Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moi...

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically segmenting and clustering hyperspectral images. In this article, we propose an unsupervised learning technique that combin...

Floods are common phenomenon in the state of Dungun, specifically in Terengganu-Malaysia. Every year, floods affecting biodiversity on this region and also causing property loss of this residential area. The residents in Dungun always suffered from floods since the water overflows to the areas adjoining to the rivers, lakes or dams. The rainfall and evaporation of the area have a large influenc...

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate rapid monitoring of soil salinity its spatial variability is great significance for the amelioration saline soils. In this study, 191 samples were collected from cotton fields southern Xinjiang, China, to obtain spectral reflectance electrical conductivity (EC) indoors. Then, mu...

the use of chlorophyll fluorescence (chlf) technique was evaluated on nondestructive measurement of sugar content during fruit development. multivariate models, principal component analysis (pca), and partial least-squares regression (plsr), were developed for the classification and prediction of fructose, glucose, and sucrose in fig fruits. the results of this study showed a significant correl...

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variab...

A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input–output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t+1) from known input reference up till and including time (t). A new partial least squares regres...

Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380-1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method w...

Phase contrast microscopy is a widely-used technique to observe multi-cellular processes. Two template matching based approaches are proposed to automatically segment cells migrating in 3D Extracellular Matrix (ECM) from phase contrast images. The first approach is based on Partial Least Square Regression (PLSR), while the second applies Principal Component Analysis (PCA) and Support Vector Mac...

Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain's circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA-based predictor of melatonin phase which r...