Reconstruction of arrays of acoustic vortices using holograms for multiple particle trapping

2021 ◽  
Vol 149 (4) ◽  
pp. A18-A18
Author(s):  
Noé Jiménez ◽  
Gabriela Sánchez-Rodríguez ◽  
Sergio Jiménez-Gambín ◽  
Diana Andrés ◽  
Francisco Camarena
Keyword(s):  
Particle Trapping ◽  
Multiple Particle

Fourier optics along a hybrid optical fiber for Bessel-like beam generation and its applications in multiple-particle trapping

2012 ◽  
Vol 37 (4) ◽  
pp. 623 ◽  
Author(s):  
Jongki Kim ◽  
Yoonseob Jeong ◽  
Sejin Lee ◽  
Woosung Ha ◽  
Jeon-Soo Shin ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Fourier Optics ◽  
Particle Trapping ◽  
Beam Generation ◽  
Multiple Particle

Multiple particle tracking

2018 ◽  
Author(s):  
Eric M. Furst ◽  
Todd M. Squires
Keyword(s):  
Brownian Motion ◽  
Best Practices ◽  
Particle Tracking ◽  
Heterogeneous Material ◽  
Quantitative Measurements ◽  
Particle Tracking Microrheology ◽  
Shell Models ◽  
Multiple Particle Tracking ◽  
Microscopy Data ◽  
Multiple Particle

The fundamentals and best practices of multiple particle tracking microrheology are discussed, including methods for producing video microscopy data, analyzing data to obtain mean-squared displacements and displacement correlations, and, critically, the accuracy and errors (static and dynamic) associated with particle tracking. Applications presented include two-point microrheology, methods for characterizing heterogeneous material rheology, and shell models of local (non-continuum) heterogeneity. Particle tracking has a long history. The earliest descriptions of Brownian motion relied on precise observations, and later quantitative measurements, using light microscopy.

scholarly journals Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Stephan Schlupkothen ◽  
Gerd Ascheid
Keyword(s):  
Monte Carlo ◽  
Computational Complexity ◽  
Particle Filtering ◽  
Gaussian Approximation ◽  
State Of The Art ◽  
Approximation Techniques ◽  
Accurate Localization ◽  
Likelihood Approximation ◽  
Current State ◽  
Multiple Particle

Abstract The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.

scholarly journals Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age

ACS Nano ◽  
2021 ◽  
Author(s):  
Michael McKenna ◽  
David Shackelford ◽  
Hugo Ferreira Pontes ◽  
Brendan Ball ◽  
Elizabeth Nance
Keyword(s):  
Extracellular Matrix ◽  
Particle Tracking ◽  
Multiple Particle Tracking ◽  
Multiple Particle ◽  
Brain Extracellular Matrix

Jupiter's internal magnetic field geometry relevant to particle trapping

1977 ◽  
Vol 82 (32) ◽  
pp. 5187-5194 ◽  
Author(s):  
Juan G. Roederer ◽  
Mario H. Acuña ◽  
Norman F. Ness
Keyword(s):  
Magnetic Field ◽  
Internal Magnetic Field ◽  
Particle Trapping ◽  
Field Geometry
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