Super Spatio-Temporal Resolution, Digital PIV System for Multi-Phase Flows With Phase Differentiation and Simultaneous Shape and Size Quantification

2002 ◽  
Author(s):  
Claude Abiven ◽  
Pavlos P. Vlachos
Keyword(s):  
Temporal Resolution ◽  
Cross Correlation ◽  
Super Resolution ◽  
Quantitative Information ◽  
Acquisition Time ◽  
Correlation Algorithm ◽  
Image Velocimetry ◽  
Spatio Temporal ◽  
Multi Phase ◽  
Shape And Size

A unique, super spatio-temporal resolution Digital Particle Image Velocimetry (DPIV) system for the analysis of time-dependent multiphase flows has been developed. The system delivers a sampling frequency between 1KHz and 10KHz, with continuous total acquisition time up to 4 secs and resolution 1Kx1K pixels down to 256×256 pixels. The hardware is integrated with sophisticated image processing algorithms that allow direct image segmentation in order to resolve the multiple phases present in the flow and provides quantitative information about the shape and size of droplets or bubbles present. Finally, the in-plane velocities are measured by a super-resolution, dynamically-adaptive cross-correlation algorithm which is coupled with a particle-tracking scheme. Each individual phase present in the flow is resolved with mean spatial resolution in the order of 3–4 pixels, and accuracy in the order of 0.01–0.1 pixels, while the spatial averaging effects of cross correlation are eliminated.

A New Image-Based Soil Deformation Measurement System

2012 ◽  
Vol 629 ◽  
pp. 488-492
Author(s):  
Yan Jiao Zhao ◽  
Yu Xin Wang ◽  
Guo He ◽  
Hong Hua Zhao
Keyword(s):  
Neural Network ◽  
Measurement System ◽  
Cross Correlation ◽  
Particle Image ◽  
Research Field ◽  
Deformation Measurement ◽  
Practical Significance ◽  
Soil Deformation ◽  
Correlation Algorithm ◽  
Image Velocimetry

A Soil Deformation Measurement System using OPENCV library and FFTW library in C++ was developed in this paper. The system applied camera calibration based on neural network and Fasst Fourier Transform (FFT) cross-correlation algorithm for Particle Image Velocimetry (PIV). It is used to obtain soil deformation data, such as displacements, velocity and strain, and visualize the deformation. Experiments show that this system could acquire deformation data from soil images accurately, efficiently and continuously, which provides a strong proof that image processing technology has practical significance and application value in the research field of geotechnical engineering.

scholarly journals High-throughput super-resolution single particle trajectory analysis reconstructs organelle dynamics and membrane re-organization

2021 ◽  
Author(s):  
Pierre Parutto ◽  
Jennifer Heck ◽  
Meng Lu ◽  
Clemens F Kaminski ◽  
Martin Heine ◽  
...  
Keyword(s):  
Single Particle ◽  
Large Scale ◽  
Time Window ◽  
Super Resolution ◽  
Quantitative Information ◽  
Neuronal Membrane ◽  
Molecular Flow ◽  
Dynamic Interactions ◽  
Massive Datasets ◽  
Spatio Temporal

Super-resolution imaging can generate thousands of single-particle trajectories. These data can potentially reconstruct subcellular organization and dynamics, as well as measure disease-linked changes. However, computational methods that can derive quantitative information from such massive datasets are currently lacking. Here we present data analysis and algorithms that are broadly applicable to reveal local binding and trafficking interactions and organization of dynamic sub-cellular sites. We applied this analysis to the endoplasmic reticulum and neuronal membrane. The method is based on spatio-temporal time window segmentation that explores data at multiple levels and detects the architecture and boundaries of high density regions in areas that are hundreds of nanometers. By statistical analysis of a large number of datapoints, the present method allows measurements of nano-region stability. By connecting highly dense regions, we reconstructed the network topology of the ER, as well as molecular flow redistribution, and the local space explored by trajectories. Segmenting trajectories at appropriate scales extracts confined trajectories, allowing quantification of dynamic interactions between lysosomes and the ER. A final step of the method reveals the motion of trajectories relative to the ensemble, allowing reconstruction of dynamics in normal ER and the atlastin-null mutant. Our approach allows users to track previously inaccessible large scale dynamics at high resolution from massive datasets. The algorithm is available as an ImageJ plugin that can be applied by users to large datasets of overlapping trajectories.

Applications of Micro-PIV Technique in Visualization of Flow in Microspace

2007 ◽  
Author(s):  
Ruijin Wang ◽  
Jianzhong Lin ◽  
Yifeng Wang
Keyword(s):  
Cross Correlation ◽  
Particle Image ◽  
Characteristic Length ◽  
Tracer Particle ◽  
Image Process ◽  
Micro Piv ◽  
Correlation Algorithm ◽  
Piv Technique ◽  
Image Velocimetry ◽  
Brown Motion

A micro-resolution particle image velocimetry (micro-PIV) technique for flow visualization in microspace is presented here. The micro-PIV system was constructed through adding an epi-fluorescence microscope, improving the light source and choosing suitable tracing particle. According to smaller characteristic length of the flow in microscale and higher precision prolepsis, an image process technique based on cross correlation algorithm was conducted. To eliminate the main error caused by Brown motion of tracer particle, an approach by averaging the velocities of the ensemble particles in same interrogation plot was brought forward. Micro-PIV measure-ments of three typical flows (in a micromixer, near barriers and in a micro-jet) were carried out. The experimental results show that the micro-PIV system is suitable to both steady and unsteady flow in microscale. It is helpful to design micro-devices and analysis on data collected from such micro-devices.

scholarly journals Deep-Learning Super-Resolution Microscopy Reveals Nanometer-Scale Intracellular Dynamics at the Millisecond Temporal Resolution

2021 ◽  
Author(s):  
Rong Chen ◽  
Xiao Tang ◽  
Zeyu Shen ◽  
Yusheng Shen ◽  
Tiantian Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Single Molecule ◽  
Super Resolution ◽  
Live Cell ◽  
Acquisition Time ◽  
Microtubule Network ◽  
Single Frame ◽  
Super Resolution Microscopy

AbstractSingle-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events in thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Based on deep learning networks, we develop a single-frame super-resolution microscopy (SFSRM) approach that reconstructs a super-resolution image from a single frame of a diffraction-limited image to support live-cell super-resolution imaging at a ∼20 nm spatial resolution and a temporal resolution of up to 10 ms over thousands of time points. We demonstrate that our SFSRM method enables the visualization of the dynamics of vesicle transport at a millisecond temporal resolution in the dense and vibrant microtubule network in live cells. Moreover, the well-trained network model can be used with different live-cell imaging systems, such as confocal and light-sheet microscopes, making super-resolution microscopy accessible to nonexperts.

Comparison of Techniques for EELS Mapping in Biology

1996 ◽  
Vol 54 ◽  
pp. 300-301
Author(s):  
R.D. Leapman ◽  
S.B. Andrews
Keyword(s):  
Electron Microscope ◽  
Transmission Electron Microscope ◽  
Image Data ◽  
Beam Current ◽  
Quantitative Information ◽  
Acquisition Time ◽  
Uniform Thickness ◽  
Electron Dose ◽  
Array Detectors ◽  
Transmission Electron

Elemental mapping of biological specimens by electron energy loss spectroscopy (EELS) can be carried out both in the scanning transmission electron microscope (STEM), and in the energy-filtering transmission electron microscope (EFTEM). Choosing between these two approaches is complicated by the variety of specimens that are encountered (e.g., cells or macromolecules; cryosections, plastic sections or thin films) and by the range of elemental concentrations that occur (from a few percent down to a few parts per million). Our aim here is to consider the strengths of each technique for determining elemental distributions in these different types of specimen.On one hand, it is desirable to collect a parallel EELS spectrum at each point in the specimen using the ‘spectrum-imaging’ technique in the STEM. This minimizes the electron dose and retains as much quantitative information as possible about the inelastic scattering processes in the specimen. On the other hand, collection times in the STEM are often limited by the detector read-out and by available probe current. For example, a 256 x 256 pixel image in the STEM takes at least 30 minutes to acquire with read-out time of 25 ms. The EFTEM is able to collect parallel image data using slow-scan CCD array detectors from as many as 1024 x 1024 pixels with integration times of a few seconds. Furthermore, the EFTEM has an available beam current in the µA range compared with just a few nA in the STEM. Indeed, for some applications this can result in a factor of ~100 shorter acquisition time for the EFTEM relative to the STEM. However, the EFTEM provides much less spectral information, so that the technique of choice ultimately depends on requirements for processing the spectrum at each pixel (viz., isolated edges vs. overlapping edges, uniform thickness vs. non-uniform thickness, molar vs. millimolar concentrations).

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