Home About us Contact
|
Home About us Contact | ||
|
|
||
Homogeneous Regions (homogeneous + regions)
Selected AbstractsCurrent density mapping approach for design of clinical magnetic resonance imaging magnetsCONCEPTS IN MAGNETIC RESONANCE, Issue 3 2002Stuart Crozier Abstract Novel current density mapping (CDM) schemes are developed for the design of new actively shielded, clinical magnetic resonance imaging (MRI) magnets. This is an extended inverse method in which the entire potential solution space for the superconductors has been considered, rather than single current density layers. The solution provides an insight into the required superconducting coil pattern for a desired magnet configuration. This information is then used as an initial set of parameters for the magnet structure, and a previously developed hybrid numerical optimization technique is used to obtain the final geometry of the magnet. The CDM scheme is applied to the design of compact symmetric, asymmetric, and open architecture 1.0,1.5 T MRI magnet systems of novel geometry and utility. A new symmetric 1.0-T system that is just 1 m in length with a full 50-cm diameter of the active, or sensitive, volume (DSV) is detailed, as well as an asymmetric system in which a 50-cm DSV begins just 14 cm from the end of the coil structure. Finally a 1.0-T open magnet system with a full 50-cm DSV is presented. These new designs provide clinically useful homogeneous regions and have appropriately restricted stray fields but, in some of the designs, the DSV is much closer to the end of the magnet system than in conventional designs. These new designs have the potential to reduce patient claustrophobia and improve physician access to patients undergoing scans. © 2002 Wiley Periodicals, Inc. Concepts in Magnetic Resonance (Magn Reson Engineering) 15: 208,215, 2002 [source] Detection of trends in annual extreme rainfallHYDROLOGICAL PROCESSES, Issue 18 2003Kaz Adamowski Abstract Information on intensity,duration,frequency of rainfall is commonly required for a variety of hydrologic applications. In this study, trends are estimated for different durations of annual extreme rainfall using the regional average Mann,Kendall S trend test. The method of L-moments was employed to delineate homogeneous regions. The trend test was modified to account for observed autocorrelation, and a bootstrap methodology was used to account for the observed spatial correlation. Numerical analysis was performed on 44 rainfall stations from the province of Ontario, Canada, for a 20 year time frame. This was done using data from homogeneous regions established using the L-moments procedure for the annual maximum observations for the following durations: 5, 10, 15 and 30 min, and 1, 2, 6 and 12 h. Depending on different rainfall durations, four or five homogeneous regions were delineated. Based on a 5% significance level, approximately 23% of the regions tested had a significant trend, predominantly for short-duration storms. Serial dependency was observed in 2·3% of data sets and spatial correlation was found in 18% of the regions. The presence of serial and spatial correlation had a significant impact on trend determination. Copyright © 2003 John Wiley & Sons, Ltd. [source] Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspectiveINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 11 2009Willem A. Landman Abstract The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied. Copyright © 2008 Royal Meteorological Society [source] Retro-active skill of multi-tiered forecasts of summer rainfall over southern AfricaINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 1 2001Willem A. Landman Abstract Sea-surface temperature (SST) variations of the oceans surrounding southern Africa are associated with seasonal rainfall variability, especially during austral summer when the tropical atmospheric circulation is dominant over the region. Because of instabilities in the linear association between summer rainfall over southern Africa and SSTs of the tropical Indian Ocean, the skilful prediction of seasonal rainfall may best be achieved using physically based models. A two-tiered retro-active forecast procedure for the December,February (DJF) season is employed over a 10-year period starting from 1987/1988. Rainfall forecasts are produced for a number of homogeneous regions over part of southern Africa. Categorized (below-normal, near-normal and above-normal) statistical DJF rainfall predictions are made for the region to form the baseline skill level that has to be outscored by more elaborate methods involving general circulation models (GCMs). The GCM used here is the Centre for Ocean,Land,Atmosphere Studies (COLA) T30, with predicted global SST fields as boundary forcing and initial conditions derived from the National Centres for Environmental Prediction (NCEP) reanalysis data. Bias-corrected GCM simulations of circulation and moisture at certain standard pressure levels are downscaled to produce rainfall forecasts at the regional level using the perfect prognosis approach. In the two-tiered forecasting system, SST predictions for the global oceans are made first. SST anomalies of the equatorial Pacific (NIÑO3.4) and Indian oceans are predicted skilfully at 1- and 3-month lead-times using a statistical model. These retro-active SST forecasts are accurate for pre-1990 conditions, but predictability seems to have weakened during the 1990s. Skilful multi-tiered rainfall forecasts are obtained when the amplitudes of large events in the global oceans (such as El Niño and La Niña episodes) are described adequately by the predicted SST fields. GCM simulations using persisted August SST anomalies instead of forecast SSTs produce skill levels similar to those of the baseline for longer lead-times. Given high-skill SST forecasts, the scheme has the potential to provide climate forecasts that outscore the baseline skill level substantially. Copyright © 2001 Royal Meteorological Society [source] Satellite image segmentation using hybrid variable genetic algorithmINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 3 2009Mohamad M. Awad Abstract Image segmentation is an important task in image processing and analysis. Many segmentation methods have been used to segment satellite images. The success of each method depends on the characteristics of the acquired image such as resolution limitations and on the percentage of imperfections in the process of image acquisition due to noise. Many of these methods require a priori knowledge which is difficult to obtain. Some of them are parametric statistical methods that use many parameters which are dependent on image property. In this article, a new unsupervised nonparametric method is developed to segment satellite images into homogeneous regions without any a priori knowledge. The new method is called hybrid variable genetic algorithm (HVGA). The variability is found in the variable number of cluster centers and in the changeable mutation rate. In addition, this new method uses different heuristic processes to increase the efficiency of genetic algorithm in avoiding local optimal solutions. Experiments performed on two different satellite images (Landsat and Spot) proved the high accuracy and efficiency of HVGA compared with another two unsupervised and nonparametric segmentation methods genetic algorithm (GA) and self-organizing map (SOM). The verification of the results included stability and accuracy measurements using an evaluation method implemented from the functional model (FM) and field surveys. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 199,207, 2009 [source] Unsupervised segmentation of predefined shapes in multivariate imagesJOURNAL OF CHEMOMETRICS, Issue 4 2003J. C. Noordam Abstract Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segments multivariate images by incorporating both spatial and spectral information. Spatial information is described by a geometrical shape description and can vary from a local neighbourhood to a more extended shape model such as Hough circle detection. A modified FCM objective function uses the spatial information as described by the shape model. This results in a segmented image in which the construction of the cluster prototypes is influenced by spatial information. The performance of SG-FCM is compared with both FCM and the sequence of FCM and a majority filter. The SG-FCM segmented image shows more homogeneous regions and less spurious pixels. Copyright © 2003 John Wiley & Sons, Ltd. [source] | ||||||||||