Example of IEEE Signal Processing Letters format
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Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format
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Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format Example of IEEE Signal Processing Letters format
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open access Open Access ISSN: 10709908 e-ISSN: 15582361
recommended Recommended

IEEE Signal Processing Letters — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Applied Mathematics #18 of 548 -
Electrical and Electronic Engineering #94 of 693 down down by 16 ranks
Signal Processing #17 of 108 down down by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1466 Published Papers | 10756 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/07/2020
Insights & related journals
General info
Top papers
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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.

3.105

5% from 2018

Impact factor for IEEE Signal Processing Letters from 2016 - 2019
Year Value
2019 3.105
2018 3.268
2017 2.813
2016 2.528
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 5% 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.

7.3

1% from 2019

CiteRatio for IEEE Signal Processing Letters from 2016 - 2020
Year Value
2020 7.3
2019 7.4
2018 7.0
2017 6.0
2016 5.3
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 1% 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.

0.815

29% from 2019

SJR for IEEE Signal Processing Letters from 2016 - 2020
Year Value
2020 0.815
2019 1.145
2018 0.785
2017 0.732
2016 0.798
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 29% 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.

1.797

14% from 2019

SNIP for IEEE Signal Processing Letters from 2016 - 2020
Year Value
2020 1.797
2019 2.085
2018 1.983
2017 1.798
2016 1.806
graph view Graph view
table view Table view

insights Insights

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

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CiteRatio: 10.9 | SJR: 1.365 | SNIP: 2.086
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IEEE Signal Processing Letters

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IEEE

IEEE Signal Processing Letters

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.... Read More

Mathematics

i
Last updated on
11 Jul 2020
i
ISSN
1070-9908
i
Impact Factor
High - 2.218
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
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/97.995823
A universal image quality index
Zhou Wang, Alan C. Bovik1

Abstract:

We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance d... We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html. read more read less

Topics:

Distortion (62%)62% related to the paper, Image quality (61%)61% related to the paper, Image processing (57%)57% related to the paper, Human visual system model (53%)53% related to the paper, Mean squared error (52%)52% related to the paper
4,687 Citations
open accessOpen access Journal Article DOI: 10.1109/LSP.2016.2603342
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Kaipeng Zhang1, Zhanpeng Zhang2, Zhifeng Li1, Yu Qiao1

Abstract:

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent corre... Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging face detection dataset and benchmark and WIDER FACE benchmarks for face detection, and annotated facial landmarks in the wild benchmark for face alignment, while keeps real-time performance. read more read less

Topics:

Face detection (64%)64% related to the paper, Deep learning (53%)53% related to the paper
2,972 Citations
open accessOpen access Journal Article DOI: 10.1109/LSP.2012.2227726
Making a “Completely Blind” Image Quality Analyzer
Anish Mittal1, Rajiv Soundararajan1, Alan C. Bovik1

Abstract:

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require kno... An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip. read more read less

Topics:

Image quality (52%)52% related to the paper, Image processing (52%)52% related to the paper
View PDF
2,222 Citations
open accessOpen access Journal Article DOI: 10.1109/LSP.2003.821662
Empirical mode decomposition as a filter bank
Patrick Flandrin1, Gabriel Rilling1, Paulo Gonçalves

Abstract:

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on num... Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent. read more read less

Topics:

Filter bank (56%)56% related to the paper, Gaussian noise (56%)56% related to the paper, Hurst exponent (53%)53% related to the paper, Hilbert–Huang transform (51%)51% related to the paper, Wavelet (51%)51% related to the paper
View PDF
2,100 Citations
open accessOpen access Journal Article DOI: 10.1109/LSP.2014.2343971
On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users
Zhiguo Ding1, Zheng Yang2, Pingzhi Fan2, H. Vincent Poor1

Abstract:

In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on th... In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In particular, a wrong choice of the targeted data rates and allocated power can lead to a situation in which the user's outage probability is always one, i.e. the user's targeted quality of service will never be met. read more read less

Topics:

Noma (50%)50% related to the paper
View PDF
1,603 Citations
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IEEE Signal Processing Letters format uses IEEEtran citation style.

Automatically format and order your citations and bibliography in a click.

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 IEEE Signal Processing Letters 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 IEEE Signal Processing Letters 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 IEEE Signal Processing Letters'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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for IEEE Signal Processing Letters.

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 IEEE Signal Processing Letters Endnote style, according to ieee guidelines.

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