InSAR处理中滤波方法研究 

合成孔径雷达干涉图质量直接影响相位解缠精度,对干涉图进行滤波处理,去除相位图噪声,改善干涉图质量,是InSAR处理中十分重要的步骤,通过探讨方位向与距离向滤波,以及多视滤波、圆周期均值/中值滤波、基于梯度自适应滤波、Goldstein自适应滤波等噪声抑制方法,并利用Envisat ASAR数据对以上滤波方法进行对比验证。     

基于纹理合成算法在恢复污损牌照中的应用

车牌识别系统是现在智能交通系统中信息采集的一种手段,但是由于图像模糊、字符污损、粘连等实际问题导致识别的瓶颈。文中提出了一种基于纹理合成的图像修补算法——二维表面纹理填洞算法。该算法是根据区域生长得到车牌字符污损部分的边界,即算法中的“洞”,再基于纹理片合成与Quilting算法相结合提出了一种二维表面纹理填洞算法,针对“洞”进行图像纹理修补。实验结果表明,该算法对恢复字符的完整性和修复纹理具有很强的自适应能力,极大地提高了后续的车牌字符分割和识别率。     

基于单片机的弓网接触压力检测

随着现代铁路的高速发展,电力机车运行速度不断提高,保障电力机车高速、安全、可靠地运行已成为一大热点.这使得接触网-受电弓之间的接触力成为最重要的检测项目.介绍了弓网接触压力的检测原理,详细阐述了接触压力数据采集装置的硬件和软件相关设计.利用了压力传感器,并通过AD574实现了对压力数据高精度的采集,给出了实验的相关结果.     

智能步态门禁系统实验平台的设计

为探索步态识别技术在实际中的应用.构建了基于步态图像特征进行身份识别的智能门禁系统硬件平台,且开发了相应的门禁系统软件.其技术流程为:首先从摄像机捕获视频输入计算机进行监控,发现后对人体进行检测与跟踪,分割人体轮廓并将其规格化叠加处理获取步态特征图,然后提取包含人体整体模型信息的边界矩参数,以此作为识别参量输入支持向量机(SVM)进行分类识别.为评价步态识别性能,分别对正确识别率(PCR)、错误接受率(FAR)和错误拒绝率(FRR)进行了测试,结果表明该实验平台能较好地时人体目标进行自动监控跟踪、提取步态特征且有效地进行分类识别.     

基于矢量小波的红外弱小目标检测算法研究

针对标量小波用于红外弱小目标检测,检测精度较低的缺点,设计并实现了一种基于矢量小波的红外弱小目标检测算法.算法首先对红外弱小目标图像进行矢量小波分解;然后时水平及垂直方向上不同子带的小渡系数进行去噪和增强处理,并舍弃低频小波系数;最后进行矢量小波重构并进行自适应阈值分割.对算法进行了实验,实验表明算法具有检测精度高和易于实时实现的优点.     

基于神经网络的机器人的逆运动学分析

文章提出了基于BP神经网络算法的机器人逆运动学的求解方法,给出了基于神经网络的机器人逆运动学求解的具体步骤和设计神经网络的相关注意事项.通过时KLD-600六自由度机器人仿真表明该算法简单可靠.     

Tuning the band gap of the CIGS solar buffer layer Cd1-xZnxS (x=0—1) to achieve high efficiency

To evaluate the impact of zinc sulfate (ZnSO4) concentration on the structural properties of the films, Cd1-xZnxS thin films were formed on glass substrates using chemical bath deposition (CBD) in this study. The effect of ZnSO4 precursor concentration on the surface morphology, optical properties, and morphological structure of the Cd1-xZnxS films was investigated. To study the impact of zinc doping content on the performance metrics of Cu(In1-xGax)Se2 (CIGS) cells in the experimental group and to improve the buffer layer thickness, simulations were run using one-dimensional solar cell capacitance simulator (SCAPS-1D) software.     

Structural, electrochemical and cycling properties of Nb5+ doped LiNi0.8Co0.1Mn0.1O2 cathode materials at different calcination temperatures for lithium-ion batteries

LiNi0.8Co0.1Mn0.1O2 cathode material is prepared by sol-gel method and the effects of Nb5+ doping and different calcination temperatures on cathode materials were deeply investigated. Structural and morphological characterizations revealed that the optimal content of 1 mol% Nb5+ can stabilize layered structures, mitigate Ni2+ migration to Li layers, improve lithium diffusion capacity, and reduce lattice expansion/shrinkage while cycling. And calcination temperature at 800 °C can not only ensure good morphology, but also suppress the mixed discharge of lithium and nickel in the internal structure. Electrochemical performance evaluation revealed that Nb5+ doping improves the discharge-specific capacity of the material, which is conducive to ameliorating its rate capability and cycle performance. And the material at 800 °C exhibits the highest discharge specific capacity, the best magnification performance, low polarizability, and the best cycle reversibility.     

Unsupervised model-driven neural network based image denoising for transmission line monitoring

With the expansion of smart grid and Internet of things (IoT) technology, edge computing has a wide variety of applications in these domains. The criteria for real-time monitoring and accuracy are particularly high in the field of online real-time monitoring of electricity lines. Based on edge technology, high-quality real-time monitoring can be performed for transmission lines using image processing techniques. Therefore, we propose an image denoising method, which can learn clean images using a stream-based generative model. The stream model uses a two-stage approach in the network to handle the different training periods of denoising separately. Experimental results show that the proposed method has good denoising performance.     

基于机器学习的超声造影分析综述

超声造影（Contrast-enhanced ultrasound, CEUS）通过外周静脉注入超声造影剂,显著增强来自肿瘤微血管的血流信号,便于临床医生以实时、动态的方式评估肿瘤血管生成、周边浸润等,广泛应用于多器官病变诊断、预后评估和治疗方案规划等方面。近年来,以深度学习为代表的机器学习方法快速发展,为动态超声造影智能分析带来新的机遇。深度学习方法很大程度上拓宽了超声造影临床应用范围,提高了其诊疗效能。但与常规超声影像类似,超声造影仍然存在斑点噪声、呼吸运动干扰和标准化程度低等问题,使得动态灌注时间、空间信息挖掘面临挑战。本文系统性回顾了近年来超声造影智能分析相关工作,涵盖良恶性鉴别、恶性分级、疗效预测和诊疗方案选择等方面应用,总结了当前影像组学及深度学习方法在超声造影分析领域的最新进展,并指出当前研究的局限性和未来发展方向。