Multi-frequency excitation based full-field laser scanning for improved depth estimation (Conference Presentation)

2019 ◽  
Author(s):  
Seong Jin Im ◽  
JunYoung Jeon ◽  
Gyuhae Park ◽  
To Kang ◽  
Soon Woo Han
Keyword(s):  
Laser Scanning ◽  
Depth Estimation ◽  
Full Field ◽  
Frequency Excitation

Defect Depth Quantification Using Pulsed Thermography

2012 ◽  
Vol 585 ◽  
pp. 72-76 ◽  
Author(s):  
D. Sharath ◽  
M. Menaka ◽  
B. Venkatraman
Keyword(s):  
Depth Estimation ◽  
Defect Size ◽  
Defect Depth ◽  
Pulsed Thermography ◽  
Full Field ◽  
First Derivative ◽  
Derivative Method ◽  
Aisi 316 ◽  
Materials Used ◽  
Defect Quantification

Pulsed Thermography is an advanced NDE technique which is becoming popular due to fast inspection rate, non contact nature and it gives full field image. Pulsed Thermography is successfully applied for defect detection, defect depth estimation, coating thickness evaluation and delamination detection in coatings but it is limited for evaluation of subsurface defects (of the order of few mm). In this paper we discuss the application of Pulsed Thermography for defect quantification and effect of defect size on it in AISI 316 grade SS which are important structural materials used in nuclear and other industries. Log First Derivative method is considered for defect depth quantification and the results are compared with Finite Difference Modeling carried out using ThermoCalc 6L software.

scholarly journals Millimeter-Wave Bat for Mapping and Quantifying Micromotions in Full Field of View

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuyong Xiong ◽  
Songxu Li ◽  
Changzhan Gu ◽  
Guang Meng ◽  
Zhike Peng
Keyword(s):  
Millimeter Wave ◽  
Laser Scanning ◽  
Wide Spectrum ◽  
Mechanical Vibration ◽  
Phase Evolution ◽  
Field Of View ◽  
Vibration Measurement ◽  
Full Field ◽  
Cluttered Environments ◽  
Visual Sensing

Echolocating bats possess remarkable capability of multitarget spatial localization and micromotion sensing in a full field of view (FFOV) even in cluttered environments. Artificial technologies with such capability are highly desirable for various fields. However, current techniques such as visual sensing and laser scanning suffer from numerous fundamental problems. Here, we develop a bioinspired concept of millimeter-wave (mmWave) full-field micromotion sensing, creating a unique mmWave Bat (“mmWBat”), which can map and quantify tiny motions spanning macroscopic to μm length scales of full-field targets simultaneously and accurately. In mmWBat, we show that the micromotions can be measured via the interferometric phase evolution tracking from range-angle joint dimension, integrating with full-field localization and tricky clutter elimination. With our approach, we demonstrate the capacity to solve challenges in three disparate applications: multiperson vital sign monitoring, full-field mechanical vibration measurement, and multiple sound source localization and reconstruction (radiofrequency microphone). Our work could potentially revolutionize full-field micromotion monitoring in a wide spectrum of applications, while may inspiring novel biomimetic wireless sensing systems.

scholarly journals Fast Optical Sectioning for Widefield Fluorescence Mesoscopy with the Mesolens based on HiLo Microscopy

2018 ◽  
Author(s):  
Jan Schniete ◽  
Aimee Franssen ◽  
John Dempster ◽  
Trevor Bushell ◽  
William Bradshaw Amos ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Computation Time ◽  
Laser Scanning Microscopy ◽  
Confocal Laser ◽  
Optical Sectioning ◽  
Full Field ◽  
Custom Software ◽  
Very Large Datasets ◽  
Acquisition Speed ◽  
Subcellular Resolution

ABSTRACTWe present here a fast optical sectioning method for optical mesoscopy based on HiLo microscopy, which makes possible imaging of specimens of up to 4.4 mm × 3 mm × 3 mm in volume in under 17 hours (estimated for a z-stack comprising 1000 images excluding computation time) with subcellular resolution throughout. Widefield epifluorescence imaging is performed with the Mesolens using a high pixel-number camera capable of sensor-shifting to generate a 259.5 Megapixel image, and we have developed custom software to perform HiLo processing of the very large datasets. Using this method, we obtain comparable sectioning strength to confocal laser scanning microscopy (CLSM), with sections as thin as 6.8±0.2 μm and raw acquisition speed of 1 minute per slice which is up to 30 times faster than CLSM on the full field of view (FOV) of the Mesolens of 4.4 mm with lateral resolution of 0.7 μm and axial resolution of 7 μm. We have applied this HiLo mesoscopy method to image fixed and fluorescently stained hippocampal neuronal specimens and a 5-day old zebrafish larva.

scholarly journals Automatic Identification of Fluorescently Labeled Brain Cells for Rapid Functional Imaging

2010 ◽  
Vol 104 (3) ◽  
pp. 1803-1811 ◽  
Author(s):  
Ilya Valmianski ◽  
Andy Y. Shih ◽  
Jonathan D. Driscoll ◽  
David W. Matthews ◽  
Yoav Freund ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Signal To Noise Ratio ◽  
Laser Scanning Microscopy ◽  
Scanning Microscopy ◽  
Morphological Properties ◽  
Automatic Identification ◽  
Full Field ◽  
Rate Limiting Step ◽  
Calcium Indicator ◽  
On Line

The on-line identification of labeled cells and vessels is a rate-limiting step in scanning microscopy. We use supervised learning to formulate an algorithm that rapidly and automatically tags fluorescently labeled somata in full-field images of cortex and constructs an optimized scan path through these cells. A single classifier works across multiple subjects, regions of the cortex of similar depth, and different magnification and contrast levels without the need to retrain the algorithm. Retraining only has to be performed when the morphological properties of the cells change significantly. In conjunction with two-photon laser scanning microscopy and bulk-labeling of cells in layers 2/3 of rat parietal cortex with a calcium indicator, we can automatically identify ∼50 cells within 1 min and sample them at ∼100 Hz with a signal-to-noise ratio of ∼10.

scholarly journals Consequences of the Nyquist-Shannon sampling criterion in Mesoscopic Multiphoton Microscopy to avail full-field sub-micron resolution resolvability

2021 ◽  
Author(s):  
Bhaskar Jyoti Borah ◽  
Jye-Chang Lee ◽  
Han-Hsiung Chi ◽  
Yang-Ting Hsiao ◽  
Chen-Tung Yen ◽  
...  
Keyword(s):  
Figure Of Merit ◽  
Laser Scanning ◽  
Multiphoton Microscopy ◽  
Optical Resolution ◽  
Large Field ◽  
Excitation Wavelength ◽  
Full Field ◽  
Digital Resolution ◽  
Nyquist Rate ◽  
Imaging Speed

AbstractWith a limited effective voxel rate, to date, each laser-scanning mesoscopic multiphoton microscope (MPM), despite securing an ultra-large field of view (FOV) and an ultra-high optical resolution simultaneously, experiences a fundamental issue with digitization; i.e., inability to satisfy the Nyquist-Shannon sampling criterion to resolve the optics-limited sub-micron resolution over the whole FOV. Such a system either neglects the criterion degrading the digital resolution to twice the pixel size, or significantly reduces the imaging area and/or the imaging speed to respect the digitization. Here we introduce a Nyquist figure of merit parameter to assess this issue, further to comprehend a maximum aliasing-free FOV and a cross-over excitation wavelength for a laser scanning MPM system. Based on our findings we demonstrate an ultra-high voxel rate acquisition in a custom-built mesoscopic MPM system to exceed the Nyquist-rate for a >3800 FOV-resolution ratio while not compromising the imaging speed as well as the photon-budget.

scholarly journals ACCURACY OF SNOW DEPTH ESTIMATION IN MOUNTAIN AND PRAIRIE ENVIRONMENTS BY AN UNMANNED AERIAL VEHICLE

2016 ◽  
Author(s):  
Phillip Harder ◽  
Michael Schirmer ◽  
John Pomeroy ◽  
Warren Helgason
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Laser Scanning ◽  
Low Cost ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Small Scale ◽  
Canadian Prairie ◽  
Aerial Vehicle ◽  
Mean Square Errors

Abstract. The quantification of the spatial distribution of snow is crucial to predict and assess snow as a water resource and understand land-atmosphere interactions in cold regions. Typical remote sensing approaches to quantify snow depth have focused on terrestrial and airborne laser scanning and recently airborne (manned and unmanned) photogrammetry. In this study photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snowcovers at a cultivated agricultural Canadian Prairie and a sparsely-vegetated Rocky Mountain alpine ridgetop site using Structure from Motion (SfM). The ability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from the DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided meaningful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to noise-ratio. The application of SfM to UAV photographs can estimate snow depth in areas with snow depth > 30 cm – this restricts its utility for studies of the ablation of shallow, windblown snowpacks. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 6 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover. Relative to surfaces having greater contrast and more identifiable features, snow surfaces present unique challenges when applying SfM to imagery collected by a small UAV for the generation of DSMs. Regardless, the low cost, deployment mobility and the capability of repeat-on-demand flights that generate DSMs and orthomosaics of unprecedented spatial resolution provide exciting opportunities to quantify previously unobservable small-scale variability in snow depth and its dynamics.

Sign in / Sign up

Export Citation Format

Share Document