferometer SAR imaging mode(DBI).The moving target can be detected by the azimuth offset caused by the time delay of the forward-looking and backward-looking beams.The clutter is suppressed by displace phase center antenna (DPCA) after back projection(BP)algorithm.Then the radial velocity of the moving target can be obtained by the interferometric processing technology. Finally, using the azimuth pixel offset in the image to estimate the azimuth velocity of the slow moving target. With this method, the detection and velocity estimation of the slow moving target can be realized after good clutter cancellation. This method is suitable for the slow-moving and the micro-moving target in clutter, which is hardly possible for conventional SAR.Besides it is not limited by the conventional DPCA condition. The effectiveness of the proposed method is validated by the simulation.

The passenger transportation, as an important index to describe the scale of aviation passenger transport, prediction and research, can let us understand the future trend of the aviation passenger transport, according to it, the airline can make corresponding marketing strategy adjustment. Combining with the knowledge of time series let us understand the characteristics of passenger transportation change, the R software is used to fit the data, so as to establish the ARIMA(1,1,8) model to describe the civil aviation passenger transport developing trend in the future and to make reasonable predictions.

High-speed maneuvering synthetic aperture radar (SAR) moves with a high speed and complex trajectory. It's difficult for it to detect the ground slow moving target using the conventional detection method. To solve this problem, a slow moving target detection method via bidirectional imaging mode (BiDi) is proposed in this paper. The influence of the motion parameters on the moving target's imaging position is discussed, and the location relationship of the moving target between the two SAR images acquired via BiDi mode is also analyzed. The along track separation in BiDi mode can make an azimuth offset of the moving target between the two SAR images. On the other hand, the moving target's opposite imaging position offset in azimuth direction will further increase this azimuth offset. While there is no offset in the range direction of the moving target due to it's azimuth velocity. According to the position differences of the moving target in the two SAR images, the ground slow moving target can be detected successfully. Simulation results have verified the effectiveness of the proposed method.

In this paper, an efficient registration algorithm for InSAR large scale image via discrete Fourier transform (DFT) model of the maximum correlation and image quadtree segmentation is proposed. In the scheme, a DFT-based sub-pixel registration model of InSAR complex images is constructed. Then, efficient sub-pixel registration for InSAR large-scale image is achieved by joint quadtree segmentation and DFT-based interpolation registration. Simulation and experimental results are presented to confirm the effectiveness of the proposed algorithm. The results demonstrate that the algorithm not only can achieve sub-pixel registration of InSAR large-scale image, but also has higher computational efficiency compared with the traditional maximum correlation registration method.

SAR deception jamming method is one of the most important jamming techniques by overlapping a group of fake targets into the SAR images, which can greatly reduce the accuracy of the SAR image interpretation. On the other hands, as a kind of active remote sensing technique, SAR system has less diffuse scattering, and the shadow characteristic is more significant than the optic system. In this paper, the shadow characteristics of the true and false targets are discussed via the simulation experiment, and the convolutional neural network(CNN) is applied for SAR deception jamming target recognition based on the shadow feature. Numerical experiments have shown that the CNN method can effectively distinguish the true and false targets correctly through the shadow feature.

Characteristics and problem of building automation system (BAS) existed in the practical engineering application are analyzed, and then the development of BAS is discussed from the perspective of network structure and supplied function service. Aiming at the space distribution feature, this paper presents the network architecture of Ubi-bus fieldbus, and then essential feature, operation mechanism and standardized information model are introduced respectively. Software and hardware are designed and developed in the control platform of variable air volume (VAV) air conditioning system. Based on the network architecture of Ubi-bus, it is potential for the solution of BAS integration in the practice application.

Due to the spectrum of a moving target being almost submerged in ground clutter, it is difficult for high-speed platform synthetic aperture radar to indicate a slowly moving target by traditional methods. To solve this problem, a detection and azimuth velocity estimation method is proposed based on along-track dual-beam synthetic aperture radar mode. Back projection is adopted to focus the forward- and backward-looking-beam images due to abilities such as automatic coregistration and geometric correction. The positions of the moving target in the two images are deduced, and we show that there is an azimuth location offset for the moving target in the forward-and-backward-looking images proportional to its azimuth velocity, which can be used to detect the moving target and estimate its azimuth velocity. Furthermore, a refocusing method is proposed to obtain a more accurate estimation. Simulation results show that the proposed method can detect slowly moving targets, especially those only with an azimuth velocity, which is difficult for many existing methods. In addition, the accuracy of azimuth velocity estimation can reach 0.01 m / s when the signal-to-clutter ratio is greater than 5 dB under simulation conditions.

An innovative interferometry configuration, imaging and digital elevation mode (DEM) generation method via bistatic forward-looking synthetic aperture radar (BiFLInSAR) is addressed in this paper. The analytical relationship expressions between the interferometric phase and the topographic height are derived. In addition, for the space-variant features of BiFLSAR resolution and interferogram, an imaging method for BiFLInSAR focusing and DEM generation based on predicted back-projection algorithm is presented. Simulation results demonstrate the effectiveness of BiFLInSAR configuration and the presented method.