Search results for "Linear discriminant analysis"

showing 3 items of 163 documents

Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

2019

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths:…

optimal spectral wavelengths010504 meteorology & atmospheric sciences0211 other engineering and technologiesRed edge02 engineering and technologyfield spectroscopy; orchards species; ANOVA–RFC–PCA; PLS; optimal spectral wavelengths; discriminant analysis01 natural sciencesPartial least squares regressionlcsh:Science021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensinganova–rfc–pcaorchards speciesNear-infrared spectroscopyHyperspectral imaging15. Life on landplsLinear discriminant analysisdiscriminant analysisfield spectroscopyRandom forestTree (data structure)Principal component analysisGeneral Earth and Planetary Scienceslcsh:QRemote Sensing
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Acoustic detection and classification of river boats

2011

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces fal…

ta113Acoustics and UltrasonicsNetwork packetbusiness.industryPattern recognitionLinear discriminant analysisRegressionWaveletDiscriminantAcoustic signatureProcess controlArtificial intelligencebusinessClassifier (UML)MathematicsApplied Acoustics
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Distinctive attributes for predicted secondary structures at terminal sequences of non-classically secreted proteins from proteobacteria

2008

Abstract C- and N-terminal sequences (64 amino acid residues each) of 89 non-classically secreted type I, type III and type IV proteins (Swiss-Prot/TrEMBL) from proteobacteria were transformed into predicted secondary structures. Multivariate analysis of variance (MANOVA) confirmed the significance of location (C- or N-termini) and secretion type as essential factors in respect of quantitative representations of structured (a-helices, b-strands) and unstructured (coils) elements. The profiles of secondary structures were transcripted using unequal property values for helices, strands and coils and corresponding numerical vectors (independent variables) were subjected to multiple discriminan…

terminal sequencesMultiple discriminant analysisGeneral Immunology and MicrobiologybiologyQH301-705.5General Neurosciencesecondary structureComputational biologyLinear discriminant analysisbiology.organism_classificationBioinformaticsdiscriminant analysisGeneral Biochemistry Genetics and Molecular BiologyCross-validationSecretory proteinDiscriminantprotein secretionSecretionProteobacteriaBiology (General)General Agricultural and Biological SciencesProtein secondary structureproteobacteriaOpen Life Sciences
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