Super Resolution for Multi-Sources Image Stream Data using Smooth and Sparse Tensor Completion and its Applications in Data Acquisition of Additive Manufacturing

Technometrics ◽  
2021 ◽  
pp. 1-41
Author(s):  
Bo Shen ◽  
Rongxuan Wang ◽  
Andrew Chung Chee Law ◽  
Rakesh Kamath ◽  
Hahn Choo ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Data Acquisition ◽  
Super Resolution ◽  
Tensor Completion ◽  
Stream Data ◽  
Image Stream

scholarly journals Low-cost Remote Rain and Stream Data Acquisition System for Mapping of Potential Micro-Hydro Sites

2013 ◽  
Vol 05 (04) ◽  
pp. 56-62 ◽  
Author(s):  
R. C. Pallugna ◽  
A. B. Cultura ◽  
C. M. Gozon ◽  
N. R. Estoperez
Keyword(s):  
Data Acquisition ◽  
Low Cost ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Stream Data

scholarly journals In-situ Data Acquisition and Tool Development for Additive Manufacturing Metal Powder Systems

2019 ◽  
Author(s):  
Nick Calta ◽  
Pete Collins ◽  
Aiden Martin ◽  
Manyalibo Matthews ◽  
Johanna Nelson Weker ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Data Acquisition ◽  
Metal Powder ◽  
Tool Development ◽  
In Situ Data

scholarly journals MIGRATION CODE PADA BACKEND CRIMEZONE DARI PHP KE SCALA

2016 ◽  
Vol 12 (2) ◽  
pp. 75 ◽  
Author(s):  
Agus Suhendra ◽  
Adam Mukharil Bachtiar
Keyword(s):  
Web Service ◽  
Programming Language ◽  
Citizen Journalism ◽  
Stream Data ◽  
Web Attacks ◽  
Large Size ◽  
Image Stream ◽  
Windows Phone ◽  
Is Failure ◽  
Selection Of

Crimezone is the application of citizen journalism developed to report crimes in Bandung which is available on windows phone platform. In its use, this app still has some problems in the backend system. One that often occurs is failure in sending image stream data with a large size, the provided web service is slow in delivering the requested data, and the backend system is highly vulnerable of web attacks. The purpose of this research is to improve the performance of Crimezone’s backend system. This is achieved by migrating from the current programming language to the new one. The selection of the Scala programming language will improve the performance of the backend system, facilitate use of asynchronous programming, concurrency, and parallelism and improve the security on the system.

scholarly journals Referencing of powder bed for in situ detection of lateral layer displacements in additive manufacturing

2021 ◽  
Vol 10 (2) ◽  
pp. 247-259
Author(s):  
Martin Lerchen ◽  
Julien Schinn ◽  
Tino Hausotte
Keyword(s):  
Additive Manufacturing ◽  
Data Acquisition ◽  
Reference System ◽  
Contour Detection ◽  
In Situ Monitoring ◽  
Measuring System ◽  
Layer By Layer ◽  
Powder Bed ◽  
Measurement Systems

Abstract. An increasing number of additive manufacturing (AM) applications leads to rising challenges for the process-accompanying quality assurance. Beside post-processing measurement systems, in situ monitoring systems in particular are currently requested to ensure feedback controlling during AM processes. For data acquisition and subsequent evaluation, a high data quality is of importance. It depends on a high resolution and accuracy of measurement systems, adapted measurement conditions and a reference to the powder bed or component for geometric measurements. Within this scientific study, a new reference system has been implemented into the powder bed to reduce measurement deviations by an abbreviated metrological loop. After data acquisition and image processing layer by layer, the position stability of the reference system has been analysed in relation to the optical measuring system. Based on a contour detection of the reference markers, the evaluation of geometrical process deviations is presented as an essential basis for a closed-loop controlling system. Thermally induced and mechanical drifts within the manufacturing process can be verified by the reference system in the powder bed. As an outlook, two methods are suggested for a process-accompanying referenced detection of the melting pool and resulting contour displacements during additive manufacturing.

scholarly journals Improved Ultrasound Localization Microscopy based on Microbubble Uncoupling via Transmit Excitation (MUTE)

2021 ◽  
Author(s):  
Jihun Kim ◽  
Mathew R. Lowerison ◽  
Nathiya Chandra Sekaran ◽  
Zhengchang Kou ◽  
Zhijie Dong ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Chorioallantoic Membrane ◽  
Super Resolution ◽  
Stimulated Emission ◽  
Acquisition Time ◽  
Localization Microscopy ◽  
Ultrasound Localization ◽  
Robust Localization ◽  
Data Acquisition Time

AbstractUltrasound localization microscopy (ULM) demonstrates great potential for visualization of tissue microvasculature at depth with high spatial resolution. The success of ULM heavily depends on the robust localization of isolated microbubbles (MBs), which can be challenging in vivo especially within larger vessels where MBs can overlap and cluster close together. While MB dilution alleviates the issue of MB overlap to a certain extent, it drastically increases the data acquisition time needed for MBs to populate the microvasculature, which is already on the order of several minutes using recommended MB concentrations. Inspired by optical super-resolution imaging based on stimulated emission depletion (STED), here we propose a novel ULM imaging sequence based on microbubble uncoupling via transmit excitation (MUTE). MUTE “silences” MB signals by creating acoustic nulls to facilitate MB separation, which leads to robust localization of MBs especially under high concentrations. The efficiency of localization accomplished via the proposed technique was first evaluated in simulation studies with conventional ULM as a benchmark. Then an in vivo study based on the chorioallantoic membrane (CAM) of chicken embryos showed that MUTE could reduce the data acquisition time by half thanks to the enhanced MB separation and localization. Finally, the performance of MUTE was validated in an in vivo mouse brain study. These results demonstrate the high MB localization efficacy of MUTE-ULM, which contributes to a reduced data acquisition time and improved temporal resolution for ULM.

scholarly journals Fault Diagnosis in the Field of Additive Manufacturing (3D Printing) Using Bayesian Networks

2019 ◽  
Vol 15 (03) ◽  
pp. 110 ◽  
Author(s):  
Abdelkabir Bacha ◽  
Ahmed Haroun Sabry ◽  
Jamal Benhra
Keyword(s):  
Additive Manufacturing ◽  
Fault Diagnosis ◽  
3D Printing ◽  
Bayesian Networks ◽  
Data Acquisition ◽  
New Approach ◽  
3D Printers ◽  
Diagnosis Accuracy ◽  
High Level ◽  
Data Acquisition And Processing

In this work, a new approach for fault diagnosis in the field of additive manufacturing (3d printing) using artificial intelligence will be given. This approach is based on the marriage of the Bayesian Networks theory and data acquisition techniques. Bayesian Networks are well known for their ability to infer probabilities and to give decisional support under uncertainty. In order to do so, these probability engines must be constructed and maintained by a big amount of data and information using learning algorithms. This work provides a methodology that uses sensors based data acquisition and processing to construct such networks. Some of these sensors are already available in most of the 3d printers available in the market, while other sensors were additionally embedded in a studied 3d printer in order to enrich the number of observational variables to gain a high level of fault diagnosis accuracy and support.

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