Probability Matching Technique Research Articles

Contour descriptor based on space symmetry and its matching technique

In this paper a novel contour descriptor and its matching technique is proposed. The method describes the contour based on global space symmetry, and then adapts a new probability matching technique to complete the shape recognition. The method is tested on data set of MPEG7 Core Experiments Shape-1 Part B, experiments show that the proposed method is suit for shape description and matching.

Record linkage in Scotland and its applications to health research

This paper will focus on the key concepts behind record linkage and describe how probability matching of Scottish health records can be used for national health research. Record linkage can bring together two or more records relating to the same individual. This allows information from multiple sources to be joined together to produce richer data sets for research purposes and has wide applicability in public health and epidemiological research. The probability matching techniques underpinning record linkage bring together records on a patient basis using key identifying information on each record. Scotland has a strong track record for performing linkage for research purposes owing to routinely collected and well-maintained national administrative health data sets, the emergence of the Scottish record linkage system and organisations like the Information Services Division of NHS National Services Scotland who centrally hold permanently linked patient-based databases. Design. A record linkage retrospective population cohort study is described within this paper. The paper will describe current linkage methodology before discussing typical applications in the setting of Information Services Division and focusing on a particular linkage study investigating rates and risk factors for gastroschisis. Conclusions from the gastroschisis study are typical of the types of important findings drawn from analysing linked health data. Scotland's good track record for linking records for health research is evidenced by the high volume of research projects, publications and findings resulting from probability matching of national health data. Relevance to clinical practice. Record linkage allows information relating to the same person held across different data sources to be brought together. Probabilistic record linkage can overcome data quality issues, producing accurate matches. This allows linked, analysable, patient-based databases, capable of answering complex research questions, to be produced from several data sources with wide applications in the field of health research.

Multi-resolution remote sensing image registration using differential evolution with adaptive strategy selection

An algorithm based on differential evolution (DE) with adaptive strategy selection for remote sensing image registration is designed. The paper has two major contributions. One contribution is the reduction of the computation time for a better result, i.e., fitness function approximate, which is based on multi-resolution processing and histogram matching. The second contribution is an adaptive strategy known as probability matching integration. The probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. To the best of our knowledge, the proposed method is the first adaptive strategy selection-based approach for image registration. In our experiments, the proposed method significantly outperforms previously proposed image registration method based on DE.

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Abstract Both the Method for Object-based Diagnostic Evaluation (MODE) and contiguous rain area (CRA) object-based verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters. One set consisted of two eight-member Weather Research and Forecasting (WRF) model ensembles: one having mixed physics and dynamics with unperturbed initial and lateral boundary conditions (Phys) and another using common physics and a dynamic core but with perturbed initial and lateral boundary conditions (IC/LBC). Traditional measures found that spread grows much faster in IC/LBC than in Phys so that after roughly 24 h, better skill and spread are found in IC/LBC. These measures also reflected a strong diurnal signal of precipitation. The other set of ensembles included five members of a 4-km grid-spacing WRF ensemble (ENS4) and five members of a 20-km WRF ensemble (ENS20). Traditional measures suggested that the diurnal signal was better in ENS4 and spread increased more rapidly than in ENS20. Standard deviations (SDs) of four object parameters computed for the first set of ensembles using MODE and CRA showed the trend of enhanced spread growth in IC/LBC compared to Phys that had been observed in traditional measures, with the areal coverage of precipitation exhibiting the greatest growth in spread with time. The two techniques did not produce identical results; although, they did show the same general trends. A diurnal signal could be seen in the SDs of all parameters, especially rain rate, volume, and areal coverage. MODE results also found evidence of a diurnal signal and faster growth of spread in object parameters in ENS4 than in ENS20. Some forecasting approaches based on MODE and CRA output are also demonstrated. Forecasts based on averages of object parameters from each ensemble member were more skillful than forecasts based on MODE or CRA applied to an ensemble mean computed using the probability matching technique for areal coverage and volume, but differences in the two techniques were less pronounced for rain rate and displacement. The use of a probability threshold to define objects was also shown to be a valid forecasting approach with MODE.

Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation

Abstract Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network–based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June–August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels—particularly channels 3 (6.5 μm) and 4 (10.7 μm)—resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms.

Comparative estimates of the financial burden to the UK health system of hospital care for people with and without diabetes in the year before death

Objective: To quantify hospital costs prior to death for patients with and without diabetes. Research design and methods: Using the Cardiff Diabetes Database, mortality data from the UK Office of National Statistics for 1996 were linked to existing hospital records using probability matching techniques. Costs were attributed using a statistical costing technique (healthcare resource groups (HRGs)) with UK 2000 prices. Results: There were 4394 deaths of which 412 (9.4%) were for patients with diabetes. In the year before death 380 (92%) patients with diabetes (DM+) were admitted as an inpatient compared with 73% of those without diabetes (DM−), a relative rate of 1.27. Total inpatient costs were UK£ 12.2M (US$ 20M) of which costs for patients with diabetes were UK£ 1.6M (US$ 2.6M), accounting for 15.6% of revenue. This translates to a rate of UK£ 2.8M (US$ 4.0M) per 100,000 population per year. The mean annual inpatient cost before death was UK£ 3997 (US$ 5676) for DM+ compared with UK£ 2656 (US$ 3772) for DM−. Mean annual outpatient costs ranged from UK£ 185 (US$ 263: year minus 4) to UK£ 248 (US$ 352: year minus 2) in DM+, and UK£ 91 (US$ 129: year minus 4) to UK£ 116 (US$ 165: year minus 2) in DM−. Mean annual outpatient costs associated with the care of people with diabetes are consistently higher: +80% at minus 1-year rising to +120% at minus 3 years. Conclusions: The costs of inpatient care for all patients increases markedly in the final year of life. People with diabetes were found to be more financially costly, even in this stage of their care, than were people who did not have diabetes.

Validating infrared-based rainfall retrieval algorithms with 1-minute spatially dense raingage measurements over Korean peninsula

¶This study compares and contrasts six infrared-based satellite rain estimation techniques and their validation during a 2-month period from June 20–August 20, 1998 over the Korean peninsula. Two probability matching techniques (PMM1, PMM2), a look-up table technique (LUT), a convective-stratiform technique (CST), the Negri-Adler-Wetzel technique (NAWT), and the Arkin technique (ARKT) are applied to hourly infrared GMS imagery. Retrieved rainrates are compared against one-minute reporting raingage observations from the dense Automated Weather Station (AWS) network of Korea. The high spatial resolution and fine temporal resolution of the AWS measurements provide a unique and effective means to validate rain estimates derived from instantaneous space measurements, which is a main scientific focus of this study.

The Effect of Spaceborne Microwave and Ground-Based Continuous Lightning Measurements on Forecasts of the 1998 Groundhog Day Storm

This study seeks to evaluate the impact of several newly available sources of meteorological data on mesoscale model forecasts of the extratropical cyclone that struck Florida on 2 February 1998. Intermittent measurements of precipitation and integrated water vapor (IWV) distributions were obtained from Special Sensor Microwave/ Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations. The TMI also provided sea surface temperatures (SSTs) with structural detail of the Loop Current and Gulf Stream. Continuous lightning distributions were measured with a network of very low frequency radio receivers. Lightning data were tuned with intermittent spaceborne microwave radiometer data through a probability matching technique to continuously estimate convective rainfall rates. A series of experiments were undertaken to evaluate the effect of those data on mesoscale model forecasts produced after assimilating processed rainfall and IWV for 6 h. Assimilating processed rainfall, IWV, and SSTs from TMI measurements in the model yielded improved forecasts of precipitation distributions and vertical motion fields. Assimilating those data also produced an improved 9-h forecast of the radar reflectivity cross section that was validated with a coincident observation from the TRMM spaceborne precipitation radar. Sensitivity experiments showed that processed rainfall information had greater impact on the rainfall forecast than IWV and SST information. Assimilating latent heating in the correct location of the forecast model was found to be more important than an accurate determination of the rainfall intensity.

Potential of combined spaceborne infrared and microwave radiometry for near real‐time rainfall attenuation monitoring along earth‐satellite links

AbstractThe objective of this paper is to investigate how spaceborne remote sensors, and their derived products, can be exploited to optimize the performances of a satellite communication system in the presence of precipitating clouds along the path. The complementarity between sun‐synchronous microwave (MW) and geo‐stationary infrared (IR) radiometry for monitoring the earth's atmosphere is discussed and their potential as a rain detection system within near real‐time countermeasure techniques for earth‐satellite microwave links is analysed. A general approach, consisting in estimating rainfall intensity and attenuation by polar‐orbiting microwave radiometers and temporally tracking the rainfall areas by geo‐stationary infrared radiometers, is delineated. Multiple regression algorithms for predicting rainfall attenuation from spaceborne brightness temperatures and from surface rainrate, trained by radiative transfer and cloud models, are illustrated. A maximum likelihood technique is delineated to discriminate stratiform and convective rainfall from spaceborne brightness temperatures. The differences among attenuation estimates derived from layered raining‐cloud structures with respect to those obtained from simple rain slabs, as recommended by ITU‐R, are also quantified. A test of the proposed attenuation prediction methods is performed using raingage and Italsat data acquired in Spino d'Adda (Italy) during 1994. A description of the statistical method, based on the probability matching technique, adopted to combine MW and IR data for retrieving and tracking precipitating cloud systems in terms of path attenuation and accumulated rain at ground is finally provided together with its application to a case study over the Mediterranean area during October 1998. Copyright © 2001 John Wiley & Sons, Ltd.

Use of routine healthcare data in safe and cost-effective drug use.

Routine healthcare data is becoming widely available, usually as a result of administrative systems. Other related data are also often available, such as biochemistry results, mortality data, and sometimes prescribing data. These records are often linked via a common identification system or by probability matching techniques. These data sources offer many opportunities to undertake research, and where prescription data are recorded and linked, the facility to research the outcome of drug use often exists. There are now a number of research agencies around the world that use these large routine data sources to undertake drug safety and outcome studies. The purpose of this commentary is to describe some of the history behind the development of these systems, illustrate some of their uses with respect to postmarketing drug safety and to other healthcare research objectives. The review then describes the data sources necessary to develop a system that would offer an optimal system to undertake a range of studies, including population drug safety surveillance. There are both positive and negative considerations when using routine data. On the positive side, these data come from 'real life' experiences and not from the clinical trial situation. On the other hand, there are important biases to be aware of such as confounding by indication. On the whole, it is argued that large databases originating from routine healthcare procedures have an important role to play in the cost-effective prescription drug use in the postmarketing setting. These systems cannot replace other methods of drug safety evaluation but they do offer an important adjunct to spontaneous reporting systems.

Best-Link Matching of Scottish Health Data Sets

Methods are described used to link the Community Health Index and the National Health Service Central Register (NHSCR) in Scotland to provide a basis for a national patient index. The linkage used a combination of deterministic and probability matching techniques. A best-link principle was used by which each Community Health Index record was allowed to link only to the NHSCR record with which it achieved the highest match weight. This strategy, applied in the context of two files which each covered virtually the entire population of Scotland, increased the accuracy of linkage approximately a thousand-fold compared with the likely results of a less structured probability matching approach. By this means, 98.8% of linkable records were linked automatically with a sufficient degree of confidence for administrative purposes.