Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Monitoring land surface processes with thermal infrared data: Calibration of SVAT parameters based on the optimisation of diurnal surface temperature cycling features

Land Surface Models (LSM) have been designed to describe water and energy transfers at the soil-vegetation-atmosphere interface, and are therefore essential in many environmental disciplines. These numerical models, driven by the boundary conditions in the atmosphere and in the soil, require adequate knowledge of those vegetation and soil characteristics which are determinant in the characterisation of mass and energy transfers. In view of the fact that, firstly this information is often only partially known, and secondly the transfers are sometimes incorrectly represented, these models can rapidly drift and need to be regularly corrected. To this aim, remote sensing is a promising tool and many studies are currently devoted to the development of assimilation techniques to control their inputs or internal variables. The research presented in this paper contributes to this effort. Its ambition is to explore new methodologies, designed to make use of remote sensing thermal infrared data recorded from space. This study is based on the analysis of links between the characteristics of the diurnal cycle of the surface brightness temperature and the soil-atmosphere interface parameters and variables. The proposed methodology takes advantage of these temperatures cycling features, instead of absolute temperature values, to calibrate the LSM. The results show that the model parameters have a significant impact on the diurnal temperature dynamics, sometimes to a greater extent than on the temperature itself, and that these relationships have diurnal and seasonal variations. As a consequence, the use of TIR data for LSM calibration can be optimised by considering only those parts of the information which are really relevant to parameter calibration.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

Interference Shaders of Thin Films

This paper presents a systematic study of light interferences at single thin films. Based on Fresnel's law, we have derived generic expressions of reflectance and transmittance for film interference, as well as specific expressions for free thin films and films coated on transparent or opaque objects. By combining film interference and volume absorption, we have obtained the reflectances and transmittances of film‐coated thin objects such as ribbons. Illumination models for all interference cases of single thin films are presented, and they have been implemented and tested for various structures and materials.     

Determination of optimal size of the de-jitter buffer of a receiving router

The influence of the size of the de-jitter buffer of a receiving router and of the network load on the transmission quality of voice traffic is investigated by means of simulation modeling. The optimal size of the de-jitter buffer that supports an acceptable level of loss of voice packets and a propagation time of the voice signal that corresponds to accepted standards is determined.     

An extension of likelihood-ratio-test for testing linear hypotheses in the baseline-category logit model

A new family of test statistics for testing linear hypotheses in baseline-category logit models is introduced and its asymptotic distribution is obtained. The new family is a natural extension of the classical likelihood ratio test. A simulation study is carried out to find new test statistics that offer an attractive alternative to the classical likelihood ratio test in terms of both exact size and exact power.     

A general topology-based framework for adaptive insertion of cohesive elements in finite element meshes

Large-scale simulation of separation phenomena in solids such as fracture, branching, and fragmentation requires a scalable data structure representation of the evolving model. Modeling of such phenomena can be successfully accomplished by means of cohesive models of fracture, which are versatile and effective tools for computational analysis. A common approach to insert cohesive elements in finite element meshes consists of adding discrete special interfaces (cohesive elements) between bulk elements. The insertion of cohesive elements along bulk element interfaces for fragmentation simulation imposes changes in the topology of the mesh. This paper presents a unified topology-based framework for supporting adaptive fragmentation simulations, being able to handle two- and three-dimensional models, with finite elements of any order. We represent the finite element model using a compact and “complete” topological data structure, which is capable of retrieving all adjacency relationships needed for the simulation. Moreover, we introduce a new topology-based algorithm that systematically classifies fractured facets (i.e., facets along which fracture has occurred). The algorithm follows a set of procedures that consistently perform all the topological changes needed to update the model. The proposed topology-based framework is general and ensures that the model representation remains always valid during fragmentation, even when very complex crack patterns are involved. The framework correctness and efficiency are illustrated by arbitrary insertion of cohesive elements in various finite element meshes of self-similar geometries, including both two- and three-dimensional models. These computational tests clearly show linear scaling in time, which is a key feature of the present data-structure representation. The effectiveness of the proposed approach is also demonstrated by dynamic fracture analysis through finite element simulations of actual engineering problems.

基于DNA计算自组装模型的Diffie-Hellman算法破译(英文)

DNA自组装计算模型是近年来引人关注的计算模型,已有基于自组装模型的二进制加法、乘法以及有限域中的加法和乘法的讨论．文中利用DNA自组装模型设计的模乘系统,实现了素数P的本原根g连续乘方后模p的数的排列,从而可以在线性时间内求解离散对数,为破译Diffie—Hellman密钥交换算法提供了新的生物方法．该模乘系统使用了Θ（p）种自组装类型,组装的时间复杂度为Θ（p-1）．系统最后组装结果提取出报告链后,经过PCR和凝胶电泳读取离散对数结果．该模型扩展了DNA自组装计算模型的应用,为求取离散对数提供了新思路．     

片上多核处理器容软错误执行模型

随着工艺的进步,微处理器将面临越来越严重的软错误威胁.文中提出了两种片上多核处理器容软错误执行模型:双核冗余执行模型DCR和三核冗余执行模型TCR.DCR在两个冗余的内核上以一定的时间间距运行两份相同的线程,store指令只有在进行了结果比较以后才能提交.每个内核增加了硬件实现的现场保存与恢复机制,以实现对软错误的恢复.文中选择的现场保存点有利于隐藏现场保存带来的时间开销,并且采用了特殊的机制保证恢复执行和原始执行过程中load数据的一致性.TCR执行模型通过在3个不同的内核上运行相同的线程实现对软错误的屏蔽.在检测到软错误以后,TCR可以进行动态重构,屏蔽被软错误破坏的内核.实验结果表明,与传统的软错误恢复执行模型CRTR相比,DCR和TCR对核间通信带宽的需求分别降低了57.5%和54.2%.在检测到软错误的情况下,DCR的恢复执行带来5.2%的性能开销,而TCR的重构带来的性能开销为1.3%.错误注入实验表明,DCR能够恢复99.69%的软错误,而TCR实现了对SEU(Single Event Upset)型故障的全面屏蔽.