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Electronics
Special Issues
Digitalization of Metrology
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Digitalization of Metrology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 16 December 2024 | Viewed by 1170

Special Issue Editors

Prof. Dr. Xingchuang Xiong

E-Mail Website
Guest Editor
Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China
Interests: digitalization of metrology; data system of metrology
Dr. Zilong Liu

E-Mail Website
Guest Editor
Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China
Interests: digitalization of metrology; data systems of metrology
Prof. Dr. Jin Li

E-Mail Website
Guest Editor
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Interests: precise measurement technology; optic-magnetic metrology; magnetometers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digitalization of metrology is a hot topic in the area of international measurement. BIPM, ISO, IEC, and other international orgnizations are aiming towards the Digital Transformation (DT) of metrology. NIST (USA), PTB (Germany), NIM (China), and other national institutes are all developing digitalizations of metrology. Resolution 2 of the 27th CGPM (2022) is on the ‘global digital transformation and the International System of Units’. The digitalization of metrology is maintaining and building confidence in the accuracy and global comparability of measurements. As such, this research area requires the creation of a full digital representation of the SI, including robust, unambiguous, and machine-actionable representations of measurement units, values, and uncertainties.

     The aim of the Special Issue focuses on frontier research on the digitalization of metrology. In this Special Issue, original research articles and reviews are welcome. Any research on the DT of metrology or measurements is invited to be submitted to this Special Issue; research areas may include (but are not limited to) the following:

  • Metrology digitalization research:
  • Digital representations of physical quantities and units of measurement，such as D-SI;
  • Digital representations of measurement errors, uncertainties, and models, such as D-VIM and D-GUM;
  • Computations with physical quantities;
  • Information retrieval and knowledge representations (semantics, ontologies, etc.);
  • The application of FAIR principles to measurement data (metadata, data quality, etc.) and the role of metrology for FAIR data.
  • Digital metrology research:
  • Standard reference data (SRD);
  • Digital representation of metrological traceability;
  • Metrological traceability in digital shadows, digital models, and digital twins;
  • Metrological traceability in the Internet of Things.
  • Fundamental research of security for data and DT:
  • Credible technology, such as time stamps, spatial stamping, and CA;
  • Security technology, such as high-security digital signatures and encryption.
  • Metrology digitalization application:
  • Digitalization in legal metrology and quality infrastructures;
  • Metrology for the quality assessment and validation of algorithms and software;
  • Digital calibration, testing, and inspection certificates,,such as DCC;
  • Principles and technologies for remote monitoring and remote calibration;
  • Digital infrastructures and technologies for interlaboratory comparisons and proficiency testing.
  • Digital metrology application:
  • Decision making in autonomous digital measurement systems, such as computer vision (CV),
  • Infrastructures for and the application of machine learning, artificial intelligence (AI), brain science, and autonomous driving;
  • Metrology for industry and additive manufacturing;
  • Metrology for digital sensor networks and systems;
  • Cybersecurity and network communication, such as 5G;
  • Augmented and virtual reality (AR and,VR) in metrological applications.

These research topics would improve the serviceability of a digital NQI, including metrology standards and conformity assessments. We look forward to receiving your contributions.

Prof. Dr. Xingchuang Xiong
Dr. Zilong Liu
Prof. Dr. Jin Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digitalization of metrology
  • digital transformation (DT) of metrology
  • security for data and DT
  • metrology digitalization application
  • digital metrology application
  • standard reference data (SRD)
  • metrology in AI
  • metrology in DW
  • metrology in brain science
  • metrology in autonomous driving
  • metrology in AR and VR
  • metrology in 5G
  • metrology in CV

Published Papers (1 paper)

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Research

17 pages, 7405 KiB  
Article
Progressive Feature Reconstruction and Fusion to Accelerate MRI Imaging: Exploring Insights across Low, Mid, and High-Order Dimensions
by Bin Wang, Yusheng Lian, Xingchuang Xiong, Han Zhou and Zilong Liu
Electronics 2023, 12(23), 4742; https://doi.org/10.3390/electronics12234742 - 22 Nov 2023
Viewed by 805
Abstract
Magnetic resonance imaging (MRI) faces ongoing challenges associated with prolonged acquisition times and susceptibility to motion artifacts. Compressed Sensing (CS) principles have emerged as a significant advancement, addressing these issues by subsampling k-space data points and enabling rapid imaging. Nevertheless, the recovery of [...] Read more.
Magnetic resonance imaging (MRI) faces ongoing challenges associated with prolonged acquisition times and susceptibility to motion artifacts. Compressed Sensing (CS) principles have emerged as a significant advancement, addressing these issues by subsampling k-space data points and enabling rapid imaging. Nevertheless, the recovery of intricate details from under-sampled data remains a complex endeavor. In this study, we introduce an innovative deep learning approach tailored to the restoration of high-fidelity MRI images from under-sampled k-space data. Our method employs a cascaded reconstruction strategy that progressively restores hierarchical features and fuses them to achieve the final reconstruction. This cascade encompasses low, intermediate, and high orders of reconstruction, which is followed by a return through intermediate and low orders. At distinct reconstruction stages, we introduce a novel reconstruction block to recapture diverse frequency information crucial for image reconstruction. The other core innovation of our proposal lies in a fusion algorithm that harmonizes results from various reconstruction tiers into the final MRI image. Our methodology is validated using two distinct datasets. Notably, our algorithm achieves impressive PSNR values of 32.60 and 31.02 at acceleration factors of 4× and 8× in the FastMRI dataset along with SSIM scores of 0.818 and 0.771, outperforming current state-of-the-art algorithms. Similarly, on the Calgary–Campinas dataset, our algorithm achieves even higher PSNR values, reaching 37.68 and 33.44, which is accompanied by substantial SSIM scores of 0.954 and 0.901. It is essential to highlight that our algorithm achieves these remarkable results with a relatively lower parameter count, underscoring its efficiency. Comparative analyses against analogous methods further emphasize the superior performance of our approach, providing robust evidence of its effectiveness. Full article
(This article belongs to the Special Issue Digitalization of Metrology)
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