Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography

2009 ◽  
Vol 36 (12) ◽  
pp. 5559-5567 ◽  
Author(s):  
Saurabh Gupta ◽  
Phaneendra K. Yalavarthy ◽  
Debasish Roy ◽  
Daqing Piao ◽  
Ram M. Vasu
Keyword(s):  
Singular Value Decomposition ◽  
Efficient Algorithm ◽  
Near Infrared ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Singular Value ◽  
Computationally Efficient ◽  
Value Decomposition

CHEMOMETRIC ANALYSIS OF DIFFUSE REFLECTANCE SPECTRAL DATA USING SINGULAR VALUE DECOMPOSITION FOR BLOOD GLUCOSE DETECTION

2018 ◽  
Vol 30 (05) ◽  
pp. 1850027
Author(s):  
S. Vasanthadev Suryakala ◽  
Shanthi Prince
Keyword(s):  
Blood Glucose ◽  
Singular Value Decomposition ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Singular Value ◽  
The Body ◽  
Measuring System ◽  
Clinical Method ◽  
Value Decomposition

Diabetes mellitus is a metabolic disorder that affects the production or usage of insulin by the body. Diabetes prevails in the body as a long-term condition which causes several other disorders if left unnoticed. Proper control of Diabetes needs continuous monitoring. The current measurement technique is invasive in nature and requires the withdrawal of blood from the body. Periodic quantification of blood glucose leads to pain and discomfort for the subject. This paper presents a non-invasive glucose measuring system using near-infrared diffuse reflectance spectroscopy (DRS). This work attempts to determine the blood glucose value from the diffuse reflected spectra in the NIR region. The study is executed with the spectral signatures of 33 diabetic subjects collected non-invasively using diffuse reflectance spectrometer from a diabetic centre. Blood glucose level of the same subjects are also recorded using the clinical method. The spectral information is subjected to standard normal variate (SNV) preprocessing method to remove baseline drift and then dimension reduction using singular value decomposition (SVD) is applied to the preprocessed data. The extracted singular values when compared with the clinically measured blood glucose is found to have a proportional relationship. The proposed study using singular value decomposition paves us the way for estimating the blood glucose value non-invasively with the obtained set of clinical blood glucose and the corresponding singular value table as a standard reference set.

scholarly journals FILTRAGEM SVD APLICADA À MELHORIA DO RASTREAMENTO DE HORIZONTES SÍSMICOS

2011 ◽  
Vol 29 (3) ◽  
Author(s):  
Milton J. Porsani ◽  
Fredy A.V. Artola ◽  
Michelângelo G. da Silva ◽  
Paulo E.M. de Melo
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
Automatic Detection ◽  
Singular Value ◽  
Seismic Events ◽  
Computationally Efficient ◽  
Automatic Mapping ◽  
Multichannel Filtering ◽  
Value Decomposition ◽  
Filtering Approach

No presente artigo apresentamos uma aplicação da filtragem SVD (Singular Value Decomposition) para o mapeamento automático de horizontes sísmicos. A filtragem SVD pode ser vista como um método de filtragem multicanal onde cada traço filtrado guarda certo grau de coerência com os traços imediatamente vizinhos. Esta filtragem preserva as relações de amplitude, fase e correlação espacial dos eventos sísmicos, ao tempo em que permite eliminar o ruído incoerente, normalmente associado aos últimos autovalores. A decomposição SVD é realizada sobre o subconjunto de traços vizinhos a cada traço da linha sísmica 2D ou de um volume 3D. O traço filtrado é obtido utilizando apenas alguns dos autovetores e autovalores associados. Ilustramos a aplicação do método sobre dados sísmicos terrestres. A melhoria da coerência dos eventos sísmicos permitiu maior robustez ao autotracking no mapeamento e interpretação automática dos horizontes sísmicos. A filtragem SVD é computacionalmente eficiente e tem o mérito de melhorar significativamente a coerência, a consistência e a continuidade dos eventos de reflexão facilitando muito o "trabalho", do tracker na busca de padrões no processo de autotracking.Keywords : mapeamento automático de horizontes; processamento sísmico; filtragem SVD; rastreamento de horizontes sísmicos.ABSTRACTWe present an application of a singular value decomposition (SVD) filtering approach to the automatic detection of seismic horizons. The SVD filtering approach may be seen as a multichannel filtering method where each filtered seismic trace retains the coherence of the neighbouring seismic traces. The SVD filtering preserves the amplitude and phase relations and reinforces the spacial correlation between seismic events, and at the same time it reduces the incoherent noise in data, which normally is associated to the last eigenvalues. The SVD decomposition is performed on each subset of traces around each trace of the original 2D or 3D seismic data. The filtered trace is obtained from the most important eigenvalues and eigenvectors. We illustrate the application of the new approach on 3D post-stack land seismic data. The improvement of the resultant coherence in the seismic reflected events allows for greater autotracking robustness during the automatic interpretation of the seismic horizons. The SVD filtering approach is computationally efficient and improves significantly the coherence, the consistency and the spacial continuity of the seismic events making easier the automatic detection of the commercial software in the search for patterns along the autotracking process.Keywords : automatic mapping of horizons; seismic processing; SVD filtering; tracking horizons seismic.

scholarly journals Applying singular value decomposition technique for quantifying the insects in commercial Thai Hommali Rice from NIR Spectrum

2017 ◽  
Vol 10 (02) ◽  
pp. 1650047 ◽  
Author(s):  
Puttinun Jarruwat ◽  
Prasan Choomjaihan
Keyword(s):  
Statistical Analysis ◽  
Singular Value Decomposition ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Singular Value ◽  
Economic Losses ◽  
Insect Infestation ◽  
Mathematical Methods ◽  
Rice Samples ◽  
Value Decomposition

Insect infestation in rice stock is a significant issue in rice exporting business, resulting in the loss of product quality, nutrient as well as the economic losses. However, detecting the insect contamination with the traditional sorting techniques were destructive, inaccurate, time consuming and unable to detect the internal insect infestation. This study used near infrared (NIR) spectroscopy for obtaining the absorbent spectra from the insect contamination in two kinds of rice samples, Milled Hommali rice (MHR) and Brown Hommali rice (BHR). The mathematical methods of partial least squares (PLSs) regression and singular value decomposition (SVD) were employed to construct the predicting model. The statistical analysis results, R2, RMSEP, RPD and bias, concluded that the predictive models from PLS for MHR and BHR were 0.95 and 0.90, 0.014 and 0.019, 4.79 and 3.11, as well as [Formula: see text]0.007 and −0.008, respectively; while the statistical analysis results from SVD for MHR and BHR were 0.97 and 0.96, 0.012 and 0.013, 5.71 and 5.39, as well as [Formula: see text]0.003 and 0.002, respectively. It showed that SVD technique performed better than PLS technique which shows that using the advantage of SVD technique required less amounts of wave numbers for predicting and was possible to construct the low cost handheld equipment for detecting the insects in rice samples.

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