Spelling suggestions: "subject:"NIR amaging"" "subject:"NIR damaging""
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Synthesis and Characterization of New Near-Infrared Chromophores: Cyanine and Phenoxazine DerivativesSoriano Juarez, Eduardo Salvador 11 August 2015 (has links)
This thesis reports the synthesis of new near infrared dyes in three chapters. The first two chapters outline the synthetic procedure for synthesizing mono- and pentamethine cyanine dyes. The initial chapter encompasses the synthesis of asymmetric monomethine dyes with red-shifted optical properties. The second chapter involves the synthesis and assessment of new symmetrical quinolin-4-yl and phenanthridin-6-yl pentamethine dyes as potential oxidative DNA cleavage agents. The last chapter of the thesis details the synthesis and evaluation of new phenoxizinum dyes as contrast agents for insulunomia, a pancreatic cancer. Furthermore, all new compounds were characterized via NMR and their coherent optical properties were obtained.
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Design, Fabrication And Characterization Of Low-Scattering Transport Regime Tissue-Equivalent Phantom And Their Use In Time-Domain NIR ImagingKarlekar, Kirtish 01 1900 (has links) (PDF)
No description available.
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Study of Biomolecular Optical Signatures for Early Disease Detection and Cell Physiology MonitoringValluru, Keerthi Srivastav 02 September 2008 (has links)
No description available.
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Photometric Methods for Autonomous Tree Species Classification and NIR Quality InspectionValieva, Inna January 2015 (has links)
In this paper the brief overview of methods available for individual tree stems quality evaluation and tree species classification has been performed. The use of Near Infrared photometry based on conifer’s canopy reflectance measurement in near infrared range of spectrum has been evaluated for the use in autonomous forest harvesting. Photometric method based on the image processing of the bark pattern has been proposed to perform classification between main construction timber tree species in Scandinavia: Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris). Several feature extraction algorithms have been evaluated, resulting two methods selected: Statistical Analysis using gray level co-occurrence matrix and maximally stable extremal regions feature detector. Feedforward Neural Network with Backpropagation training algorithm and Support Vector Machine classifiers have been implemented and compared. The verification of the proposed algorithm has been performed by real-time testing.
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