多源遥感影像学习型特征双向一致性配准

针对多源遥感影像之间非线性辐射和几何畸变的差异严重影响配准质量的问题,本文提出一种具有双向一致性变换适用于多源遥感影像的配准方法。首先,利用微调的ResNet101网络模型提取多源遥感影像学习型特征,在特征匹配阶段,为提高同名特征匹配的可靠性,设计了一种双向一致性特征匹配网络模型;然后,基于小型轻量级网络加权回归变换模型参数,实现多源遥感影像稳健可靠的配准。试验利用Google Earth影像、卫星影像、无人机影像、Google Earth-卫星-无人机混合影像4种不同数据源对本文方法进行测试,并与具有代表性的多种方法进行比较,结果表明本文方法在配准精度、效率、稳健性方面具有优势,基本实现了2像素以内的自动配准精度。

21世纪学习型学校特征

21世纪,我们已经步入以知识为中心的时代。整个社会都发生了巨大的变化,知识和信息正在成为社会的发展的核心力量,学校在社会进步中扮演着越来越重要的角色,它应该成为一个社会知识创造者。基于工业时代所建立的学校有着根深蒂固的稳定性和惰性,学校要想在社会变革中走在最前列推动着整个进程的快速发展,它必须不断的学习,以积极主动的姿态去改革自身,创建出一个全新的学习型学校。

“学习型公民”界说

在营造学习型社会的理论与实践中,必须十分注重对学习型社会的主体——“学习型公民”的研究。通过对传统型公民与学习型公民的比较,揭示学习型公民的内涵,概括学习型公民的5个特征:自主性、生活性、整体性、发展性、创新性。未来学习型公民应具有3个方面的素养。

Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.

Business English Teaching model based on MOOC

This study was based on self-established MOOC business English teaching. Firstly, based on the typical characteristics of MOOC, this paper analyzes MOOC leveraging business English courses and reports the concrete practice MOOC business English teaching mode. Quantitative and qualitative data analysis of learner feedback survey showed MOOC based teaching model suits for business English teaching, MOOc teaching students have been highly recognized and the two give full play to both collaborative IT and foreign language teaching deep integration of hybrid learning potential.

Determining the Best Mathematical Models of Stable Hydraulics Slop for Jajroud River with Analyzing and Comparing the Results by HEC-RAS

This paper deals with an analysis of selected equations used for the determination of a stable longwise slope calculation of torrential rivers of Jajroud in east of Tehran. Irregularity of the gradient, accompanied by heavy bed-load experiencing abrupt changes of the flow as a result of heavy rainfalls of short duration and high intensity, these are typical features impacting the behavior and characteristics of torrential rivers. The determination of the stable bottom slope, when the river bed is kept unpaved but still provides resistance against harmful effects of rapids, becomes an essential objective of the study. Three methods are used to determine the stable slope： the first is based on tangent tension （shear stress theory）, the second observes a （critical） non-scouring cross-sectional velocity （critical mean channel velocities）, and the third applies the bottom layer velocity （the critical bed velocities）. The mathematical hydraulic model HEC-RAS v. 3.1.3 has been used for the verification of the methods in this research study.

A saliency and Gaussian net model for retinal vessel segmentation

Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.