scholarly journals GAWBS noise correlation between cores in multi-core fibers

2021 ◽  
Author(s):  
Kozo Sato ◽  
Masato Yoshida ◽  
Keisuke Kasai ◽  
Toshihiko Hirooka ◽  
Masataka Nakazawa
Keyword(s):  
Noise Correlation

scholarly journals Impact of noise correlation on multimodality

2016 ◽  
Author(s):  
Saloua Chlaily ◽  
Pierre-Olivier Amblard ◽  
Olivier Michel ◽  
Christian Jutten
Keyword(s):  
Noise Correlation

scholarly journals Landslide monitoring using seismic ambient noise correlation: challenges and applications

2021 ◽  
pp. 103518
Author(s):  
Mathieu Le Breton ◽  
Noélie Bontemps ◽  
Antoine Guillemot ◽  
Laurent Baillet ◽  
Éric Larose
Keyword(s):  
Ambient Noise ◽  
Landslide Monitoring ◽  
Noise Correlation ◽  
Seismic Ambient Noise

scholarly journals A numeric evaluation of attenuation from ambient noise correlation functions

2013 ◽  
Vol 118 (12) ◽  
pp. 6134-6145 ◽  
Author(s):  
Jesse F. Lawrence ◽  
Marine Denolle ◽  
Kevin J. Seats ◽  
Germán A. Prieto
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Noise Correlation

scholarly journals Population Coding of Self-Motion: Applying Bayesian Analysis to a Population of Visual Interneurons in the Fly

2005 ◽  
Vol 94 (3) ◽  
pp. 2182-2194 ◽  
Author(s):  
Katja Karmeier ◽  
Holger G. Krapp ◽  
Martin Egelhaaf
Keyword(s):  
Bayesian Analysis ◽  
Rotation Axis ◽  
Sensory Information ◽  
Population Coding ◽  
Calliphora Vicina ◽  
Integration Time ◽  
Noise Correlation ◽  
Neuronal Populations ◽  
Response Onset ◽  
Self Motion

Coding of sensory information often involves the activity of neuronal populations. We demonstrate how the accuracy of a population code depends on integration time, the size of the population, and noise correlation between the participating neurons. The population we study consists of 10 identified visual interneurons in the blowfly Calliphora vicina involved in optic flow processing. These neurons are assumed to encode the animal's head or body rotations around horizontal axes by means of graded potential changes. From electrophysiological experiments we obtain parameters for modeling the neurons' responses. From applying a Bayesian analysis on the modeled population response we draw three major conclusions. First, integration of neuronal activities over a time period of only 5 ms after response onset is sufficient to decode accurately the rotation axis. Second, noise correlation between neurons has only little impact on the population's performance. And third, although a population of only two neurons would be sufficient to encode any horizontal rotation axis, the population of 10 vertical system neurons is advantageous if the available integration time is short. For the fly, short integration times to decode neuronal responses are important when controlling rapid flight maneuvers.

scholarly journals Digital holography-based quantitative elastography using noise-correlation

2021 ◽  
Author(s):  
Agathe Marmin ◽  
Simon Chatelin ◽  
Manuel Flury ◽  
Sybille Facca ◽  
Stefan Catheline ◽  
...  
Keyword(s):  
Digital Holography ◽  
Noise Correlation
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