The extinction curve of the lensing galaxy of B1152+199 at z=0.44

We present UBVRIz' photometry of the gravitational lens candidate CLASS B1152+119 obtained with the Nordic Optical Telescope. The two QSO components are resolved in the B, V, R, I and z' bands confirming the lensing nature of the system. The z=0.44 lens galaxy is clearly detected in B, R, I and z' and its position is found to be almost coincident with the faint QSO image which is heavily extincted (relative to the brighter QSO image) by dust in the lens galaxy. The extinction curve of the lens galaxy derived from the relative photometry is well fitted by a Galactic extinction law with 1.3 < R_V < 2.0 and E(B-V) ~ 1. From a simple model of the system we predict a time delay of ~ 60 days.Comment: 6 pages, 7 figures, accepted for publication in Astronomy and Astrophysic

Hyperspectral imaging as a tool for profiling basidiomycete decay of Pinus sylvestris L.

Given the right climatic and environmental conditions, a range of microorganisms can deteriorate wood. Decay by basidiomycete fungi accounts for significant volumes of wood in service that need to be replaced. In this study, a short-wave infrared hyperspectral camera was used to explore the possibilities of using spectral imaging technology for the fast and non-destructive detection of fungal decay. The study encompassed different degradation stages of Scots pine sapwood (Pinus sylvestris L.) specimens inoculated with monocultures of either a brown rot fungus (Rhodonia placenta Fr.) or a white rot fungus (Trametes versicolor L.). The research questions were if the hyperspectral camera can profile fungal wood decay and whether it also can differentiate between decay mechanisms of brown rot and white rot decay. The data analysis employed Partial Least Squares (PLS) regression with the mass loss percentage as the response variable. For all models, the mass loss could be predicted from the wavelength range 1460–1600 nm, confirming the reduction in cellulose. A single PLS component could describe the mass loss to a high degree (90%). The distinction between decay by brown or white rot fungi was made based on spectral peaks around 1680 and 2240 nm, related to lignin.publishedVersio

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

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Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

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Orders of magnitude speed increase in Partial Least Squares feature selection with new simple indexing technique for very tall data sets

Feature selection is a challenging combinatorial optimization problem that tends to require a large number of candidate feature subsets to be evaluated before a satisfying solution is obtained. Because of the computational cost associated with estimating the regression coefficients for each subset, feature selection can be an immensely time-consuming process and is often left inadequately explored. Here, we propose a simple modification to the conventional sequence of calculations involved when fitting a number of feature subsets to the same response data with partial least squares (PLS) model fitting. The modification consists in establishing the covariance matrix for the full set of features by an initial calculation and then deriving the covariance of all subsequent feature subsets solely by indexing into the original covariance matrix. By choosing this approach, which is primarily suitable for tall design matrices with significantly more rows than columns, we avoid redundant (identical) recalculations in the evaluation of different feature subsets. By benchmarking the time required to solve regression problems of various sizes, we demonstrate that the introduced technique outperforms traditional approaches by several orders of magnitude when used in conjunction with PLS modeling. In the supplementary material, we provide code for implementing the concept with kernel PLS regression.acceptedVersio

The Effect of Pull Speed and Heat Treatment on Thermal Donors in Czhocralski Silicon

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Increased sensitivity in near infrared hyperspectral imaging by enhanced background noise subtraction

Near infrared hyperspectral photoluminescence imaging of crystalline silicon wafers can reveal new knowledge on the spatial distribution and the spectral response of radiative recombination active defects in the material. The hyperspectral camera applied for this imaging technique is subject to background shot noise as well as to oscillating background noise caused by temperature fluctuations in the camera chip. Standard background noise subtraction methods do not compensate for this oscillation. Many of the defects in silicon wafers lead to photoluminescence emissions with intensities that are one order of magnitude lower than the oscillation in the background noise level. These weak signals are therefore not detected. In this work, we demonstrate an enhanced background noise subtraction scheme that accounts for temporal oscillations as well as spatial differences in the background noise. The enhanced scheme drastically increases the sensitivity of the camera and hence allows for detection of weaker signals. Thus, it may be useful to implement the method in all hyperspectral imaging applications studying weak signals

Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland

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Exploring Robots and UAVs as Phenotyping Tools in Plant Breeding

Recent advances in robot and sensor technology makes it possible to survey a large number of plants in a non destructive and cost efficient way. The present research approach includes measurements with VIS/NIR multi-spectral camera mounted on UAV and robot and traditional manual ground measurements. The analysis presented here, aims (1) to evaluate the use of multi-spectral imaging from drone and robot as phenotyping tools, (2) to compare images from drone and robot to see how they can complement each other for an optimised analysis of the plants and (3) to study the reflectance response of various plant species exposed to two different regimes of fertilisers. The sensors on UAVs provide a unique perspective of the growth of the plants revealing the map of the variations within the field of stud

Virtual phenomics - use of robots and drones in combination with genomics accelerate genetic gains in wheat breeding

Wheat breeding is a tedious process that usually takes 10-15 years and depends heavily on the ability to identify superior progeny lines by visual inspection and manual scoring of traits. Two emerging technologies are now offering potential for more precise selection and faster genetic gains: genomic prediction of breeding values based on genome-wide SNP markers and use of high throughput phenotyping technologies. In the innovation project “Reliable and efficient high-throughput phenotyping to accelerate genetic gains in Norwegian plant breeding (virtual phenomics; vPheno), 2017-2022” we are combining multispectral imaging with genomic prediction. This is a collaborative project between the industry partners Graminor AS and Making View AS and world-leading research groups in genetics, robotics and image analysis at the Norwegian University of Life Sciences, Boston University and the International Maize and Wheat Improvement Center (CIMMYT) in Mexico. In order to follow the growth of the plants during the season and calculating vegetation indices that can be used to predict grain yield, the project makes use of drones fitted with multispectral camera that are flown at weekly interval during the field season. In addition, a custom-built field robot is being used for gathering close-up images of field plots that will be used for counting the number of heads per square meter and other plant features that cannot be reliably recognized from drone images. One major use of the data is to improve the precision of genomic prediction models, the other is to enable plant breeders to visit field trials in "virtual reality", by integrating information from the drone and robot images with other available data on the field plots (grain yield, disease resistance, quality traits, marker data etc.). A prototype of the VR tool will be presented along with the progress on improving grain yield prediction by use of the multispectral drone images.Supporting documentatio