一种基于Ohta-Okamoto签名的门限群签名方案

在修改的Ohta_Okamoto数字签名方案基础上,设计出了一个新的(t,n)门限签名方案。该方案具有可验证性和防止内部成员合谋攻击;而且验证者在验证群签名的有效性计算的复杂性与一般个人签名方案一样。该方案的安全性是基于大整数的因式分解,因此在计算上是安全的。     

Influence of Structural Non-Stationarity of Surface Roughness on Morphological Characterization and Mechanical Deformation of Rock Joints

Summary Structural non-stationarity of surface roughness affects accurate morphological characterization as well as mechanical behaviour of rock joints at the laboratory scale using samples with a size below the stationarity threshold. In this paper, the effect of structural non-stationarity of surface roughness is investigated by studying the scale dependence of surface roughness and mechanical behaviour of rock joints. The results show that the structural non-stationarity mainly affects the accurate characterization of the surface roughness of the fracture samples. It also controls the amount and location of the contact areas during shear tests, which in turn affects the mechanical properties and asperity degradation of the samples. It is concluded that for accurate determination of the morphological and mechanical properties of rock joints at laboratory and field scales, samples with size equal to or larger than the stationarity threshold are required. Author’s address: Nader Fardin, Rock Mechanics Group, Department of Mining Engineering, Faculty of Engineering, University of Tehran, P.O. Box: 11365/4563, Tehran, Iran     

Geoacoustic Inversion via Genetic Algorithm and Its Application to Manganese Sediment Identification

An acoustic inversion method using a wide-band signal and two near field receivers is proposed and applied to multiple layered seabed models including a manganese sediment. The inversion problem can be formulated into a probabilistic model comprised of signals, a forward model, and additive noise. The forward model simulates wide-band signals, such as chirp signals, and is chosen to be the source-waveletconvolution plane wave modeling method. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the possible numerical ranges for a priori uniform distribution is based. The genetic algorithm is applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L 2 norm of the difference between measured and modeled signals. Not only the marginal pdf but also its statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm.     

Otolith shape as a stock discrimination tool for ling (Genypterus blacodes) in the fjords of Chilean Patagonia

ABSTRACT

Genypterus blacodes, in terms of its fishing history and local economic importance, is an emblematic species harvested in Chilean Patagonia (41°00’–57°00’S). Most of the current fisheries and biological knowledge of this species come from the open ocean, whereas information about the species in fjords and inner channels is fragmentary. In 2018, two research surveys targeting G. blacodes were conducted in the fjords and inner channels of Chilean Patagonia. A total of 253 pairs of sagittal otoliths were sampled at three different localities, and their contours were modelled using wavelet analysis as a tool for stock discrimination. Contours were compared using canonical analysis, and classification was performed using linear discriminant and Random Forest analyses. The results indicated that the wavelet method is efficient in modelling otolith contours, and the discriminant analyses showed differences among fishing grounds across the latitudinal gradient, thus confirming the hypothesis that G. blacodes conform to at least two separate stock units in Chilean Patagonia. Fishing grounds that were closer in space showed higher levels of misclassification. The discussion focuses on how environmental variables and the geography of fjords shape stock differences and how this information can be used for the sustainable management of G. blacodes.     

Comments on the Gravity and Magnetic Anomalies of Saros Bay using Wavelet Approach

In this paper, residual and regional gravity and magnetic anomalies of Saros Bay are separated using wavelet method. Wavelet is one of the modern, stochastic image approaches processing technique in electronics. The vertical, horizontal and diagonal components of wavelet output are evaluated simultaneously and an underground model is obtained by suitable cross-sections. Thus the geological and tectonic properties of Saros Bay are extracted. Our proposed model is confirmed by deep and shallow seismic researches of Turkish Petroleum Cooperation (TPAO). The South-East region of Saros Graben is formed by strike slip Ganos Fault and Anafartalar reverse Fault that lies on the east of Ganos Fault. On the Northwest region, there is strike slip component fault resulting in the Enez Graben. Here we detected two new oval type geological structure, both of them starting from the Eastern direction of Canakkale Dardanelles and but one of them continuing through Gelibolu Peninsuls, while the other tending to the Enez Graben. We think that these structures are the ruins of a very old sea in this region. This revised version was published online in November 2006 with corrections to the Cover Date.     

改进的自适应遗传算法在遥感图像分割中的应用

为了自动确定遥感图像分割的最佳阈值,本文提出了一种改进的自适应遗传算法,并利用该算法对二维Otsu图像阈值分割函数进行了全局优化,提高了分割闻值的求解速度。该算法能够根据个体适应度大小和群体的分散程度自动调整遗传控制参数,从而能够在保持群体多样性的同时加快收敛速度,克服基本遗传算法的收敛性差、易早熟问题。实验结果表明,该算法具有良好的收敛速度和稳定性,达到较好的图像分割效果,大大缩短了计算时间。     

小波理论在遥感图像融合中应用

小波变换用于多源遥感图像融合已得到了国内外学者的广泛关注,并且又提出了很多小波变换与传统融合方法结合的融合算法。本文在对这些算法进行总结的基础上,讨论了基于小波包变换和最优树变换以及多进制小波变换在融合中的应用,分析他们在融合中各自的特色,以便使用时根据具体情况进行选择。     

基于小波变换的全色和多光谱遥感图像融合

基于多分辨率小波变换,针对高分辨率全色图像和多光谱图像的融合,提出了一种基于方向可调滤波器的区域能量测量的小波变换融合算法.将此方法与HIS和传统小波的融合方法比较,结果表明,该方法在保留多光谱图像光谱信息的同时,能够更好地保留高分辨率图像的空间信息,融合效果有较大改善.     

经验模式分解的尺度阈值滤波在变形分析中的应用

本文将信号处理领域的经验模式分解算法应用于变形信息的提取中,通过引入阈值函数,建立了基于经验模式分解的尺度阈值滤波模型,采用优化模型确定了经验模式分解的次数。分别通过模拟试验和实测数据与小波阈值法和多项式拟合法进行了比对,分析表明:在低噪声情况下,三种方法都有一定的滤波效果;在高噪声情况下,经验模式分解的尺度阈值滤波法具有与小波阈值去噪法相等的精度,而且瞬时强噪声识别能力更好,优于多项式拟合法。