Example of IEEE Signal Processing Magazine format
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Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format
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Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format Example of IEEE Signal Processing Magazine format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

IEEE Signal Processing Magazine — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Applied Mathematics #3 of 548 -
Electrical and Electronic Engineering #10 of 693 down down by 5 ranks
Signal Processing #3 of 108 down down by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 319 Published Papers | 6006 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/07/2020
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Related Journals

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IEEE

Quality:  
High
CiteRatio: 7.3
SJR: 0.815
SNIP: 1.797
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IEEE

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CiteRatio: 10.9
SJR: 1.365
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Quality:  
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CiteRatio: 15.2
SJR: 2.01
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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.

11.35

49% from 2018

Impact factor for IEEE Signal Processing Magazine from 2016 - 2019
Year Value
2019 11.35
2018 7.602
2017 7.451
2016 9.654
graph view Graph view
table view Table view

18.8

31% from 2019

CiteRatio for IEEE Signal Processing Magazine from 2016 - 2020
Year Value
2020 18.8
2019 14.3
2018 13.4
2017 16.9
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 49% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 31% 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.

2.058

17% from 2019

SJR for IEEE Signal Processing Magazine from 2017 - 2020
Year Value
2020 2.058
2019 2.479
2018 1.364
2017 1.747
graph view Graph view
table view Table view

5.373

10% from 2019

SNIP for IEEE Signal Processing Magazine from 2017 - 2020
Year Value
2020 5.373
2019 4.903
2018 4.582
2017 5.075
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 17% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 10% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
IEEE Signal Processing Magazine

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IEEE

IEEE Signal Processing Magazine

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest. Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional f...... Read More

Mathematics

i
Last updated on
11 Jul 2020
i
ISSN
1053-5888
i
Impact Factor
Very High - 4.906
i
Open Access
No
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/MSP.2007.914731
An Introduction To Compressive Sampling
Emmanuel J. Candès1, Michael B. Wakin1

Abstract:

Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the... Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. read more read less

Topics:

Nyquist frequency (66%)66% related to the paper, Oversampling (64%)64% related to the paper, Nyquist–Shannon sampling theorem (62%)62% related to the paper, Nyquist rate (62%)62% related to the paper, Sampling (signal processing) (61%)61% related to the paper
9,686 Citations
Journal Article DOI: 10.1109/MSP.2012.2205597
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

Abstract:

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the... Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition. read more read less

Topics:

Acoustic model (62%)62% related to the paper, Time delay neural network (60%)60% related to the paper, FMLLR (60%)60% related to the paper, Hidden Markov model (57%)57% related to the paper, Artificial neural network (53%)53% related to the paper
9,091 Citations
open accessOpen access Journal Article DOI: 10.1109/MSP.2011.2178495
Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

Abstract:

Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, a... Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2]. read more read less

Topics:

MIMO (61%)61% related to the paper, Many antennas (56%)56% related to the paper, Bell Laboratories Layered Space-Time (53%)53% related to the paper, Transmitter (52%)52% related to the paper, Base station (50%)50% related to the paper
View PDF
5,158 Citations
Journal Article DOI: 10.1109/79.526899
Two decades of array signal processing research: the parametric approach
Hamid Krim1, Mats Viberg2

Abstract:

The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signa... The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example. read more read less

Topics:

Array processing (67%)67% related to the paper, Sensor array (63%)63% related to the paper, Beamforming (56%)56% related to the paper, Direction of arrival (56%)56% related to the paper, Parametric statistics (54%)54% related to the paper
4,410 Citations
Journal Article DOI: 10.1109/MSP.2007.4286571
Compressive Sensing [Lecture Notes]
Richard G. Baraniuk1

Abstract:

This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate. This method, called compressive sensing, employs nonadaptive linear projections that preserve the structure of the signal; the signal is then reconstructed from these projections using an optim... This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate. This method, called compressive sensing, employs nonadaptive linear projections that preserve the structure of the signal; the signal is then reconstructed from these projections using an optimization process. read more read less

Topics:

Nyquist rate (55%)55% related to the paper, Nyquist stability criterion (52%)52% related to the paper, Signal reconstruction (51%)51% related to the paper, Compressed sensing (51%)51% related to the paper
3,555 Citations
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IEEE Signal Processing Magazine 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

1. Can I write IEEE Signal Processing Magazine in LaTeX?

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

2. Do you follow the IEEE Signal Processing Magazine guidelines?

Yes, the template is compliant with the IEEE Signal Processing Magazine 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 IEEE Signal Processing Magazine?

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 IEEE Signal Processing Magazine citation style.

4. Can I use the IEEE Signal Processing Magazine templates for free?

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 IEEE Signal Processing Magazine.

5. Can I use a manuscript in IEEE Signal Processing Magazine 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 IEEE Signal Processing Magazine that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Signal Processing Magazine?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Signal Processing Magazine.

7. Where can I find the template for the IEEE Signal Processing Magazine?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Signal Processing Magazine's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IEEE Signal Processing Magazine's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IEEE Signal Processing Magazine an online tool or is there a desktop version?

SciSpace's IEEE Signal Processing Magazine 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.

10. I cannot find my template in your gallery. Can you create it for me like IEEE Signal Processing Magazine?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like IEEE Signal Processing Magazine?”

11. What is the output that I would get after using IEEE Signal Processing Magazine?

After writing your paper autoformatting in IEEE Signal Processing Magazine, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Signal Processing Magazine'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 IEEE Signal Processing Magazine?

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 IEEE Signal Processing Magazine. 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 IEEE Signal Processing Magazine?

The 5 most common citation types in order of usage for IEEE Signal Processing Magazine 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 IEEE Signal Processing Magazine?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Signal Processing Magazine's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Signal Processing Magazine 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 IEEE Signal Processing Magazine Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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