NIST and IEEE Challenge for MagPieR
نویسندگان
چکیده
Date of publication: 8 October 2012 The Fastest Mobile Microrobots in the World R ecent advances in micro/nanotechnologies and microelectromechanical systems have enabled micromachined mobile agents. Highly dynamic mobile microrobots are believed to open the gate for various future applications. However, at the submillimeter scale, the adhesion effects dominate physics, especially in the air environment. Although many studies have been performed to avoid or reduce this effect, the sticking phenomena are still one of the biggest challenges in achieving highly dynamic micromobile robots. Subsequently, intrinsic challenges at the given scale (hundreds of micrometers) are the powering technique themselves. Although often designed from active materials, actuation may only be performed by means of various external fields that often require a lot of space around the scene. In this context, the National Insti-tute of Standards and Technology (NIST) and the IEEE initiated an annual state-of-theart microrobotics chal-lenge, boosting the development of novel mobile agents with precise and highly dynamic propulsion mechanisms and controllability. During our first participation in this competition in 2010, the French team Centre National de la Recherche Scientifique (CNRS) proposed a magnetic and piezoelectric mobile microrobot called MagPieR, which dramatically enhanced the propulsion speed to 28 ms for the so-called 2-mm dash task. It literally cut the former By Ioan Alexandru Ivan, Gilgueng Hwang, Joel Agnus, Nicolas Chaillet, and Stéphane Régnier
منابع مشابه
STC Speaker Recognition System for the NIST i-Vector Challenge
This paper presents a Speech Technology Center (STC) system submitted to the NIST i-vector Challenge. The system includes different subsystems based on PLDA, LDA-SVM, RBM-PLDA and DBN-PLDA. We propose an original iterative scheme for clustering the NIST i-vector Challenge devset. We also introduce the RBM-PLDA subsystem in the NIST i-vector Challenge. Experiments performed on the progress datas...
متن کاملNIST language recognition evaluation - plans for 2015
We discuss two NIST coordinated evaluations of automatic language recognition technology planned for calendar year 2015 along with possible additional plans for the future. The first is the Language Recognition i-Vector Machine Learning Challenge, largely modeled on the 2013-2014 Speaker Recognition i-Vector Machine Learning Challenge. This online challenge, emphasizing the language identificat...
متن کاملStatus Report to CCEM of Electrical Metrology Developments at NIST
NIST researchers advanced the measurement and automation capabilities of quantum-based dc and ac programmable Josephson voltage standards (PJVS) such that the NIST PJVS is now capable of providing ac voltage measurements through differential sampling at voltages up to 10 V and frequencies up to 400 kHz. New automation software and hardware were implemented. Measurements of leakage resistance we...
متن کاملThe NIST 2014 Speaker Recognition i-Vector Machine Learning Challenge
During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the i-vector paradigm widely used by state-of-the-art speaker recognition systems. The i-vector challenge was run entirely online and used as source data fixed-length feature vectors projected into a low-dimensional space (i-vectors) rather than audio recordings. These changes made the challenge mor...
متن کاملA method for range calculation of proton in liquid water: Validation study using Monte Carlo method and NIST data
Introduction: The main advantage of using ion beams over photons in radiotherapy is due to their inverse depth-dose profiles, allowing higher doses to tumors, while better sparing normal tissues. When calculating dose distributions with ion beams, one crucial point is the uncertainty of the Bragg-peak range. Recently great effort is devoted to enhance the accuracy of the comput...
متن کامل