Example of IEEE Transactions on Signal Processing format
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Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format
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Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format Example of IEEE Transactions on Signal Processing format
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IEEE Transactions on Signal Processing — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Electrical and Electronic Engineering #43 of 693 down down by 10 ranks
Signal Processing #10 of 108 down down by 4 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1753 Published Papers | 20180 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/07/2020
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Related Journals

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CiteRatio: 3.9
SJR: 0.318
SNIP: 0.926
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CiteRatio: 7.3
SJR: 0.815
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CiteRatio: 5.5
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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.

5.028

4% from 2018

Impact factor for IEEE Transactions on Signal Processing from 2016 - 2019
Year Value
2019 5.028
2018 5.23
2017 4.203
2016 4.3
graph view Graph view
table view Table view

11.5

3% from 2019

CiteRatio for IEEE Transactions on Signal Processing from 2016 - 2020
Year Value
2020 11.5
2019 11.2
2018 9.8
2017 9.4
2016 8.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 3% 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.

1.638

22% from 2019

SJR for IEEE Transactions on Signal Processing from 2016 - 2020
Year Value
2020 1.638
2019 2.098
2018 1.477
2017 1.247
2016 1.385
graph view Graph view
table view Table view

2.451

4% from 2019

SNIP for IEEE Transactions on Signal Processing from 2016 - 2020
Year Value
2020 2.451
2019 2.545
2018 2.632
2017 2.671
2016 2.627
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 22% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 4% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Signal Processing

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IEEE

IEEE Transactions on Signal Processing

The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others,...... Read More

Engineering

i
Last updated on
19 Jul 2020
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ISSN
1053-587X
i
Impact Factor
Very High - 3.259
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
IEEEtran
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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

open accessOpen access Journal Article DOI: 10.1109/78.978374
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
M.S. Arulampalam1, Simon Maskell2, Neil Gordon2, T. Clapp

Abstract:

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as ... Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example. read more read less

Topics:

Particle filter (67%)67% related to the paper, Auxiliary particle filter (63%)63% related to the paper, Monte Carlo localization (59%)59% related to the paper, Extended Kalman filter (58%)58% related to the paper, Kalman filter (56%)56% related to the paper
View PDF
11,409 Citations
Journal Article DOI: 10.1109/78.258082
Matching pursuits with time-frequency dictionaries
Stéphane Mallat1, Zhifeng Zhang1

Abstract:

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal r... The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. They derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992). > read more read less

Topics:

Matching pursuit (62%)62% related to the paper, Basis pursuit (62%)62% related to the paper, K-SVD (60%)60% related to the paper, Basis pursuit denoising (57%)57% related to the paper, Gabor atom (54%)54% related to the paper
9,380 Citations
Journal Article DOI: 10.1109/TSP.2006.881199
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Michal Aharon1, Michael Elad1, Alfred M. Bruckstein1

Abstract:

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regulariza... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method-the K-SVD algorithm-generalizing the K-means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The K-SVD algorithm is flexible and can work with any pursuit method (e.g., basis pursuit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data read more read less

Topics:

K-SVD (76%)76% related to the paper, Sparse approximation (66%)66% related to the paper, Matching pursuit (61%)61% related to the paper, Basis pursuit (58%)58% related to the paper, Vector quantization (52%)52% related to the paper
View PDF
8,905 Citations
open accessOpen access Journal Article DOI: 10.1109/78.650093
Bidirectional recurrent neural networks

Abstract:

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time ... In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported. read more read less

Topics:

Recurrent neural network (59%)59% related to the paper, Posterior probability (52%)52% related to the paper
View PDF
7,290 Citations
Journal Article DOI: 10.1109/78.258085
Embedded image coding using zerotrees of wavelet coefficients
J.M. Shapiro1

Abstract:

The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from t... The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding > read more read less

Topics:

Data compression (62%)62% related to the paper, Set partitioning in hierarchical trees (60%)60% related to the paper, Lossless compression (60%)60% related to the paper, Arithmetic coding (59%)59% related to the paper, Signal compression (57%)57% related to the paper
View PDF
5,559 Citations
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IEEE Transactions on Signal Processing format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on 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 IEEE Transactions on Signal Processing guidelines and auto format it.

2. Do you follow the IEEE Transactions on Signal Processing guidelines?

Yes, the template is compliant with the IEEE Transactions on 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 IEEE Transactions on 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 IEEE Transactions on Signal Processing citation style.

4. Can I use the IEEE Transactions on Signal Processing 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 Transactions on Signal Processing.

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

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

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 Transactions on Signal Processing.

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

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 Transactions on Signal Processing'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 Transactions on Signal Processing'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 Transactions on Signal Processing an online tool or is there a desktop version?

SciSpace's IEEE Transactions on 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.

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

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 Transactions on Signal Processing?”

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

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

12. Is IEEE Transactions on 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 IEEE Transactions on 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 IEEE Transactions on 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 IEEE Transactions on Signal Processing?

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

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 Transactions on Signal Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on 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 IEEE Transactions on Signal Processing 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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