A spatio-temporal fusion method for remote sensing data Using a linear injection model and local neighbourhood information

This paper presents a spatio-temporal fusion method for remote sensing images by using a linear injection model and local neighbourhood information. In this method, the linear injection model is first introduced to generate an initial fused image, the spatial details are extracted from the fine-resolution image at the base date, and are weighted by a proper injection gains. Then, the spatial details and the relative spectral information from the coarse-resolution images are blended to generate the fusion result. To further enhance its robustness to the noise, the local neighbourhood information, derived from the fine-resolution image and the fused result simultaneously, is introduced to refine the initial fused image to obtain a more accurate prediction result. The algorithm can effectively capture phenology change or land-cover-type change with minimum input data. Simulated data and two types of real satellite images with seasonal changes and land-cover-type changes are employed to test the performance of the proposed method. Compared with a spatial and temporal adaptive reflectance fusion model (STARFM) and a flexible spatio-temporal fusion algorithm (FSDAF), results show that the proposed approach improves the accuracy of fused images in phenology change area and effectively captures land-cover-type reflectance changes.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Crystallographic characteristics and microwave dielectric properties of Ni-modified MgTa2O6 ceramics

Ni²⁺ modified MgTa₂O₆ ceramics with a trirutile phase and space group P4₂/mnm were obtained. The correlations between crystallographic characteristics and microwave dielectric performance of MgTa₂O₆ ceramics were systematically studied based on the chemistry bond theory (PVL theory) for the first time. The results indicate that the introduction of Ni²⁺ causes a change in polarizability and the Mg–O bond ionicity, which contributes to the variation of dielectric constant. Moreover, the lattice energy, and packing fraction, full width at half maximum of the Raman peak of Ta–O bond, as the quantitative characterization of crystallographic parameters, regulate the dielectric loss of MgTa₂O₆ ceramics in GHz frequency band. In addition, the study of sintering behavior shows that the densification and micromorphology are the crucial factors affecting the microwave dielectric performance. Typically, Ni²⁺ doping on the A-site of MgTa₂O₆ can effectively promote the Q × f values to 173,000 GHz (at 7.43 GHz), which ensures its applicability in 5G communication technology.     

发挥设备专业化优势探索项目建设设备综合管理标准化工作

设备综合管理工作始终贯穿在项目建设整个过程中.从项目立项审查、基础设计审查、设备订货前的技术把关,到工程施工环节的专业技术检查,再到项目中交、联动试车、投料试车,处处可见设备综合管理的内容.本文梳理出设备综合管理的主要节点内容、现状和存在的问题,针对镇海基地项目群的建设,就如何发挥设备专业化的优势、如何对设备综合管理工作进行创新、如何优化工作流程进行了探索.旨在通过设备综合管理工作的统筹和协调,将专业化优势形成合力,更高质量、更高效率地为项目建设服务.     

Investigation of crystal characteristics,Raman spectra,and microwave dielectric properties of Mg1-xZnxTa2O6 ceramics

Mg_(1-x)Zn_xTa₂O₆ (x = 0.00?0.08) dielectric ceramics were synthesized via the traditional solid-state reaction method. We used XRD and Rietveld refinement to demonstrate that a pure Mg_(1-x)Zn_xTa₂O₆ phase with trirutile structure was formed. Zn²⁺ substitution helped to decrease the Raman full width at half width of the A_1g mode at 703 cm^?1, which resulted in an increase in the order and rigidity of the TaO₆ octahedron, this in turn contributed to improving the Q×f values. Additionally, the introduction of Zn²⁺ significantly promoted grain growth and increased the dense, and the molecular polarizability, these factors lead to a higher permittivity. Moreover, enhanced Ta-O bond energy resulted in a more stable TaO₆ octahedron in the Mg_(1?x)Zn_xTa₂O₆ system, which contributed to enhanced τ_f values via substitution of Zn²⁺ doped on the A-site. Correspondingly, the microwave dielectric properties were significantly improved for 0.04-doped samples, obtaining: ε_r = 27, Q × f = 185,000 GHz (at 7.47 GHz), τ_f =32 ppm/°C.     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.