The Experts below are selected from a list of 6384 Experts worldwide ranked by ideXlab platform
J.v. Stafford - One of the best experts on this subject based on the ideXlab platform.
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Information on within-field variability from sequences of yield maps: multivariate classification as a first step of interpretation
Nutrient Cycling in Agroecosystems, 1998Co-Authors: R.m. Lark, J.v. StaffordAbstract:It is shown that Automated Pattern Recognition applied to a series of yield maps can be used to divide a field into regions within which yields show similar between-season variation. These regions are associated with particular soil types. Such a regionalisation may be a useful way of recognising important within-field scales of variability, and may be a useful first step in interpretation to develop a management response.
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Classification as a first step in the interpretation of temporal and spatial variation of crop yield
Annals of Applied Biology, 1997Co-Authors: R.m. Lark, J.v. StaffordAbstract:Summary. Automated Pattern Recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general Patterns of season-to-season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.
R.m. Lark - One of the best experts on this subject based on the ideXlab platform.
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Information on within-field variability from sequences of yield maps: multivariate classification as a first step of interpretation
Nutrient Cycling in Agroecosystems, 1998Co-Authors: R.m. Lark, J.v. StaffordAbstract:It is shown that Automated Pattern Recognition applied to a series of yield maps can be used to divide a field into regions within which yields show similar between-season variation. These regions are associated with particular soil types. Such a regionalisation may be a useful way of recognising important within-field scales of variability, and may be a useful first step in interpretation to develop a management response.
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Classification as a first step in the interpretation of temporal and spatial variation of crop yield
Annals of Applied Biology, 1997Co-Authors: R.m. Lark, J.v. StaffordAbstract:Summary. Automated Pattern Recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general Patterns of season-to-season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.
N Fuller - One of the best experts on this subject based on the ideXlab platform.
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Solar feature catalogues in EGSO
SOL PHYS, 2005Co-Authors: N FullerAbstract:The Solar Feature Catalogues (SFCs) are created from digitized solar images using Automated Pattern Recognition techniques developed in the European Grid of Solar Observation (EGSO) project. The techniques were applied for detection of sunspots, active regions and filaments in the automatically standardized full-disk solar images in CaII K1, CaII K3 and H alpha taken at the Meudon Observatory and white-light images and magnetograms from SOHO/MDI. The results of Automated Recognition are verified with the manual synoptic maps and available statistical data from other observatories that revealed high detection accuracy. A structured database of the Solar Feature Catalogues is built on the MySQL server for every feature from their recognized parameters and cross-referenced to the original observations. The SFCs are published on the Bradford University web site http://www.cyber.brad.ac.uk/egso/SFC/ with the pre-designed web pages for a search by time, size and location. The SFCs with 9 year coverage (1996-2004) provide any possible information that can be extracted from full disk digital solar images. Thus information can be used for deeper investigation of the feature origin and association with other features for their Automated classification and solar activity forecast.
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Searchable solar feature catalogues
Advances in Space Research, 2005Co-Authors: V. V. Zharkova, J. Aboudarham, S. Zharkov, Stanley S. Ipson, A. K. Benkhalil, N FullerAbstract:Abstract The searchable Solar Feature Catalogues (SFCs) are developed from digitized solar images using Automated Pattern Recognition techniques. The techniques were applied for the detection of sunspots, active regions, filaments and line-of-sight magnetic neutral lines in automatically standardized full disk solar images in Ca II K1, Ca II K3 and Ha lines taken at the Paris-Meudon Observatory and white light images and magnetograms from SOHO/MDI. The results of the Automated Recognition were verified with manual synoptic maps and available statistical data that revealed good detection accuracy. Based on the recognized parameters, a structured database of Solar Feature Catalogues was built on a MySQL server for every feature and published with various pre-designed search pages on the Bradford University web site http://www.cyber.brad.ac.uk/egso/SFC/ . The SFCs with nine year coverage (1996–2004) is to be used for deeper investigation of the feature classification and solar activity forecast.
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Searchable Solar Feature Catalogues in EGSO
2004Co-Authors: V. V. Zharkova, J. Aboudarham, S. Zharkov, Stanley S. Ipson, A. K. Benkhalil, N FullerAbstract:This paper describes a searchable Solar Feature Catalogue (SFC) created using Automated Pattern Recognition techniques from digitized solar images. The techniques were developed for detection of sunspots, active regions, filaments and line-of-sight magnetic neutral lines using Ca II K1, Ca II K3 and Ha solar images from the Meudon Observatory and white light images and magnetograms from SOHO/MDI. A comp arison of the results of automatic detection with manually generated synoptic maps shows good detection accuracy. Using the characteristics extracted from the recognized features a structured database of the Solar Feature Catalogues has been built on a mysql server and published with various pre-designed search pages on the Bradford University web site http://www.cyber.brad.ac.uk/egso/. The future SFC with 11 year coverage (1995-2005) is to be used for feature classification and short-term and long-term solar activity forecast. This research is a part of the European Grid of Solar Observations (EGSO) project.
Guillaume Péron - One of the best experts on this subject based on the ideXlab platform.
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The time frame of home-range studies: from function to utilization.
Biological reviews of the Cambridge Philosophical Society, 2019Co-Authors: Guillaume PéronAbstract:As technological and statistical innovations open new avenues in movement ecology, I review the fundamental implications of the time frame of home-range studies, with the aim of associating terminologies consistently with research objectives and methodologies. There is a fundamental distinction between (a) extrapolations of stationary distributions, associated with long time scales and aiming at asymptotic consistency, and (b) period-specific techniques, aiming at specificity but typically sensitive to the sampling design. I then review the difference between function and utilization in home-range studies. Most home-range studies are based on phenomenological descriptions of the time budgets of the study animals, not the function of the visited areas. I highlight emerging trends in Automated Pattern-Recognition techniques for inference about function rather than utilization.
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The time frame of home‐range studies: from function to utilization
Biological Reviews, 2019Co-Authors: Guillaume PéronAbstract:As technological and statistical innovations open new avenues in movement ecology, I review the fundamental implications of the time frame of home-range studies, with the aim of associating terminologies consistently with research objectives and methodologies. There is a fundamental distinction between (a) extrapolations of stationary distributions, associated with long time scales and aiming at asymptotic consistency, and (b) period-specific techniques, aiming at specificity but typically sensitive to the sampling design. I then review the difference between function and utilization in home-range studies. Most home-range studies are based on phenomenological descriptions of the time budgets of the study animals, not the function of the visited areas. I highlight emerging trends in Automated Pattern-Recognition techniques for inference about function rather than utilization.
Sabine Van Huffel - One of the best experts on this subject based on the ideXlab platform.
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Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra.
Magnetic resonance in medicine, 2008Co-Authors: Jan Luts, Jean-baptiste Poullet, Juan M. García-gómez, Arend Heerschap, Montserrat Robles, Johan A. K. Suykens, Sabine Van HuffelAbstract:This study examines the effect of feature extraction methods prior to Automated Pattern Recognition based on magnetic resonance spectroscopy (MRS) for brain tumor diagnosis. Since individual inspection of spectra is time-consuming and requires specific spectroscopic expertise, the introduction of clinical decision support systems (DSSs) is expected to strongly promote the clinical use of MRS. This study focuses on the feature extraction step in the preprocessing protocol of MRS when using a DSS. On two independent data sets, encompassing single-voxel and multi-voxel data, it is observed that the use of the full spectra together with a kernel-based technique, handling high dimensional data, or using an Automated Pattern Recognition method based on independent component analysis or Relief-F achieves accurate performances. In addition, these approaches have low cost and are easy to automate. When sophisticated quantification methods are used in a DSS, user interaction should be minimized. The computationally intensive quantification techniques do not tend to increase the performance in these circumstances. The results suggest to simplify the feature reduction step in the preprocessing protocol when using a DSS purely for classification purposes. This can greatly speed up the execution of classifiers and DSSs and may accelerate their introduction into clinical practice.
