To better understand and address the challenges faced in linking all stages of a manufacturing and design process, an investigative fabrication process was designed and enacted as part of a collaboration between National Institute of Standards and Technology (NIST) and Manufacturing Technology Centre (MTC). This collaboration sought to test selected open standards' ability to integrate the product-lifecycle stages of engineering design, manufacturing, and quality assurance through a thorough implementation of a small scale model-based enterprise (MBE).

This dataset represents the results from conducted simulations to evaluate the performance of Device-to-Device (D2D) communications in LTE when the User Equipment (UEs) are operating in UE-scheduled mode (Mode 2) with frequency hopping enabled. Different scenarios were simulated and communication performance was evaluated for each frequency hopping scheme defined in the LTE standard. The communication performance metrics include the successful transmission-reception ratio of transport blocks (TBs) at the physical layer, and the probability mass funciton (PMF) of consecutive TBs lost.

Wireless technology is a key enabler of the vision of the future factory work-cell. Such work-cell will operate autonomously with a high degree of mobility enabled by wireless technology. This model describes the work-cell using the Systems Modeling Language (SysML). Using SysML the structural and parametric characteristics of the work-cell are described. Our model provides the architectural components and performance constraints of the work-cell in which wireless is used for a significant portion of connectivity. It identifies the structural components, interfaces, and data flows.

This work involves only data processing software and does not involve data to publish. Data are acquired by the users of this software by the following methods. Two methods can be used to acquire images of agar plates with bacterial colonies grown. In the first, plates were placed on a flatbed scanner, covered by soft gloss paper (HP LaserJet soft gloss paper), and 8-bit grayscale images of the plates were obtained at 300 dpi (dots per inch; 118 dots per cm), 600 dpi (236 dots per cm), and 1,200 dpi (472 dots per cm). In the second approach, images are acquired with a digital camera.

In this study, a rail of a linear axis testbed was intentionally degraded to various levels, affecting the carriage motion, which can be deduced with either an inertial measurement unit (IMU) or a laser-based reference system. The rail was degraded over a 10-cm-long region. Thus, the two trucks that move on the rail interact with this region of the rail, changing the translational and angular error motions of the carriage. To mechanically simulate spalling, a handheld grinder was used to wear the surface of the raceway groove of the rail.

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images.

In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail surface area of a fingerprint equivalent to a rolled fingerprint from an unacclimated user, without assistance from a trained operator.

NIST, working with the FBI, has digitized 888 inked fingerprint arrest cards that were in various physical conditions, from pristine to badly damaged and faded, and were collected during law enforcement professionals' duties. This database contains images of the 10 rolled fingerprint impressions, the two four-finger slap impressions (finger positions 13 and 14), the two thumb slap impressions (finger positions 11 and 12) and the segmented impressions from the slap images (13,14).

REFPROP is an acronym for REFerence fluid PROPerties. This program, developed by the National Institute of Standards and Technology (NIST), calculates the thermodynamic and transport properties of industrially important fluids and their mixtures. These properties can be displayed in Tables and Plots through the graphical user interface; they are also accessible through spreadsheets or user-written applications accessing the REFPROP dll. REFPROP is based on the most accurate pure fluid and mixture models currently available.

There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.