Example of Multidimensional Systems and Signal Processing format
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Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format Example of Multidimensional Systems and Signal Processing format
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open access Open Access

Multidimensional Systems and Signal Processing — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Applied Mathematics #88 of 548 up up by 8 ranks
Computer Science Applications #221 of 693 down down by 9 ranks
Information Systems #107 of 329 down down by 6 ranks
Signal Processing #39 of 108 up up by 4 ranks
Hardware and Architecture #63 of 157 down down by 6 ranks
Artificial Intelligence #94 of 227 down down by 18 ranks
Software #162 of 389 up up by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 361 Published Papers | 1463 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

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.

A measure of average citations received per peer-reviewed paper published in the journal.

1.81

23% from 2018

Impact factor for Multidimensional Systems and Signal Processing from 2016 - 2019
Year Value
2019 1.81
2018 2.338
2017 2.088
2016 1.365
graph view Graph view
table view Table view

4.1

14% from 2019

CiteRatio for Multidimensional Systems and Signal Processing from 2016 - 2020
Year Value
2020 4.1
2019 3.6
2018 3.4
2017 3.0
2016 3.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 23% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 14% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.337

13% from 2019

SJR for Multidimensional Systems and Signal Processing from 2016 - 2020
Year Value
2020 0.337
2019 0.388
2018 0.507
2017 0.494
2016 0.424
graph view Graph view
table view Table view

0.919

7% from 2019

SNIP for Multidimensional Systems and Signal Processing from 2016 - 2020
Year Value
2020 0.919
2019 0.988
2018 1.239
2017 1.27
2016 1.109
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.

insights Insights

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

Multidimensional Systems and Signal Processing

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Springer

Multidimensional Systems and Signal Processing

Multidimensional Systems and Signal Processing is an archival, peer-reviewed, technical journal publishing survey and original papers, spanning fundamentals as well as applicable research contributions.While the subject of multidimensional systems is concerned with mathematica...... Read More

Mathematics

i
Last updated on
09 Jun 2020
i
ISSN
0923-6082
i
Impact Factor
High - 1.607
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
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1023/A:1008494815252
Analysis of Linear Iterative Learning Control Schemes -A 2D Systems/Repetitive Processes Approach
David H. Owens1, N. Amann2, Eric Rogers3, Mark French3

Abstract:

This paper first develops results on the stability and convergence properties of a general class of iterative learning control schemes using, in the main, theory first developed for the branch of 2D linear systems known as linear repetitive processes. A general learning law that uses information from the current and a finite ... This paper first develops results on the stability and convergence properties of a general class of iterative learning control schemes using, in the main, theory first developed for the branch of 2D linear systems known as linear repetitive processes. A general learning law that uses information from the current and a finite number of previous trials is considered and the results, in the form of fundamental limitations on the benefits of using this law, are interpreted in terms of basic systems theoretic concepts such as the relative degree and minimum phase characteristics of the example under consideration. Following this, previously reported powerful 2D predictive and adaptive control algorithms are reviewed. Finally, new iterative adaptive learning control laws which solve iterative learning control algorithms under weak assumptions are developed. read more read less

Topics:

Iterative learning control (67%)67% related to the paper, Stability (learning theory) (60%)60% related to the paper, Adaptive control (60%)60% related to the paper, Linear system (56%)56% related to the paper
148 Citations
Journal Article DOI: 10.1007/S11045-007-0022-3
Super-resolution reconstruction in a computational compound-eye imaging system
Wai-San Chan1, Edmund Y. Lam1, Michael K. Ng2, Giuseppe Y. Mak1

Abstract:

From consumer electronics to biomedical applications, device miniaturization has shown to be highly desirable. This often includes reducing the size of some optical systems. However, diffraction effects impose a constraint on image quality when we simply scale down the imaging parameters. Over the past few years, compound-eye... From consumer electronics to biomedical applications, device miniaturization has shown to be highly desirable. This often includes reducing the size of some optical systems. However, diffraction effects impose a constraint on image quality when we simply scale down the imaging parameters. Over the past few years, compound-eye imaging system has emerged as a promising architecture in the development of compact visual systems. Because multiple low-resolution (LR) sub-images are captured, post-processing algorithms for the reconstruction of a high-resolution (HR) final image from the LR images play a critical role in affecting the image quality. In this paper, we describe and investigate the performance of a compound-eye system recently reported in the literature. We discuss both the physical construction and the mathematical model of the imaging components, followed by an application of our super-resolution algorithm in reconstructing the image. We then explore several variations of the imaging system, such as the incorporation of a phase mask in extending the depth of field, which are not possible with a traditional camera. Simulations with a versatile virtual camera system that we have built verify the feasibility of these additions, and we also report the tolerance of the compound-eye system to variations in physical parameters, such as optical aberrations, that are inevitable in actual systems. read more read less

Topics:

Image quality (59%)59% related to the paper, Compound eye (55%)55% related to the paper, Depth of field (51%)51% related to the paper
123 Citations
Journal Article DOI: 10.1007/S11045-012-0178-3
A perceptual metric for stereoscopic image quality assessment based on the binocular energy
Rafik Bensalma1, Mohamed-Chaker Larabi1

Abstract:

Stereoscopic imaging is becoming very popular and its deployment by means of photography, television, cinema. . .is rapidly increasing. Obviously, the access to this type of images imposes the use of compression and transmission that may generate artifacts of different natures. Consequently, it is important to have appropriat... Stereoscopic imaging is becoming very popular and its deployment by means of photography, television, cinema. . .is rapidly increasing. Obviously, the access to this type of images imposes the use of compression and transmission that may generate artifacts of different natures. Consequently, it is important to have appropriate tools to measure the quality of stereoscopic content. Several studies tried to extend well-known metrics, such as the PSNR or SSIM, to 3D. However, the results are not as good as for 2D images and it becomes important to have metrics dealing with 3D perception. In this work, we propose a full reference metric for quality assessment of stereoscopic images based on the binocular fusion process characterizing the 3D human perception. The main idea consists of the development of a model allowing to reproduce the binocular signal generated by simple and complex cells, and to estimate the associated binocular energy. The difference of binocular energy has shown a high correlation with the human judgement for different impairments and is used to build the Binocular Energy Quality Metric (BEQM). Extensive experiments demonstrated the performance of the BEQM with regards to literature. read more read less

Topics:

Stereoscopy (60%)60% related to the paper, Image quality (51%)51% related to the paper, Metric (mathematics) (50%)50% related to the paper
123 Citations
Journal Article DOI: 10.1023/B:MULT.0000017024.66297.A0
Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters

Abstract:

This paper presents a new filtering approach for impulsive noise removal in color images. Incorporating the nonnegative integer weight corresponding to the central sample into the structure of the basic vector directional filter (BVDF), the proposed framework constitutes a class of center-weighted vector directional filters (... This paper presents a new filtering approach for impulsive noise removal in color images. Incorporating the nonnegative integer weight corresponding to the central sample into the structure of the basic vector directional filter (BVDF), the proposed framework constitutes a class of center-weighted vector directional filters (CWVDF). It can be easily observed that the CWVDF filters are computationally efficient and extend design flexibility of the standard BVDF scheme. By varying the center weight, the proposed CWVDF framework can provide the smoothing characteristics ranging from an identity operation to that of the BVDF. Therefore, design characteristics relate to the CWVDF, which removes impulses and outliers from the image while simultaneously preserving the structural information. To adaptively determine the optimal value of the center weight, two adaptive approaches based on the angular thresholds are provided. Both techniques achieve excellent results in terms of the commonly used objective image quality criteria and significantly outperform standard multichannel filtering algorithms. read more read less

Topics:

Median filter (58%)58% related to the paper, Filter (signal processing) (58%)58% related to the paper, Smoothing (56%)56% related to the paper, Image quality (54%)54% related to the paper
123 Citations
open accessOpen access Journal Article DOI: 10.1007/S11045-013-0269-9
Tucker factorization with missing data with application to low-$$n$$n-rank tensor completion
Marko Filipović, Ante Jukic1

Abstract:

The problem of tensor completion arises often in signal processing and machine learning. It consists of recovering a tensor from a subset of its entries. The usual structural assumption on a tensor that makes the problem well posed is that the tensor has low rank in every mode. Several tensor completion methods based on minim... The problem of tensor completion arises often in signal processing and machine learning. It consists of recovering a tensor from a subset of its entries. The usual structural assumption on a tensor that makes the problem well posed is that the tensor has low rank in every mode. Several tensor completion methods based on minimization of nuclear norm, which is the closest convex approximation of rank, have been proposed recently, with applications mostly in image inpainting problems. It is often stated in these papers that methods based on Tucker factorization perform poorly when the true ranks are unknown. In this paper, we propose a simple algorithm for Tucker factorization of a tensor with missing data and its application to low-$$n$$n-rank tensor completion. The algorithm is similar to previously proposed method for PARAFAC decomposition with missing data. We demonstrate in several numerical experiments that the proposed algorithm performs well even when the ranks are significantly overestimated. Approximate reconstruction can be obtained when the ranks are underestimated. The algorithm outperforms nuclear norm minimization methods when the fraction of known elements of a tensor is low. read more read less

Topics:

Tensor (intrinsic definition) (63%)63% related to the paper, Rank (linear algebra) (61%)61% related to the paper, Missing data (53%)53% related to the paper, Multilinear subspace learning (52%)52% related to the paper, Matrix norm (52%)52% related to the paper
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102 Citations
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Frequently asked questions

1. Can I write Multidimensional Systems and Signal Processing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Multidimensional Systems and Signal Processing guidelines and auto format it.

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Yes, the template is compliant with the Multidimensional Systems and Signal Processing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Multidimensional Systems and Signal Processing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Multidimensional Systems and Signal Processing citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Multidimensional Systems and Signal Processing.

5. Can I use a manuscript in Multidimensional Systems and Signal Processing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Multidimensional Systems and Signal Processing that you can download at the end.

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SciSpace's Multidimensional Systems and Signal Processing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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12. Is Multidimensional Systems and Signal Processing's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Multidimensional Systems and Signal Processing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Multidimensional Systems and Signal Processing. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In Multidimensional Systems and Signal Processing?

The 5 most common citation types in order of usage for Multidimensional Systems and Signal Processing are:.

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

15. How do I submit my article to the Multidimensional Systems and Signal Processing?

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16. Can I download Multidimensional Systems and Signal Processing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Multidimensional Systems and Signal Processing Endnote style according to Elsevier guidelines.

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