Papers by Keyword: Spatial

Abstract: Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.

Abstract: The perception of Tolerance Analysis (TA)/Tolerance Stackup is imperative for every Design and Manufacturing Engineer because Tolerance is the criterion that should be compromised between the cost and function of a product. The literatures relevant to 15 methods of TA which are being used to determine Assembly Tolerance from Component Tolerances are collected and critically analyzed to gain an insight into the existing methods. Out of these methods, four major methods viz., Simulation Based Stack-Up Analysis, Second Order Tolerance Analysis, OpTol - Spatial Tolerance Analysis and Tolerance Analysis of 2D and 3D Assemblies are chosen for further study and comparative analysis. Based on the analysis and based on the identified merits and demerits of these methods, a framework for a new TA Method is developed. Based on the developed framework, a new TA Method using Artificial Neural Network (ANN) is developed and trained which can predict the value of Assembly Tolerance for the known Component Tolerances.

Abstract: The homogeneous-grid spatial structure of rural habitats is a special spatial type in less developed rural areas. The paper analyzed the homogenization distribution characteristics from the aspects of size and density, and analyzed the gridded morphological characteristics from the aspects of space skeleton and land use. The author concludes that economies scale, industrial development, natural conditions and facilities construction are the main driving forces for the formation of this spatial type.

Abstract: Rainfall erosivity is one of the key parameters that determine soil erosion, sediment yield, and water quality, thus its importance has grown in modeling of the environmental effects of climate change. The spatial and temporal distribution of rainfall erosivity in the Bailong River Basin in China's Gansu Province were analyzed. We derived a rainfall erosivity map based on data from 18 meteorological stations in and around the basin using the inverse distance weighting interpolation approach. The annual mean rainfall erosivity within the Bailong River Basin was 798.8 MJ mm ha^-1 h^-1 yr^-1. The mean annual amount of erosive rainfall accounts for 36.0 to 47.1% of annual precipitation, depending on the station. Rainfall erosivity was greatest from June to September, and rainfall during this period accounts for 77.7% to 84.8% of the total annual rainfall erosivity.

Abstract: Based on the daily rainfall data of 145 meteorological stations in the Yellow River basin, the spatial and temporal variations characteristic of erosive rainfall was analyzed by using statistical methods. The results show that the trend of the erosion precipitation, extreme precipitation and annual precipitation is significantly reduced. The erosion precipitation, extreme precipitation and annual precipitation are decrease from southeast to northwest. The long-term trends of different intensities rainfall is non-uniformity in space nearly 50 years. The erosion precipitation and annual precipitation are increasing in most areas of the upper reaches of the Yellow River basin, and are decreasing in the others areas, especially decreasing significantly in the water and soil loss of serious erosion in the Loess Plateau.

Abstract: As essential character of urban region, building extraction and recognition has been applied broadly in urban mapping, urban planning and population census. Traditional manual plotting is time consuming and expensive, which therefore challenges for automatic or semi-automatic solutions. High-resolution multi-spectral remote sensing imagery provides both spectral and spatial information for acquiring urban features to update geographic information database. An advanced algorithm based on the combined use of spectral and spatial features will be developed and employed to recognize and extract buildings from multi-spectral imagery in this paper. Firstly, the imagery is spatially filtered to achieve more homogeneous regions. With the spectral and spatial features, an automatic and iterative region growing algorithm is employed to segment the imagery. A feature vector is developed to recognize the buildings from the final segmentation result. The result shows that this method can extract 69.8% of the buildings in the tested imagery.

Abstract: The study analyzed the hourly and daily trends of PM_2.5 concentration and summarized the spatial change in PM_2.5 concentration as well as locations of the concentration hot spots based on data of PM_2.5 concentration, wind speed, and wind direction collected at the air quality monitoring stations in 2010 at Kaohsiung City, Taiwan. Results from the correlation analysis of PM_2.5 concentration suggest that for short-term (1-hr), PM_2.5 concentration could be easily affected by pollution sources around the monitoring stations, due to the atmospheric dispersion, the trends of long-term concentration change among stations were similar (24-hr). The average annual PM_2.5 concentration at Kaohsiung City was 41 μg/m³, and the annual over-standard rate was 13.08% compared with the alert concentration value of 65 μg/m³. The average 24-hr PM_2.5 concentration was the lowest in summer (23 μg/m³) but the highest in winter (62 μg/m³). Concentration change was also the greatest in winter, and nearly 40% of the winter time the concentration was over-standard. Results of this study suggest that higher PM_2.5 concentration would mainly happen with the winter monsoon (north wind), while lower PM_2.5 concentration would mainly happen with the summer monsoon (southwest wind). Furthermore, Daliao and Linyuan monitoring stations at Kaohsiung City are the hot spots with the highest concentration. The results also suggest that the environmental agency should further assess influences from these high PM_2.5 concentration hot spots on local people and formulate effective strategies for pollution emission control.

Abstract: Long-term trends and spatial patterns of water quality at 5 stations in the upstream of the Daling River basin of North China were examined for 5 parameters—pH, suspended sediment (SS), dissolved oxygen (DO), permanganate demand (CODMn) and biochemical oxygen demand (BOD5). Analysis determined the trends of parameters of each station between 1987 and 2007. The variations in permanganate demand and biochemical oxygen demand showed increasing trends and the variations in dissolved oxygen were decrease in 1990s. Multi-year average values of permanganate demand and dissolved oxygen in Chaoyang station and Jianping station were 2.8 mg/L, 37.6 mg/L and 9.6 mg/L, 6.1 mg/L, respectively. The parameter characteristics of water quality in flood and dry season showed significant heterogeneity at main stream and tributary. Correlations between parameters were analyzed using a regression analysis method. The correlations of each parameter determined there were linear negative correlation between dissolved oxygen and permanganate demand, dissolved oxygen and biochemical oxygen demand at Habaqi station, Dachengzi station and Chaoyang station. The permanganate demand and biochemical oxygen demand was significant positive correlation in 3 stations.

Abstract: This paper mainly reports the annual spatial variation of primary types of the grass in the study area through MODIS NDVI and EVI. First of all, we analyze each type invariably characteristic curve of monthly vegetation index in the area severally and analyze the real-time characteristics about different vegetation index of the same type to study the feature of grass in each growth stage. It turned out that the variation trend of each vegetation index is identical in general and the variation of each month is a bit difference. The value of NDVI is bigger than EVI and so is the amplitude of fluctuation. The vegetation index of grass achieves the maximum value in July except the Stenothermal desert grass which achieves it in August, and descends to the minimum in February. It turned out that the yield is filled for grass in July and August. Then we use the image taken in July to analyze the spatial status and take the density segmentation by Maximum synthesis and take two kind of image. Results show that two kinds of vegetation index reflect grass type of spatial continuity and difference. The grading of EVI and NDVI is able to reflect adequately grass type of spatial distribution, and EVI reflects a gradual change of grassland types of space more than NDVI. According to the main grass type vegetation index analysis of the spatial and temporal variation characteristics of grassland, accurate embodies the basic condition can type for further grass information extraction work and grassland dynamic monitoring research which provides the main material basis and basic work.

Abstract: The determination of an ODF, C-coefficients, property tensors and portions of texture components from EBSD orientation measurements is afflicted with statistical errors introduced by incomplete sampling of the grains. Since the measurements are highly spatially correlated and stochastically dependent, classical sampling theory does not apply. A general statistical method for error estimation in the presence of stochastically dependent observations has been developed and applied to the most important quantities of texture analysis. The method is based on the assumption of a finite range of dependence between different measurements and on the estimation of the covariance in the observed set of orientation. The methods allows the computation of standard measurement errors and confidence limits for the mentioned texture quantities. It can be used for an objective decision whether two textures are statistically equal or not, based on the comparison of estimated ODFs. Further we can decide statistically whether the ODF obeys certain types of symmetry (e.g. whether it is a girdle textures or whether it is symmetric about the shear plane observed in the field).