An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency

Generation and high-resolution imaging of higher-order polarization via metasurface

The generation and focusing properties of higher-order polarized beams have attracted lots of interests due to its significant applications. In this paper,we derived the formula of transforming linear polarization into higher-order polarization, which is applicable to generating arbitrary order polarization. Based on the derived formula, the focusing properties of higher-order polarization by dielectric metasurface lens are studied , which exhibit an Abbe-limit-breaking feature for small numerical aperture, i.e., NA<0.6. When a binary phase (0 & {\pi}) is further imposed on the aperture of metasurface lens, the focusing spot of fourth-order polarization breaks Abbe limit even by 14.3% at NA= 0.6. In addition, the effect of fabrication tolerance, say, substrate thickness and central deviation, on the focusing feature of higher-order polarization is also investigated. Our study may find significant applications in achieving higher-resolution lithography and imaging, say, by just replacing conventional linear or circular polarization with higher-order polarization

Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

Due to the limitations of spatial quantification methods, the spatio-temporal patterns of grassland utilization intensity (GUI) in the Selinco watershed (SLCW), the core region of ecological security on the Qinghai-Tibetan Plateau, is unclear under multiple utilization modes. This paper quantified GUI by constructing the association between the potential and actual Enhanced Vegetation Index (EVI) of grasslands in terms of interannual variability. To obtain an accurate spatio-temporal dataset of potential EVI, the following two components were considered on. Firstly, the temporal lag effects of each raw climate factor were investigated to determine the optimal climate variables affecting vegetation productivity. Secondly, four machine learning (ML) algorithms, including an artificial neural network, random forest, support vector machine, and gradient boosting regression tree combined with the Bayesian model average, were used to construct grassland potential EVI models involving EVI, grassland type, and environmental factors (topography, soil, raw climate, and bioclimatic). Meanwhile, to maximize the performance of ML models, variable selection, variable transformation, and hyperparameter optimization were systematically implemented, where the hyperparameter optimization algorithms employ the grid search algorithm, Bayesian optimization, genetic algorithm, and particle swarm optimization. Then, the spatio-temporal dataset of GUI in the SLCW from 2001 to 2019 was established by using the above quantification method based on multisource remote sensing and artificial intelligence algorithms. The analysis of spatio-temporal variation in GUI showed that the implementation of ecological restoration projects leads to a significant and rapid decline in the overall GUI of the SLCW after 2010 (declining by 4.8%), which is more obvious in the non-nature reserve (declining by 9.3%). In the Qiangtang Nature Reserve within the SLCW, although the GUI shows a declining trend after 2010 because of the implementation of ecological restoration projects, it shows an insignificant increase from 2001 to 2019 due to the recovery increase of wildlife populations in recent decades. Besides, by exploring the effects of elevation and slope on the GUI, it is found that grasslands on higher slopes at lower elevations are at a greater risk of degradation due to more intensive grassland utilization

Nanometer-thin pure B layers Grown by MBE as metal diffusion barrier on GaN Diodes

Pure boron layers, deposited by molecular beam epitaxy (MBE) on AlGaN/GaN/p-Si substrates to a thickness of ~ 7 nm, were applied as barriers to aluminum metallization. For low-temperature deposition from 250°C - 400°C, low-saturation-current diodes to the n-type GaN were fabricated that all tolerated alloying at 400°C. After alloying, the relatively high current level of the 250°C diode was reduced to that of the other low temperature diodes, whereas 700°C B deposition resulted in high-current diode characteristics. The results suggest a favorable B-to-GaN chemistry at 350°C - 400°C