New transform to project axisymmetric deflection fields along arbitrary rays

2021 ◽  
Author(s):  
Timothy Sipkens ◽  
S. J. Grauer ◽  
Adam M Steinberg ◽  
S N Rogak ◽  
Patrick Kirchen
Keyword(s):  
High Speed ◽  
Gas Flow ◽  
State Of The Art ◽  
Tomographic Reconstruction ◽  
Quantitative Information ◽  
Target Object ◽  
Line Of Sight ◽  
Reconstruction Algorithms ◽  
Abel Transform ◽  
Axisymmetric Deflection

Abstract Axisymmetric tomography is used to extract quantitative information from line-of-sight measurements of gas flow and combustion fields. For instance, background oriented schlieren (BOS) measurements are typically inverted by tomographic reconstruction to estimate the density field of a high-speed or high-temperature flow. Conventional reconstruction algorithms are based on the inverse Abel transform, which assumes that rays are parallel throughout the target object. However, camera rays are not parallel, and this discrepancy can result in significant errors in many practical imaging scenarios. We present a generalization of the Abel transform for use in tomographic reconstruction of light-ray deflections through an axisymmetric target. The new transform models the exact path of camera rays instead of assuming parallel paths, thereby improving the accuracy of estimates. We demonstrate our approach with a simulated BOS scenario in which we reconstruct noisy synthetic deflection data across a range of camera positions. Results are compared to state-of-the-art Abel-based algorithms. Reconstructions computed using the new transform are consistently more stable and accurate than conventional reconstructions.

scholarly journals Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging

2020 ◽  
Vol 34 (07) ◽  
pp. 10933-10940
Author(s):  
Xiaochen Han ◽  
Bo Wu ◽  
Zheng Shou ◽  
Xiao-Yang Liu ◽  
Yimeng Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
State Of The Art ◽  
Learning Ability ◽  
Reconstruction Algorithms ◽  
Compressive Imaging ◽  
Video Frames ◽  
Synthetic Datasets ◽  
Measurement Frame ◽  
Thresholding Algorithm

Snapshot compressive imaging (SCI) cameras capture high-speed videos by compressing multiple video frames into a measurement frame. However, reconstructing video frames from the compressed measurement frame is challenging. The existing state-of-the-art reconstruction algorithms suffer from low reconstruction quality or heavy time consumption, making them not suitable for real-time applications. In this paper, exploiting the powerful learning ability of deep neural networks (DNN), we propose a novel Tensor Fast Iterative Shrinkage-Thresholding Algorithm Net (Tensor FISTA-Net) as a decoder for SCI video cameras. Tensor FISTA-Net not only learns the sparsest representation of the video frames through convolution layers, but also reduces the reconstruction time significantly through tensor calculations. Experimental results on synthetic datasets show that the proposed Tensor FISTA-Net achieves average PSNR improvement of 1.63∼3.89dB over the state-of-the-art algorithms. Moreover, Tensor FISTA-Net takes less than 2 seconds running time and 12MB memory footprint, making it practical for real-time IoT applications.

X-Ray Nanotomography (XRMT) Tool for Non-Destructive High-Resolution Imaging of ICs

2001 ◽  
Author(s):  
Wenbing Yun ◽  
Steve Wang ◽  
David Scott ◽  
Kenneth W. Nill ◽  
Waleed S. Haddad
Keyword(s):  
High Resolution ◽  
Process Development ◽  
3D Visualization ◽  
Tomographic Reconstruction ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
Visualization Software ◽  
Volumetric Reconstruction ◽  
Resolution Imaging ◽  
Non Destructive

Abstract A high-resolution table-sized x-ray nanotomography (XRMT) tool has been constructed that shows the promise of nondestructively imaging the internal structure of a full IC stack with a spatial resolution better than 100 nm. Such a tool can be used to detect, localize, and characterize buried defects in the IC. By collecting a set of X-ray projections through the full IC (which may include tens of micrometers of silicon substrate and several layers of Cu interconnects) and applying tomographic reconstruction algorithms to these projections, a 3D volumetric reconstruction can be obtained, and analyzed for defects using 3D visualization software. XRMT is a powerful technique that will find use in failure analysis and IC process development, and may facilitate or supplant investigations using SEM, TEM, and FIB tools, which generally require destructive sample preparation and a vacuum environment.

scholarly journals Neuro-Inspired Computing with Spin-VCSELs

2021 ◽  
Vol 11 (9) ◽  
pp. 4232
Author(s):  
Krishan Harkhoe ◽  
Guy Verschaffelt ◽  
Guy Van der Sande
Keyword(s):  
Processing Speed ◽  
Delay Time ◽  
High Speed ◽  
State Of The Art ◽  
Polarization Modulation ◽  
Time Interval ◽  
Computing Technique ◽  
High Modulation ◽  
Short Time ◽  
Modulation Speed

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.

scholarly journals Cavitation patterns in high-pressure homogenization nozzles with cylindrical orifices: Influence of mixing stream in Simultaneous Homogenization and Mixing

2021 ◽  
Author(s):  
V. Gall ◽  
E. Rütten ◽  
H. P. Karbstein
Keyword(s):  
High Pressure ◽  
Process Design ◽  
High Speed ◽  
State Of The Art ◽  
Back Pressure ◽  
High Pressure Homogenization ◽  
Unit Operation ◽  
Mixing Chamber ◽  
Cavitation Intensity ◽  
Droplet Sizes

AbstractHigh-pressure homogenization is the state of the art to produce high-quality emulsions with droplet sizes in the submicron range. In simultaneous homogenization and mixing (SHM), an additional mixing stream is inserted into a modified homogenization nozzle in order to create synergies between the unit operation homogenization and mixing. In this work, the influence of the mixing stream on cavitation patterns after a cylindrical orifice is investigated. Shadow-graphic images of the cavitation patterns were taken using a high-speed camera and an optically accessible mixing chamber. Results show that adding the mixing stream can contribute to coalescence of cavitation bubbles. Choked cavitation was observed at higher cavitation numbers σ with increasing mixing stream. The influence of the mixing stream became more significant at a higher orifice to outlet ratio, where a hydraulic flip was also observed at higher σ. The decrease of cavitation intensity with increasing back-pressure was found to be identical with conventional high-pressure homogenization. In the future, the results can be taken into account in the SHM process design to improve the efficiency of droplet break-up by preventing cavitation or at least hydraulic flip.

scholarly journals The Influence of the Axial Rub Added in the Radial Rub on the Wear of the Seal Fins during the High Speed Rub of Labyrinth-Honeycomb Seal

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 1997
Author(s):  
Bin Lu ◽  
Haijun Xuan ◽  
Xiaojian Ma ◽  
Fangjun Han ◽  
Weirong Hong ◽  
...  
Keyword(s):  
Wear Rate ◽  
High Speed ◽  
State Of The Art ◽  
Negative Influence ◽  
Axial Thrust ◽  
Cross Sectional ◽  
Honeycomb Seal ◽  
Aero Engine ◽  
Honeycomb Seals ◽  
Clearance Change

Labyrinth-honeycomb seals are a state-of-the-art sealing technology commonly used in aero-engine interstage seal. The undesirable severe rub between the seal fins and the honeycomb due to the clearance change may induce the cracking of the seal fins. A pervious study investigated the wear of the seal fins at different radial incursion rates. However, due to the axial thrust and mounting clearance, the axial rub between the seal fins and the honeycomb may occur. Hence, this paper focuses on the influence of the axial rub added in the radial rub on the wear of the seal fins. The rub tests results, including rubbing forces and temperature, wear rate, worn morphology, cross-sectional morphology and energy dispersive spectroscopy results, are presented and discussed. Overall, the participation of the axial rub leads to higher rubbing forces, temperature, and wear rate. The tribo-layer on the seal fin is thicker and the cracks are more obvious at high axial incursion rate. These phenomena indicate the axial rub has a negative influence on the wear of the seal fins and should be avoided.

scholarly journals Heat Transmission in High Speed Gas Flow (3rd Report)

1952 ◽  
Vol 18 (67) ◽  
pp. 31-35
Author(s):  
Kensuke KAWASHIMO ◽  
Shigebumi AOKI
Keyword(s):  
High Speed ◽  
Gas Flow ◽  
Heat Transmission

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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