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Publications

NIST Authors in Bold

Displaying 1 - 25 of 50804

The 2019 NIST Audio-Visual Speaker Recognition Evaluation

Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Elliot Singer, Douglas A. Reynolds, Lisa Mason, Jaime Hernandez-Cordero
In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted the most recent in an ongoing series of speaker recognition evaluations (SRE)

The 2019 NIST Speaker Recognition Evaluation CTS Challenge

Author(s)
Seyed Omid Sadjadi, Craig S. Greenberg, Elliot Singer, Douglas Reynolds, Lisa Mason, Jaime Hernandez-Cordero
In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted a leaderboard style speaker recognition challenge using conversational

Analysis of a Slug Calorimeter with a Curie Point

Author(s)
Jiann C. Yang, Thomas J. Ohlemiller
A lump-capacitance heat transfer model was developed to model the operation of a slug calorimeter constructed of metal with a Curie point. The resulting formula

Improving 3D Vision-Robot Registration for Assembly Tasks

Author(s)
Marek Franaszek, Geraldine S. Cheok, Karl Van Wyk, Jeremy A. Marvel
Quality registration between the coordinate frames of a perception system and a robot is important for the efficient operation of autonomous systems in vision

Benchmarking Protocols for Evaluating Small Parts Robotic Assembly Systems

Author(s)
Kenneth E. Kimble, Joseph A. Falco, Elena R. Messina, Karl Van Wyk , Yu Sun, Mizuho Shibata, Wataru Uemura, Yasuyoshi Yokokohji
This paper presents a set of performance metrics, test methods, and associated artifacts to help progress the development and deployment of robotic assembly

Metallo-N-Heterocycles - A New Family of Hydrogen Storage Material

Author(s)
Khai Chen Tan, Yang Yu, Ruting Chen, Teng He, Zijun Jing, Qijun Pei, Jintao Wang, Yong Shen Chua, Anan Wu, Wei Zhou, Hui Wu, Ping Chen
Storing hydrogen efficiently in condensed material under mild conditions is a key technique challenge. Here, we developed a new family of hydrogen storage

Auto-tuning of double dot devices it in situ with machine learning

Author(s)
Justyna P. Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob M. Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for
Displaying 1 - 25 of 50804