The Experts below are selected from a list of 88080 Experts worldwide ranked by ideXlab platform
Dmitri V Semikoz - One of the best experts on this subject based on the ideXlab platform.
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gamma ray constraints on maximum cosmogenic neutrino fluxes and uhecr source evolution models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 1018 eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 1019 eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
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Gamma-Ray Constraints on Maximum Cosmogenic Neutrino Fluxes and UHECR Source Evolution Models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 10$^{18}$ eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 10$^{19}$ eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
Graciela B Gelmini - One of the best experts on this subject based on the ideXlab platform.
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gamma ray constraints on maximum cosmogenic neutrino fluxes and uhecr source evolution models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 1018 eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 1019 eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
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Gamma-Ray Constraints on Maximum Cosmogenic Neutrino Fluxes and UHECR Source Evolution Models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 10$^{18}$ eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 10$^{19}$ eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
O Kalashev - One of the best experts on this subject based on the ideXlab platform.
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gamma ray constraints on maximum cosmogenic neutrino fluxes and uhecr source evolution models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 1018 eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 1019 eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
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Gamma-Ray Constraints on Maximum Cosmogenic Neutrino Fluxes and UHECR Source Evolution Models
Journal of Cosmology and Astroparticle Physics, 2012Co-Authors: Graciela B Gelmini, O Kalashev, Dmitri V SemikozAbstract:The dip model assumes that the ultra-high energy cosmic rays (UHECRs) above 10$^{18}$ eV consist exclusively of protons and is consistent with the spectrum and Composition Measure by HiRes. Here we present the range of cosmogenic neutrino fluxes in the dip-model which are compatible with a recent determination of the extragalactic very high energy (VHE) gamma-ray diffuse background derived from 2.5 years of Fermi/LAT data. We show that the largest fluxes predicted in the dip model would be detectable by IceCube in about 10 years of observation and are within the reach of a few years of observation with the ARA project. In the incomplete UHECR model in which protons are assumed to dominate only above 10$^{19}$ eV, the cosmogenic neutrino fluxes could be a factor of 2 or 3 larger. Any fraction of heavier nuclei in the UHECR at these energies would reduce the maximum cosmogenic neutrino fluxes. We also consider here special evolution models in which the UHECR sources are assumed to have the same evolution of either the star formation rate (SFR), or the gamma-ray burst (GRB) rate, or the active galactic nuclei (AGN) rate in the Universe and found that the last two are disfavored (and in the dip model rejected) by the new VHE gamma-ray background.
Mohammad Faizal Ahmad Fauzi - One of the best experts on this subject based on the ideXlab platform.
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segmentation and automated Measurement of chronic wound images probability map approach
Proceedings of SPIE, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue Composition, Measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for Measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map; 3) Auto-calibration of Measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.
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Medical Imaging: Computer-Aided Diagnosis - Segmentation and automated Measurement of chronic wound images: probability map approach
Medical Imaging 2014: Computer-Aided Diagnosis, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue Composition, Measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for Measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map; 3) Auto-calibration of Measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.
Metin N Gurcan - One of the best experts on this subject based on the ideXlab platform.
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segmentation and automated Measurement of chronic wound images probability map approach
Proceedings of SPIE, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue Composition, Measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for Measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map; 3) Auto-calibration of Measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.
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Medical Imaging: Computer-Aided Diagnosis - Segmentation and automated Measurement of chronic wound images: probability map approach
Medical Imaging 2014: Computer-Aided Diagnosis, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue Composition, Measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for Measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map; 3) Auto-calibration of Measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.