Multi-scale object-based measurement of arid plant community structure

The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

A divide-and-conquer hole-filling method for handling disocclusion in single-view rendering

Large holes are unavoidably generated in depth image based rendering (DIBR) using a single color image and its associated depth map. Such holes are mainly caused by disocclusion, which occurs around the sharp depth discontinuities in the depth map. We propose a divide-and-conquer hole-filling method which refines the background depth pixels around the sharp depth discontinuities to address the disocclusion problem. Firstly, the disocclusion region is detected according to the degree of depth discontinuity, and the target area is marked as a binary mask. Then, the depth pixels located in the target area are modified by a linear interpolation process, whose pixel values decrease from the foreground depth value to the background depth value. Finally, in order to remove the isolated depth pixels, median filtering is adopted to refine the depth map. In these ways, disocclusion regions in the synthesized view are divided into several small holes after DIBR, and are easily filled by image inpainting. Experimental results demonstrate that the proposed method can effectively improve the quality of the synthesized view subjectively and objectively.     

Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform

Multimedia Tools and Applications - Most of the existing image encryption algorithms based on compressive sensing are too complex to operate. An image compression-encryption scheme with simple...     

Behavioral Analysis of E-commerce Acceptance in Micro-enterprises简

E-commerce present opportunities and challenges for businesses, especially for micro-enterprises. This paper proposed a theoretical framework that in line with the unique characteristics of E-commerce acceptance in micro-enterprises in Chengdu, which is based on the unified theory of acceptance and use of technology （UTAUT） model. The extending UTAUT model includes key constructs of performance expectation, perceived behavioral control and the characteristics of owner-managers.     

小学数学教学模式的实施

随着新课程改革的不断推进,小学数学课堂教学形式异彩纷呈。作为一名小学数学教师,我注重以新课标为指引,积极投身课改实验,逐步摸索出一条适用于小学数学课堂教学的模式。     

无线传感器网络中一种高效IBE方案

无线传感器网络的应用日益广泛但安全性问题越发突出,由于其计算能力、存储能力和通信能力有限,一直被认为不适合使用公钥密码学方案。然而近年来不需要认证中心的IBE算法的研究成果表明,IBE方案非常适合无线传感器网络。本文提出一种高效的IBE方案,并做了算法介绍,同时在无线传感器网络中提出相应的密钥分配、认证、管理以及更新的应用方案。与现有其他算法相比,新方案具有系统参数短、安全性高等特点。     

基于工作任务的“4-5-6”情景化项目驱动教学模式的设计与实践

目前我国高职教育正处于改革的重要阶段,创新课程的教学模式,是高职教育的要求,也是课程建设与职业岗位有效接轨的要求。基于工作任务的"4-5-6"情景化项目驱动教学模式,是以职业能力培养为目标,围绕"岗位工作任务"这一主题,以"4"个阶段来划分能力培养过程,以"5"化教学作为教学设计,以"6"个步骤作为教学实施的教学法。通过在Linux操作系统课程教学中的改革实践表明,该教学模式培养了学生的自我学习能力和创造力,提高了学生的职业能力。     

对pSFLASH扰动公钥密码的一个实际攻击

通过对SFLASH的中心映射进行扰动,最近Wang等人提出了一个新的多变量公钥系统pSFLASH.pSFLASH的设计者认为,扰动后的中心映射可以破坏SFLASH公钥潜在的数学结构,从而抵抗针对SFLASH的差分代数攻击[2-3].然而对于以(T-1,U-1,β,γ)为私钥的任一pSFLASH实例,一定存在一个可逆仿射...     

一种基于码本聚类的低复杂度用户选择算法

现有块对角化(BD)预编码系统用户选择算法较少考虑利用已选用户与剩余用户之间的关系来排除不可选用户.针对这点不足,给出了一种采用码本聚类的低复杂度用户选择算法( CodeGreedy算法).该算法采用弦距离刻画用户信道的相关性,以此为依据将用户划分到不同码本空间中,聚集在同一码本空间的用户信道具有较强的相关性,形成互斥...