Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE.

For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document's paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations.

Evaluating an image-based bidirectional reflectance distribution function measurement setup: erratum.

Two typographical errors in [Appl. Opt.57, 1918 (2018)]APOPAI0003-693510.1364/AO.57.001918 are identified and have been corrected here.

Evaluating an image-based bidirectional reflectance distribution function measurement setup.

We evaluate an image-based multiangle bidirectional reflectance distribution function measurement setup by comparing it to measurements from two commercially available goniospectrophotometers. The image-based setup uses an RGB camera to perform bidirectional measurements of the sample material. We use a conversion matrix to calculate luminance from the captured data. The matrix is calculated using camera spectral sensitivities that are measured with a monochromator. Radiance factor of the sample material is measured using a commercially available tabletop goniospectrophotometer and compared to measurements made using the image-based setup in the colorimetric domain. Our measurement setup is validated by comparing the measurements performed using a goniospectrophotometer. Uncertainty and error propagation is calculated and taken into account for validation. The sample material measured is wax-based ink printed on packaging paper substrate commonly used in the print and packaging industry. Results obtained show that the image-based setup can perform bidirectional reflectance measurements with a known uncertainty. The goniospectrophotometer measurements lie within the uncertainty of the measurements performed by the image-based measurement setup. The setup can be used to perform bidirectional reflectance measurements on samples with properties similar to the samples used in this paper.

Reducing motor vehicle greenhouse gas emissions in a non-California state: a case study of Minnesota.

Approaches for reducing greenhouse gas (GHG) emissions from motor vehicles include more-efficient vehicles, lower-carbon fuels, and reducing vehicle-kilometers traveled (VKT). Many U.S. states are considering steps to reduce emissions through actions in one or more of these areas. We model several technology and policy options for reducing GHGs from motor vehicles in Minnesota. Considerable analysis of transportation GHGs has been done for California, which has a large population and vehicle fleet and can enact unique emissions regulations; Minnesota represents a more typical state with respect to many demographic and transportation parameters. We conclude that Minnesota has a viable approach to meeting its stated GHG reduction targets (15% by 2015 and 30% by 2025, relative to year 2005) only if advancements are made in all three areas-vehicle efficiency, carbon content of fuels, and VKT. If policies focus on only one or two areas, potential improvements may be negated by backsliding in another area (e.g., increasing VKT offsetting improvements in vehicle efficiency).