Trade-off between sequential and time warp-based parallelsimulation

Discrete event simulation is a methodology to study the behavior of complex systems. Its drawback is that, in order to get reliable results, simulations usually have to be run over a long stretch of time. This time requirement could decrease through the usage of parallel or distributed computing systems. In this paper, we analyze the Time Warp synchronization protocol for parallel discrete event simulation and present an analytical model evaluating the upper bound on the completion time of a Time Warp simulation. In our analysis, we consider the case of a simulation model with homogeneous logical processes, where “homogeneous” means they have the same average event routine time and the same state saving cost. Then we propose a methodology to determine when it is time-convenient to use a Time Warp synchronized simulation, instead of a sequential one, for a simulation model with features matching those considered in our analysis. We give an answer to this question without the need to preliminary generate the simulation code. Examples of methodology usage are reported for the case of both a synthetic benchmark and a real world model     

Trade-off between noise sensitivity and robustness for LQG regulators

The redesign of a nominal, ‘optimal’ state-space design to improve robustness properties is described. The procedure is limited to open-loop stable plants, and we treat only single-input-single-output systems. The redesign amounts to the construction of a γ-filter. The effects of the γ-filter on noise performance and improved robustness properties turn out to be fairly explicit.     

无线传感器网络MAC层能耗与时延的权衡

无线传感器网络节点设计的最重要约束之一就是要求低能耗,这与传统网络依赖持续的能源供应而致力于提供尽可能高的服务质量截然不同.研究了一个结合无线传感器网络的能耗特性而提出的MAC协议S-MAC协议,并在对802.11MAC、S-MAC中的能耗状态与时延特征进行理论和数学分析的基础上,通过仿真实验得出了在模拟网络环境中802.11MAC和S-MAC的能耗和平均时延,S-MAC协议中引入节点周期性睡眠、冲突串音避免和消息传递等新技术,以牺牲时延为代价换取高效的能量利用,提供了一种高效可扩展的在能耗和时延之间进行权衡的方案.     

Estimating impervious surfaces from medium spatial resolution imagery: a comparison between fuzzy classification and LSMA

Impervious surfaces are important environmental indicators and are related to many environmental issues, such as water quality, stream health and the urban heat island effect. Therefore, detailed impervious surface information is crucial for urban planning and environment management. To extract impervious surfaces from remote sensing imagery, many algorithms and techniques have been developed. However, there are still debates over the strengths and limitations of linear versus nonlinear algorithms in handling mixed pixels in the urban landscapes. In the meantime, although many previous studies have compared various techniques, few comparisons were made between linear and nonlinear techniques. The objective of this study is to compare the performance between nonlinear and linear methods for impervious surface extraction from medium spatial resolution imagery. A linear spectral mixture analysis (LSMA) and a fuzzy classifier were applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images acquired on 5 April 2004, 16 June 2001 and 3 October 2000, which covered Marion County, Indiana, United States. An aerial photo of Marion County with a spatial resolution of 0.14 m was used for validation of estimation results. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R ²) were calculated to indicate the accuracy of impervious surface maps. The results show that the fuzzy classification outperformed LSMA in impervious surface estimation in all seasons. For the June image, LSMA yielded a result with an RMSE of 13.2%, while the fuzzy classifier yielded an RMSE of 12.4%. For the April image, LSMA yielded an accuracy of 21.1% and the fuzzy classifier yielded 17.0%. For the October image, LSMA yielded a result with an RMSE of 19.8%, but the fuzzy classifier yielded an RMSE of 17.5%. Moreover, a subset image of the commercial, high-density and low-density residential areas was selected in order to compare the effectiveness of the developed algorithms for estimating impervious surfaces of different land use types. The result shows that the fuzzy classification was more effective than LSMA in both high-density and low-density residential areas. These areas prevailed with mixed pixels in the medium resolution imagery, such as ASTER. The results from the tested commercial area had a very high RMSE value due to the prevalence of shade in the area. It is suggested that the fuzzy classifier based on the nonlinear assumption can handle mixed pixels more effectively than LSMA.     

Data fusion for high spatial resolution LAI estimation

Leaf Area Index (LAI) is a critical variable for forest management. It is difficult to obtain accurate LAI estimations of high spatial resolution over large areas. Local estimations can be obtained from in situ field measurements. Extrapolation of local measurements is prone to error. Remote sensing LAI estimation products, such as the one provided by MODIS are of very low resolution and subject to criticism in recent validation works. Forest management requires increasingly high resolution estimations of LAI. We propose a data fusion process for high spatial resolution estimation of the LAI over a large area, combining several heterogeneous information sources: field sampled data, elevation data and remote sensing data. The process makes use of spatial interpolation techniques. We follow a hybrid validation approach that combines the conventional prediction error measures with a spatial validation based on image segmentation. We obtain encouraging results of this information fusion process on data from a forest area in the north of Portugal.     

Auto-registration of medium and high spatial resolution satellite images by integrating improved SIFT and spatial consistency constraints

Feature-based methods have been developed in the past decades for the registration of optical satellite images. However, it is still a challenging problem to handle well the registration between medium and high spatial resolution images due to the large difference of the spatial structural features and local details for the same objects. In this study, an automated co-registration technique is proposed that integrates an improved SIFT (I-SIFT) and a novel matching strategy called spatial consistency constraints (SCC) to cope with the large difference in spatial resolutions between the image pair. Three constraints on angle, distance, and ratio are introduced to re the initial matching features obtained by I-SIFT. Three groups of experiments were conducted to validate the effectiveness of the proposed method. The experiments used high resolution multispectral and panoramic SPOT 5/6 images and Landsat 5/8 orthorectification images. Experimental results show that the registration error lies in about 1 pixel of high-resolution images and demonstrate that the proposed I-SIFT-SCC approach is suitable for fine registration of optical satellite images from medium spatial resolution to high spatial resolution with resolution ratio up to 6.     

Moment-based feature extraction from high spatial resolution hyperspectral images

In this article, we propose a method for extracting spatio-spectral features from high spatial resolution hyperspectral (HS) images. The method is based on extracting two-dimensional moments from neighbourhoods of pixels. Three different types of moments are considered: geometric, complex Zernike and Legendre. Moments of a given type are extracted from a few principal components (PC) of HS data, and are stacked on the original HS data to form a joint spatio-spectral feature space. These features are classified using a support vector machine (SVM) classifier. The influence of the moments orders and the size of the neighbourhood window on the quality of the extracted features are analysed. A few experiments are conducted on two widely used HS data sets, Pavia University and Salinas. The results demonstrate high capabilities of the proposed method in comparison with some state-of-the-art spatio-spectral HS classification methods.     

The role of spatial resolution in quantification problems: spatialization method

Quantification of the surface covered by a parameter distributed at the soil's surface is a current work in remote sensing. The numerical value obtained is always concerned with the resolution of the sensors. We analyse the usual thresholding procedures in the light of new results on spatialization of models. We give the optimal bounds of the error on quantification due to the heterogeneity of the surface distribution and the sensor's resolution. We define new procedures for quantification of covered surfaces within a pixel.     

Spatial variation in land cover and choice of spatial resolution for remote sensing

Prior to acquiring remotely sensed imagery with which to map land cover investigators may wish to select an appropriate spatial resolution. Previously, statistics such as the local variance and scale variance have been used to facilitate this goal. However, where such statistics vary locally over the region of interest, their use in selecting a single spatial resolution may be undermined. The variogram and scale variance (plotted as a function of spatial resolution) were predicted for airborne multispectral imagery with a spatial resolution of 4?m of St Albans, Hertfordshire, UK and of Arundel, Sussex, UK. The remotely sensed response in the red and near-infrared wavelengths was found to vary appreciably both within and between broad land categories (such as urban, agricultural and semi-natural areas). These differences mean that where the subject of interest is a general region rather than a specific feature or object the mean local variance or scale variance over that region may be unhelpful in selecting a single spatial resolution. Further, differences observed between the red and near-infrared wavelengths have implications for users who wish to select a single spatial resolution for multispectral imagery.     

Indicators for separating undesirable and well-delineated tree crowns in high spatial resolution images

Much effort has been spent on the automatic detection and delineation of individual trees from high spatial resolution images. However, delineation errors may lead to an inaccurate crown size when compared with ground measurements. Thus, it is problematic to use delineated crowns to derive information on tree variables, e.g. crown diameter, tree height, diameter at breast height (DBH), stand volume, stem volume or stand competition index. In this study, we investigated two indicators – the mean digital number (MDN) within each delineated crown and the difference between MDNs (DMDNs) for 0.6 m buffer zones outside and inside the boundary of each delineated crown – to separate poorly delineated crowns from well-delineated ones. We modelled the relationships between delineated crowns and field-based crown size, between delineated crowns and tree height, and between delineated crowns and DBH observations in a Norway spruce (Picea abies) stand, separately considering models based on all delineated results and crowns identified as being well delineated. Our results showed that the capability of the two indicators in separating poorly and well-delineated crowns varied under different thresholds. The results also indicated that models considering only well-delineated crowns were more robust and effective in estimating and predicting tree crown diameter, DBH and tree height than models that considered all delineated results.     

Fine spatial resolution satellite sensors for the next decade

Following the end of the Cold War governmental restrictions on the commercial availability of fine spatial resolution satellite sensor imagery have been relaxed world-wide. This, combined with marked reductions in the costs of developing, launching and operating satellites, has led to considerable research activity in this field by a number of private remote sensing organisations. Within the next few years, imagery with a spatial resolution as fine as 1 m in panchromatic mode and 4 m in multispectral mode will be available widely. This Letter presents a review of fine spatial resolution satellite sensors in operation or planned for operation within the next decade. Details of both commercial and governmental systems are provided. The emphasis is on commercially available data and so data collected for defence applications only are not included. A variety of both instrument and data specifications are highlighted, including spatial and spectral capabilities, and characteristics of viewing geometry, satellite orbit, data collection and supply. Typically, these systems are characterized not only by their fine spatial resolution, but also by high geometric precision, short revisit intervals and rapid data supply.     

一种提高CCD空间分辨力的方法研究

基于多次移位成像的原理,提出了一种提高红外电荷耦合器件(CCD)空间分辨力的方法.在不改变CCD像元尺寸的前提下,使各相邻像元对应的地面目标进行局部多次采样,提高采样频率,达到将CCD空间分辨力提高多倍的目的.仿真试验显示了其良好的性能.     

基于空间分辨率的无线传感网节点调度策略

节点调度策略是解决无线传感器网络(WSN)能量受限和覆盖高度冗余的一种有效方法,但在节能的同时又必须满足覆盖率的需求。针对随机调度中的能量消耗不均衡且使用不合理等问题,提出一种基于空间分辨率的节点调度策略。该策略通过控制区域中的活动节点数来保障网络的覆盖率要求,并利用剩余能量来均衡化各节点的能耗。同时借助邻居节点保障机制,一方面关闭休眠节点的实时监听,减少不合理的能耗;另一方面缓解节点轮休时可能出现的覆盖漏洞问题,有效保障网络覆盖率。仿真结果表明,该策略在网络覆盖率、生存期以及节点间的能耗均衡度等方面的性能表现优于Gur Game等调度算法。     

Toward combining autonomy and interactivity for social robots

The success of social robots in achieving natural coexistence with humans depends on both their level of autonomy and their interactive abilities. Although a lot of robotic architectures have been suggested and many researchers have focused on human–robot interaction, a robotic architecture that can effectively combine interactivity and autonomy is still unavailable. This paper contributes to the research efforts toward this architecture in the following ways. First a theoretical analysis is provided that leads to the notion of co-evolution between the agent and its environment and with other agents as the condition needed to combine both autonomy and interactivity. The analysis also shows that the basic competencies needed to achieve the required level of autonomy and the envisioned level of interactivity are similar but not the same. Secondly nine specific requirements are then formalized that should be achieved by the architecture. Thirdly a robotic architecture that tries to achieve those requirements by utilizing two main theoretical hypothesis and several insights from social science, developmental psychology and neuroscience is detailed. Lastly two experiments with a humanoid robot and a simulated agent are reported to show the potential of the proposed architecture.     

Estimating the effective spatial resolution of an AVHRR time series

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.     

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m² nested in macroplots of 10,000 m². Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image.Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r = 0.69) while coarse resolution images showed contrasting results (r = 0.43, r = 0.67, and r = 0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity.Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one.     

Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring

Mountain pine beetle red attack damage has been successfully detected and mapped using single-date high spatial resolution (< 4 m) satellite multi-spectral data. Forest managers; however, need to monitor locations for changes in beetle populations over time. Specifically, counts of individual trees attacked in successive years provide an indication of beetle population growth and dynamics. Surveys are typically used to estimate the ratio of green (current) attack trees to red (previous) attack trees or G:R. In this study, we estimate average stand-level G:R using a time-series of QuickBird multi-spectral and panchromatic satellite data, combined with field data for three forested stands near Merritt, British Columbia, Canada. Using a ratio of QuickBird red to green wavelengths (Red-Green Index or RGI), the change in RGI (ΔRGI) in successive image pairs is used to estimate red attack damage in 2004, 2005, and 2006, with true positive accuracies ranging from 89 to 93%. To overcome issues associated with differing viewing geometry and illumination angles that impair tracking of individual trees through time, segments are generated from the QuickBird multi-spectral data to identify small groups of trees. These segments then serve as the vehicle for monitoring changes in red attack damage over time. A local maxima filter is applied to the panchromatic data to estimate stem counts, thereby allowing an indication of the total stand population at risk of attack. By combining the red attack damage estimates with the local maxima stem counts, predictions are made of the number of attacked trees in a given year. Backcasting the current year's red attack damaged trees as the previous year's green attack facilitates the estimation of an average stand G:R. In this study area, these retrospective G:R values closely match those generated from field surveys. The results of this study indicate that a monitoring program using a time series of high spatial resolution remotely sensed data (multi-spectral and panchromatic) over select sample locations, could be used to estimate G:R over large areas, facilitating landscape level management strategies and/or providing a mechanism for assessing the efficacy of previously implemented strategies.