This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of ytterbium. A unit of SRM 3166a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of ytterbium. The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of aluminum. A unit of SRM 3101a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of aluminum. The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of antimony. A unit of SRM 3102a consists of 50 mL of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of antimony in a high-density polyethylene bottle sealed in an aluminized bag.

Integrated Path Differential Absorption LIDAR Measurement of CO2, CH4, and H2O near 1.6 microns

Standard Reference Material-1721: Southern Oceanic Air. Ambient Nominal Amount of Substance Fraction for Carbon Dioxide, Methane & Nitrous Oxide. This Standard Reference Material (SRM) is a primary gas mixture for which the amount-of-substance fraction, expressed as concentration, may be related to secondary working standards. This SRM is intended for the calibration of instruments used for ambient carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) determinations and for other applications. This data is public in the Certificate of Analysis for this material.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description:
In response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.

Objective:
The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database which contains different types of models, such as generic, articulated, CAD and architecture models.

Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset.

Introduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches.