Incidence Position Estimation in a PET Detector Using a Discretized Positioning Circuit and Neural Networks

2007 ◽  
pp. 684-691 ◽  
Author(s):  
Fernando Mateo ◽  
Ramón José Aliaga ◽  
Jorge Daniel Martínez ◽  
José María Monzó ◽  
Rafael Gadea
Keyword(s):  
Neural Networks ◽  
Position Estimation

scholarly journals Ensemble of neural networks for 3D position estimation in monolithic PET detectors

2019 ◽  
Vol 64 (19) ◽  
pp. 195010 ◽  
Author(s):  
A Iborra ◽  
A J González ◽  
A González-Montoro ◽  
A Bousse ◽  
D Visvikis
Keyword(s):  
Neural Networks ◽  
Position Estimation ◽  
Pet Detectors

Simultaneous Identification and Localization of Still and Mobile Speakers Based on Binaural Robot Audition

2017 ◽  
Vol 29 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Karim Youssef ◽  
◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Neural Networks ◽  
Speaker Identification ◽  
Position Estimation ◽  
Acoustic Features ◽  
Front End ◽  
Robot Audition ◽  
Speaker Tracking ◽  
Common Signal ◽  
Interaural Level Differences ◽  
Simultaneous Identification

[abstFig src='/00290001/06.jpg' width='300' text='Efficient mobile speaker tracking' ] This paper jointly addresses the tasks of speaker identification and localization with binaural signals. The proposed system operates in noisy and echoic environments and involves limited computations. It demonstrates that a simultaneous identification and localization operation can benefit from a common signal processing front end for feature extraction. Moreover, a joint exploitation of the identity and position estimation outputs allows the outputs to limit each other’s errors. Equivalent rectangular bandwidth frequency cepstral coefficients (ERBFCC) and interaural level differences (ILD) are extracted. These acoustic features are respectively used for speaker identity and azimuth estimation through artificial neural networks (ANNs). The system was evaluated in simulated and real environments, with still and mobile speakers. Results demonstrate its ability to produce accurate estimations in the presence of noises and reflections. Moreover, the advantage of the binaural context over the monaural context for speaker identification is shown.

Interests and Limits of Machine Learning-Based Neural Networks for Rotor Position Estimation in EV Traction Drives

2018 ◽  
Vol 14 (5) ◽  
pp. 1942-1951 ◽  
Author(s):  
Wided Zine ◽  
Zaatar Makni ◽  
Eric Monmasson ◽  
Lahoucine Idkhajine ◽  
Bruno Condamin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Position Estimation ◽  
Rotor Position ◽  
Traction Drives ◽  
Rotor Position Estimation

Position Estimation Of UAV By Image Processing With Neural Networks

2016 ◽  
Author(s):  
G. A. M. Goltz ◽  
E. H. Shiguemori ◽  
Haroldo F. De Campos Velho
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Position Estimation
Sign in / Sign up

Export Citation Format

Share Document