Long Range, High Accuracy Optical Ranging using Frequency-Shifted Feedback Lasers

2007 ◽  
Author(s):  
Cheikh Ndiaye ◽  
Takefumi Hara ◽  
Hiromasa Ito
Keyword(s):  
Long Range ◽  
High Accuracy

scholarly journals Variability and Performance of Short to Long-Range Single Baseline RTK GNSS Positioning in Indonesia

2019 ◽  
Vol 94 ◽  
pp. 01012 ◽  
Author(s):  
Irwan Gumilar ◽  
Brian Bramanto ◽  
Fuad F. Rahman ◽  
I Made D. A. Hermawan
Keyword(s):  
Long Range ◽  
High Frequency ◽  
Carrier Phase ◽  
Satellite System ◽  
High Accuracy ◽  
Gnss Positioning ◽  
And Performance ◽  
Gnss Network ◽  
Single Baseline ◽  
Global Navigation Satellite

As the modernized Global Navigation Satellite System (GNSS) method, Real Time Kinematic (RTK) ensures high accuracy of position (within several centimeters). This method uses Ultra High Frequency (UHF) radio to transmit the correction data, however, due to gain and power issues, Networked Transport of RTCM via Internet Protocol (RTCM) is used to transmit the correction data for a longer baseline. This Research aims to investigate the performance of short to long-range single baseline RTK GNSS (Up to 80 KM) by applying modified LAMBDA method to resolve the ambiguity in carrier phase. The RTK solution then compared with the differential GNSS network solution. The results indicate that the differences are within RTK accuracy up to 80 km are several centimeter for horizontal solution and three times higher for vertical solution.

scholarly journals Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

2021 ◽  
Vol 17 (3) ◽  
pp. e1008865
Author(s):  
Yang Li ◽  
Chengxin Zhang ◽  
Eric W. Bell ◽  
Wei Zheng ◽  
Xiaogen Zhou ◽  
...  
Keyword(s):  
Long Range ◽  
3D Structure ◽  
High Accuracy ◽  
Large Set ◽  
Contact Map ◽  
Homologous Proteins ◽  
Convolutional Networks ◽  
Contact Maps ◽  
Model Training ◽  
Protein Contact Maps

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

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