A multiscale numerical method for the heterogeneous cable equation

Several interesting problems in neuroscience are of multiscale type, i.e. possess different temporal and spatial scales that cannot be disregarded. Such characteristics impose severe burden to numerical simulations since the need to resolve small scale features pushes the computational costs to unreasonable levels. Classical numerical methods that do not resolve the small scales suffer from spurious oscillations and lack of precision.This paper presents an innovative numerical method of multiscale type that ameliorates these maladies. As an example we consider the case of a cable equation modeling heterogeneous dendrites. Our method is not only easy to parallelize, but it is also nodally exact, i.e., it matches the values of the exact solution at every node of the discretization mesh, for a class of problems.To show the validity of our scheme under different physiological regimes, we describe how the model behaves whenever the dendrites are thin or long, or the longitudinal conductance is small. We also consider the case of a large number of synapses and of large or low membrane conductance.     

Visual analysis of social networks in space and time using smartphone logs

We designed and applied interactive visualisation techniques for investigating how social networks are embedded in time and space, using data collected from smartphone logs. Our interest in spatial aspects of social networks is that they may reveal associations between participants missed by simply making contact through smartphone devices. Four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data, along with demographic and attitudinal variables, helped add context to the behaviours we observed. Using these techniques, we were able to characterise spatial and temporal aspects of participants’ social networks and suggest explanations for some of them. This provides some validation of our techniques.Unexpected deficiencies in the data that became apparent prompted us to evaluate the dataset in more detail. Contrary to what we expected, we found significant gaps in participant records, particularly in terms of location, a poorly connected sample of participants and asymmetries in reciprocal call logs. Although the data captured are of high quality, deficiencies such as these remain and are likely to have a significant impact on interpretations relating to spatial aspects of the social network. We argue that appropriately-designed interactive visualisation techniques–afforded by our flexible prototyping approach–are effective in identifying and characterising data inconsistencies. Such deficiencies are likely to exist in other similar datasets, and although the visual approaches we discuss for identifying data problems may not be scalable, the categories of problems we identify may be used to inform attempts to systematically account for errors in larger smartphone datasets.     

Texture-based volume rendering of adaptive mesh refinement data

Many phenomena in nature and engineering happen simultaneously on rather diverse spatial and temporal scales. In other words, they exhibit a multi-scale character. A special numerical multilevel technique associated with a particular hierarchical data structure is adaptive mesh refinement (AMR). This scheme achieves locally very high spatial and temporal resolutions. Due to its popularity, many scientists are in need of interactive visualization tools for AMR data. In this article, we present a 3D texture-based volume-rendering algorithm for AMR data that directly utilizes the hierarchical structure. Thereby fast rendering performance is achieved even for high-resolution data sets. To avoid multiple rendering of regions that are covered by grids of different levels of resolution, we propose a space partitioning scheme to decompose the volume into axis-aligned regions of equal-sized cells. Furthermore the problems of interpolation artifacts, opacity corrections, and texture memory limitations are addressed. Published online: November 6, 2002 Correspondence to: R. K?hler     

A GIS tool for historical instability processes data entry: An approach to hazard management in two Italian Alpine river basins

Analysis of instability processes requires historical data over a range of temporal and spatial scales. While historical data offer a wealth of information about when, where and how a flood or a landslide happened or may recur, managing the data remains problematic. Before the data can be entered into historical and geographical databases, they need to be extracted from a vast variety of paper documents and transformed into a standard format. To do this, we developed a Geographical Information System (GIS)-based tool that permits easy data entry for comparing information on different temporal and spatial scales. The GIS tool was combined with a methodology for spatial data analysis to identify main hazardous areas. The historical and geographical databases were then queried with this tool to obtain the frequency of catastrophic events and their spatial recurrence. The GIS tool allowed accurate and rapid data management for establishing a connection between textual and spatial information for new data generation.This paper illustrates a methodology that utilizes the GIS tool for analyzing instability processes in two Italian river basins in the Western Alps.     

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.     

Configuring Hierarchical Layouts to Address Research Questions

We explore the effects of selecting alternative layouts in hierarchical displays that show multiple aspects of large multivariate datasets, including spatial and temporal characteristics. Hierarchical displays of this type condition a dataset by multiple discrete variable values, creating nested graphical summaries of the resulting subsets in which size, shape and colour can be used to show subset properties. These 'small multiples' are ordered by the conditioning variable values and are laid out hierarchically using dimensional stacking. Crucially, we consider the use of different layouts at different hierarchical levels, so that the coordinates of the plane can be used more effectively to draw attention to trends and anomalies in the data. We argue that these layouts should be informed by the type of conditioning variable and by the research question being explored. We focus on space-filling rectangular layouts that provide data-dense and rich overviews of data to address research questions posed in our exploratory analysis of spatial and temporal aspects of property sales in London. We develop a notation ('HiVE') that describes visualisation and layout states and provides reconfiguration operators, demonstrate its use for reconfiguring layouts to pursue research questions and provide guidelines for this process. We demonstrate how layouts can be related through animated transitions to reduce the cognitive load associated with their reconfiguration whilst supporting the exploratory process.     

Dryland observation at local and regional scale — Comparison of Landsat TM/ETM+ and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.     

Real-time recovery of moving 3D faces for emerging applications

Existing face imaging systems are not suitable to meet the face representation and recognition demands for emerging applications in areas such as interactive gaming, enhanced learning environments and directed advertising. This is mainly due to the poor capture and characterisation of facial data that compromises their spatial and temporal precision. For emerging applications it is not only necessary to have a high level of precision for the representation of facial data, but also to characterise dynamic faces as naturally as possible and in a timely manner. This study proposes a new framework for capturing and recovering dynamic facial information in real-time at significantly high order of spatial and temporal accuracy to capture and model subtle facial changes for enhanced realism in 3D face visualisation and higher precision for face recognition applications. We also present a novel, fast, and robust correspondence mapping approach for 3D registration of moving 3D faces.     

螺旋线多维可视化模型及其应用

提出一种新的多维数据可视化方法,将风暴潮的多维信息有效结合。该数据表示模型,将多维信息按照空间圆柱螺旋线的方式进行排列,提供用户参与选择的交互窗口,并利用空间螺旋线的重复性和独特的旋转特性,将该螺旋线坐标系上的坐标轴投影到二维平面或三维体上,从而实现多维数据的可视化分析。以2006年福建台风“珍珠”的原始观测数据为例进行测试,实验结果表明,该模型能有效地将风暴潮特性数据表示出来,为应急处理等提供直观、及时的信息服务,同时对进一步的分析提供有力帮助。     

Computing optical flow across multiple scales: An adaptive coarse-to-fine strategy

Single-scale approaches to the determination of the optical flow field from the time-varying brightness pattern assume that spatio-temporal discretization is adequate for representing the patterns and motions in a scene. However, the choice of an appropriate spatial resolution is subject to conflicting, scene-dependent, constraints. In intensity-base methods for recovering optical flow, derivative estimation is more accurate for long wavelengths and slow velocities (with respect to the spatial and temporal discretization steps). On the contrary, short wavelengths and fast motions are required in order to reduce the errors caused by noise in the image acquisition and quantization process.Estimating motion across different spatial scales should ameliorate this problem. However, homogeneous multiscale approaches, such as the standard multigrid algorithm, do not improve this situation, because an optimal velocity estimate at a given spatial scale is likely to be corrupted at a finer scale. We propose an adaptive multiscale method, where the discretization scale is chosen locally according to an estimate of the relative error in the velocity estimation, based on image properties.Results for synthetic and video-acquired images show that our coarse-to-fine method, fully parallel at each scale, provides substantially better estimates of optical flow than do conventional algorithms, while adding little computational cost.     

Visual Analysis of Trajectories in Multi‐Dimensional State Spaces

Multi‐dimensional data originate from many different sources and are relevant for many applications. One specific sub‐type of such data is continuous trajectory data in multi‐dimensional state spaces of complex systems. We adapt the concept of spatially continuous scatterplots and spatially continuous parallel coordinate plots to such trajectory data, leading to continuous‐time scatterplots and continuous‐time parallel coordinates. Together with a temporal heat map representation, we design coordinated views for visual analysis and interactive exploration. We demonstrate the usefulness of our visualization approach for three case studies that cover examples of complex dynamic systems: cyber‐physical systems consisting of heterogeneous sensors and actuators networks (the collection of time‐dependent sensor network data of an exemplary smart home environment), the dynamics of robot arm movement and motion characteristics of humanoids.     

计算机辅助结构设计与分析的集成框架研究

针对计算机辅助结构设计和分析的集成问题,设计了一种基于统一模型库 方式的组件式CAD/CAE 集成框架,以结构CAD/CAE 集成模型库为框架底层,以几何造型、 可视化交互、第三方CAD、第三方CAE 等组件为中间组件层,以空间结构设计子系统、空 间结构分析子系统为上层应用层,提供空间实体建模、工程模型管理、多种有限元模型分析 与计算等功能,并应用于港口码头的结构设计及有限元分析。     

Evaluation of multiple satellite rainfall products over the rugged topography of the Tekeze-Atbara basin in Ethiopia

Satellite rainfall products are an important source of rainfall data in un-gauged catchments. However, these products need to be validated as their accuracy can be affected by geographical position, topography, climate and embedded algorithms. Eight satellite rainfall products such as African Rainfall Climatology (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPs), Global Precipitation Climatology Project GPCP), CPC Morphic technique (CMorph), Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMap), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation Algorithm version 2 (RFEv2) and Tropical Rainfall Measurement Mission (TRMM) were evaluated against ground observations over the complex topography of the upper Tekeze-Atbara basin in Ethiopia. The accuracy of the datasets was evaluated at different temporal and larger spatial scales over the period 2002–2015. The results show that the rainfall data of CHIRPS outperformed all other products at all temporal and spatial scales. Next to CHIRPS, estimates from RFEv2, 3B42v7, and PERSIANN products are closest to the measurements at rain gauges for all spatiotemporal scales: daily, monthly and seasonal, and both at point and spatial scales. The percentage bias (PBias) and correlation coefficient (r) of these products were within ±25% and >0.5 for all scales. The remaining products performed poorly with PBias up to 200% and lower r (<0.5) at all scales. However, the performance of all products improved as the temporal scale increased to month and season at all spatial scales. Compared to low altitudes <2000 m above sea level (m.a.s.l.), the PBias at high altitude (>3000 m.a.s.l.) increased by 35% whilst r dropped by 28%. CHIRPS and 3B42v7 products showed best agreement in mountainous terrains. However, all datasets show no consistency of the error sign. CMorph and 3B42v7 consistently overestimate rainfall relative to all rain gauges during the pixel-to-point rainfall comparison approach and at lowland areas during the areal averaged rainfall comparison. The other six products showed a clear underestimation at all spatial scales. In summary, the results show that rainfall estimates by CHIRPS, RFEv2 and 3B42v7 have a consistently better agreement with ground rainfall at all spatiotemporal scales. Considering the complex topography and limited gauges, the performance of CHIRPS, RFEv2 and 3B42v7 indicates that these products can be used for hydrological and overall water management applications in the region.     

Visibility-driven PET-CT visualisation with region of interest (ROI) segmentation

Multi-modality (MM) positron emission tomography-computed tomography (PET-CT) visualises biological and physiological functions (from PET) as region of interests (ROIs) within a higher resolution anatomical reference frame (from CT). The need to efficiently assess and assimilate the information from these co-aligned volumes simultaneously has stimulated new visualisation techniques that combine 3D volume rendering with interactive transfer functions to enable efficient manipulation of these volumes. However, in typical MM volume rendering visualisation, the transfer functions for the volumes are manipulated in isolation with the resulting volumes being fused, thus failing to exploit the spatial correlation that exists between the aligned volumes. Such lack of feedback makes MM transfer function manipulation complex and time consuming. Further, transfer function alone is often insufficient to select the ROIs when they have similar voxel properties to those of non-relevant regions. In this study, we propose a new ROI-based MM visibility-driven transfer function (m ²-vtf) for PET-CT visualisation. We present a novel ‘visibility’ metric, a fundamental optical property that represents how much of the ROIs are visible to the users, and use it to measure the visibility of the ROIs in PET in relation to how it is affected by transfer function manipulations to its counterpart CT. To overcome the difficulty in ROI selection, we provide an intuitive ROI selection tool based on automated PET segmentation. We further present a MM transfer function automation where the visibility metrics from the PET ROIs are used to automate its CT’s transfer function. Our GPU implementation achieved an interactive visualisation of PET-CT with efficient and intuitive transfer function manipulations.     

Scale considerations in vegetation monitoring using AVHRR data

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) is currently the only operational remote sensing system capable of providing global daily data which can be used for vegetation monitoring. These data are available with resolution cell sizes ranging from around one to 20 km on a side, though the temporal and spatial extent of cover at each resolution is variable. In this paper Normalized Difference Vegetation Index temporal curves derived from AVHRR at different resolutions are compared over both agricultural and natural tropical vegetation types. For the agricultural regions the length of growing season and major breaks of slope associated with key crop development events are equally well shown at coarse and fine resolution. Detailed examination of the curves reveals differences thought to result from temporal changes in landscape structure. Temporal curves derived from AVHRR data at dilTerent spatial resolutions shows that the spatial organization of both agricultural and natural landscapes, tropical forest in this case, changes throughout a single season. Transitions across major ecological zones are detected across a range of resolutions, though the undersampling employed in the generation of the coarser resolution products is found to exert some limitations on the spatial representivity of these data; this varies both with geographical area and time. These observations highlight the importance of a consideration of scale when using AVHRR data for vegetation monitoring, and emphasize the need for dilTerent scales of observation (both in temporal and spatial terms) for different problems and at different times of the year.     

Multi‐sensor data fusion and comparison of total column ozone

With many remote‐sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution—as well as format, structure, and temporal and spatial coverage—make the data merging, or fusion, difficult. NASA Goddard Earth Sciences Data and Information Services Center (GES‐DISC) has archived several data products for various sensors in different formats, structures, and multi‐temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/fusing several NASA Earth Observing Systems (EOS) remote‐sensing datasets available at GES‐DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES‐DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer (MODIS) Terra and Aqua Level‐3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation (OI), also was conducted and found to produce better results. Ozone Monitoring Instrument (OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder (AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high (0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/Aqua fused total column ozone is 0.79.     

生物医疗健康大数据应用支撑平台与关键技术

随着生物信息学的不断发展,生物医学领域积累了大量的数据,大数据已经贯穿基础研究、临床诊断、医药开发、健康管理等生物医学领域的各个环节。如何有效存储、管理、分析这些海量数据面临严峻的而挑战。基于超级计算机的计算分析和存储能力,在生物医学大数据处理的异构融合架构,面向生物医学大数据的层次式存储系统,生物医学大数据处理的异构并行计算和多源数据的汇聚机制与分析方法,突破生物医学大数据的汇聚、存储、分析等方面的关键技术,构建一个计算、分析处理和存储融合平台,以满足多种类型生物医学大数据应用的不同需求。     

Monitoring freshwater,estuarine and near-shore benthic ecosystems with multi-sensor remote sensing: An introduction to the special issue

This special issue features 15 papers focused on the use of remote sensing as applied to various aspects of freshwater, estuarine and near-shore benthic ecosystems. The work described herein is diverse, ranging across spatial and temporal scales, and making use of optical, radar and lidar technologies. The need for this applied research has become more crucial by the day, as resource managers are faced with issues ranging from non-point source pollution to wetland destruction, invasive species, climate change and more. Routine monitoring and mapping of the changes taking place in these ecosystems enable managers to focus their efforts in time and space, and to prioritize their responses to the most pressing issues. The most compelling aspect of much of the research reported within these pages is that the best advances emerge from the combined use of technologies across the spectrum (literally) and across a range of spatial and temporal scales. No single issue threatening the health of these ecosystems can be addressed using a single approach or with a single image acquired at a given point in time. Despite the additional resources and expense required to make use of multi-sensor, multi-temporal and multi-scale image data, the utility of such data becomes evident across a diverse range of ecosystems and a wide range of conditions. As such, we view this compilation as useful not only for applied resource management, but also as a compelling contribution to the advancement of both basic and applied research in this subject area.     

应用于GIS系统的多级时序数据模型

GIS中地理对象生命期的表示以及动态信息的表达需要时序数据的支持。一些基于关系数据库的GIS系统在实现时序数据时形成大量的数据冗余且难以维护数据的一致性。针对这些问题，结合GIS中对于时序精度的不同需求，提出了一种多级别时序数据模型。讨论了时间的相关概念，定义了一套基本的时间对象类型，提出并解释了对象级和属性级时序数据模型。基于面向对象数据库的实现显示，模型不仅满足了GIS中支持时序数据的需求，同时在存储开销、数据一致性等方面具有显著的优点。