Example of Measurement Science and Technology format
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Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format Example of Measurement Science and Technology format
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open access Open Access ISSN: 9570233 e-ISSN: 13616501

Measurement Science and Technology — Template for authors

Publisher: IOP Publishing
Categories Rank Trend in last 3 yrs
Applied Mathematics #104 of 548 down down by 5 ranks
Engineering (miscellaneous) #18 of 77 down down by 4 ranks
Instrumentation #37 of 128 down down by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1979 Published Papers | 7504 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/06/2020
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

1.857

0% from 2018

Impact factor for Measurement Science and Technology from 2016 - 2019
Year Value
2019 1.857
2018 1.861
2017 1.685
2016 1.585
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 0% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

3.8

6% from 2019

CiteRatio for Measurement Science and Technology from 2016 - 2020
Year Value
2020 3.8
2019 3.6
2018 3.3
2017 3.0
2016 3.3
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.48

13% from 2019

SJR for Measurement Science and Technology from 2016 - 2020
Year Value
2020 0.48
2019 0.551
2018 0.57
2017 0.53
2016 0.672
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.107

7% from 2019

SNIP for Measurement Science and Technology from 2016 - 2020
Year Value
2020 1.107
2019 1.191
2018 1.268
2017 1.095
2016 1.242
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access ISSN: 190578
recommended Recommended

Elsevier

CiteRatio: 8.7 | SJR: 1.147 | SNIP: 2.049
open access Open Access e-ISSN: 22137467

Springer

CiteRatio: 2.6 | SJR: 1.01 | SNIP: 0.941
open access Open Access ISSN: 5704928 e-ISSN: 1520569X
recommended Recommended

Taylor and Francis

CiteRatio: 9.0 | SJR: 0.984 | SNIP: 2.03
open access Open Access ISSN: 17459737 e-ISSN: 17459745

Taylor and Francis

CiteRatio: 1.4 | SJR: 0.214 | SNIP: 0.992
Measurement Science and Technology

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IOP Publishing

Measurement Science and Technology

Subject coverage With 12 issues per year, Measurement Science and Technology publishes articles on new measurement techniques and associated instrumentation. Papers that describe experiments must represent an advance in measurement science or measurement technique rather than ...... Read More

Mathematics

i
Last updated on
01 Jun 2020
i
ISSN
0957-0233
i
Impact Factor
High - 1.21
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
iopart-num
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Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker C W J 2006 Phys. Rev. Lett. 97 067007 URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1088/0957-0233/11/12/702
Random Data Analysis and Measurement Procedures
J S Bendat and A G Piersol1

Abstract:

This is a new edition of a book on random data analysis which has been on the market since 1966 and which was extensively revised in 1971. The book has been a bestseller since. It has been fully updated to cover new procedures developed in the last 15 years and extends the discussion to a broad range of applied fields, such a... This is a new edition of a book on random data analysis which has been on the market since 1966 and which was extensively revised in 1971. The book has been a bestseller since. It has been fully updated to cover new procedures developed in the last 15 years and extends the discussion to a broad range of applied fields, such as aerospace, automotive industries or biomedical research. The primary purpose of this book is to provide a practical reference and tool for working engineers and scientists investigating dynamic data or using statistical methods to solve engineering problems. It is comprehensive and self-contained and expands the coverage of the theory, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind. It could well be used as a teaching textbook for advanced courses on the analysis of random processes. The first four chapters present the background material on descriptions of data, properties of linear systems and statistical principles. They also include probability distribution formulas for one-, two- and higher-order changes of variables. Chapter five gives a comprehensive discussion of stationary random-process theory, including material on wave-number spectra, level crossings and peak values of normally distributed random data. Chapters six and seven develop mathematical relationships for the detailed analysis of single input/output and multiple input/output linear systems including algorithms. In chapters eight and nine important practical formulas to determine statistical errors in estimates of random data parameters and linear system properties from measured data are derived. Chapter ten deals with data aquisition and processing, including data qualification. Chapter eleven describes methods of data analysis such as data preparation, Fourier transforms, probability density functions, auto- and cross-correlation, spectral functions, joint record functions and multiple input/output functions. Chapter twelve shows how to handle nonstationary data analysis, classification of nonstationary data, probability structure of nonstationary data, calculation of nonstationary mean values or mean square values, correlation structures of nonstationary data and spectral structures of nonstationary data. The last chapter deals with the Hilbert transform including applications for both nondispersive and dispersive propagation problems. All chapters include many illustrations and references as well as examples and problem sets. This allows the reader to use the book for private study purposes. Altogether the book can be recommended for practical working engineers and scientists to support their daily work, as well as for university readers as a teaching textbook in advanced courses. M Krystek read more read less

Topics:

Probability distribution (53%)53% related to the paper, Dynamic data (51%)51% related to the paper, Probability density function (50%)50% related to the paper
3,332 Citations
open accessOpen access Journal Article DOI: 10.1088/0957-0233/17/12/R01
Energy harvesting vibration sources for microsystems applications
S Beeby1, Michael Tudor1, Neil M. White1

Abstract:

This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equat... This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equations that relate to the three main transduction mechanisms employed to extract energy from the system. These transduction mechanisms are: piezoelectric, electromagnetic and electrostatic. Piezoelectric generators employ active materials that generate a charge when mechanically stressed. A comprehensive review of existing piezoelectric generators is presented, including impact coupled, resonant and human-based devices. Electromagnetic generators employ electromagnetic induction arising from the relative motion between a magnetic flux gradient and a conductor. Electromagnetic generators presented in the literature are reviewed including large scale discrete devices and wafer-scale integrated versions. Electrostatic generators utilize the relative movement between electrically isolated charged capacitor plates to generate energy. The work done against the electrostatic force between the plates provides the harvested energy. Electrostatic-based generators are reviewed under the classifications of in-plane overlap varying, in-plane gap closing and out-of-plane gap closing; the Coulomb force parametric generator and electret-based generators are also covered. The coupling factor of each transduction mechanism is discussed and all the devices presented in the literature are summarized in tables classified by transduction type; conclusions are drawn as to the suitability of the various techniques. read more read less

Topics:

Energy harvesting (53%)53% related to the paper, Electromagnetic induction (52%)52% related to the paper
View PDF
2,637 Citations
Journal Article DOI: 10.1088/0957-0233/9/6/022
An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements

Abstract:

Students in a science or engineering curriculum ought to be introduced early to the requirement that a meaningful measurement result should always be accompanied by a statement of its uncertainty. This book has been written specifically with this objective in mind. That the first edition has been successful in doing this is a... Students in a science or engineering curriculum ought to be introduced early to the requirement that a meaningful measurement result should always be accompanied by a statement of its uncertainty. This book has been written specifically with this objective in mind. That the first edition has been successful in doing this is attested to by its popularity with both faculty and students, and its translation into six languages. This book is not a statistics text - nor was it intended to be - but an introdution to the mathematics required for the analysis of measurements at the level of a first-year laboratory course. Part 1 begins with uncertainty as a qualitative concept and builds slowly, using many numerical examples and exercises for the student, to develop methods for quantifying uncertainty, and ultimately relating it to the standard deviation of a statistical distribution. Along the way, Taylor develops the rules for expressing and combining (`propagating') uncertainties, and introduces the student to the gaussian (normal) distribution and some of its properties. Part 2 covers, with somewhat more mathematical rigor, specific topics such as data rejection criteria, the binomial and Poisson distributions, covariance and correlation, least-squares fitting, and the chi-squared test. I was not familiar with the first edition, and from a quick scan of the Preface I looked forward to reading this book and learning something about the state of statistical analysis in first-year university texts today. I was disappointed (in part with what the level of the book implies about the sad state of preparation of today's students). Although there are now two ISO publications ( International Vocabulary of Basic and General Terms in Metrology (VIM) and Guide to the Expression of Uncertainty in Measurement (GUM), Geneva, 1993), Taylor makes no mention of either, and never gives a formal definition of `uncertainty' (although he ultimately associates `random uncertainty' with the standard deviation of a gaussian distribution). The book also does not clearly define `error', or the distinction between error and uncertainty. The important point, that the `propagation of uncertainty' is additive in terms of variances is valid for any distributions with finite variance, is not emphasized; instead Taylor restricts the discussion solely to the normal distribution and or those that can be approximated by it. I also find it unfortunate that the book does not clearly distinguish between the variance of a sample , the variance of a distribution , and the sample estimate of the variance of the distribution ( or ). Instead, he accepts the fact that formulas for the variance with either N or N - 1 dividing the sum of the squares of the deviations from the mean exist in the literature and concludes simply: `Nevertheless, you need to be aware of both definitions. In the physics laboratory, using the more conservative... def- inition... is almost always best.' In spite of these shortcomings, the book is a significant contribution to a student laboratory reading list, and it is written at a level that facilitates a self-study program. It has an important message to deliver and it appears to be delivering it well. read more read less

Topics:

Variance (accounting) (54%)54% related to the paper, Measurement uncertainty (52%)52% related to the paper, Normal distribution (50%)50% related to the paper
2,449 Citations
Journal Article DOI: 10.1088/0957-0233/20/6/062001
Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review
Bing Pan1, Kemao Qian1, Huimin Xie2, Anand Asundi1

Abstract:

As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field ... As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field strains by comparing the digital images of a test object surface acquired before and after deformation. In this review, methodologies of the 2D DIC technique for displacement field measurement and strain field estimation are systematically reviewed and discussed. Detailed analyses of the measurement accuracy considering the influences of both experimental conditions and algorithm details are provided. Measures for achieving high accuracy deformation measurement using the 2D DIC technique are also recommended. Since microscale and nanoscale deformation measurement can easily be realized by combining the 2D DIC technique with high-spatial-resolution microscopes, the 2D DIC technique should find more applications in broad areas. read more read less

Topics:

Digital image correlation (57%)57% related to the paper, Displacement field (55%)55% related to the paper, Digital image (52%)52% related to the paper, Deformation (meteorology) (50%)50% related to the paper
View PDF
2,125 Citations
Journal Article DOI: 10.1088/0957-0233/12/10/706
Fault Detection and Diagnosis in Industrial Systems

Abstract:

The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists. The material included is well organized with logical and clearly identified parts; the list of references is quite comprehensive and will be of inte... The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists. The material included is well organized with logical and clearly identified parts; the list of references is quite comprehensive and will be of interest to readers who wish to explore a particular subject in depth. The presentation of the subject material is clear and concise, and the contents are appropriate to postgraduate engineering students, researchers and industrialists alike. The end-of-chapter homework problems are a welcome feature as they provide opportunities for learners to reinforce what they learn by applying theory to problems, many of which are taken from realistic situations. However, it is felt that the book would be more useful, especially to practitioners of fault detection and diagnosis, if a short chapter on background statistical techniques were provided. Joe Au read more read less
1,546 Citations
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Measurement Science and Technology format uses iopart-num citation style.

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SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Measurement Science and Technology guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Measurement Science and Technology citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Measurement Science and Technology's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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After uploading your paper on SciSpace, you would see a button to request a journal submission service for Measurement Science and Technology.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Measurement Science and Technology Endnote style, according to iop-publishing guidelines.

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