Example of Journal of Signal Processing Systems 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

Journal of Signal Processing Systems — Template for authors

Publisher: Springer

Your data will always be in your control. Learn more

Get started with a free account

— or sign up using email —

See how it works

Guideline source

Categories	Rank	Trend in last 3 yrs
Modeling and Simulation	#135 of 290	up by 3 ranks
Control and Systems Engineering	#122 of 260	up by 4 ranks
Information Systems	#163 of 329	down by 4 ranks
Theoretical Computer Science	#62 of 120	up by 8 ranks
Signal Processing	#58 of 108	-
Hardware and Architecture	#93 of 157	up by 3 ranks

Journal quality:

Good

Last 4 years overview: 419 Published Papers | 1122 Citations

Indexed in: Scopus

Last updated: 11/06/2020

Related journals

Insights

General info

Related Journals

Open Access

Multidimensional Systems and Signal Processing

Springer

Quality:

High

CiteRatio: 4.1

SJR: 0.337

SNIP: 0.919

Open Access

Recommended

Information Fusion

Elsevier

Quality:

High

CiteRatio: 24.9

SJR: 2.776

SNIP: 5.378

Open Access

Journal of Control and Decision

Taylor and Francis

Quality:

High

CiteRatio: 4.9

SJR: 0.686

SNIP: 1.305

Open Access

Information Processing Letters

Elsevier

Quality:

Medium

CiteRatio: 2.3

SJR: 0.415

SNIP: 0.836

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.

1.013

2% from 2018

Impact factor for Journal of Signal Processing Systems from 2016 - 2019
Year	Value
2019	1.013
2018	1.035
2017	1.088
2016	0.893

Graph view

2.7

13% from 2019

CiteRatio for Journal of Signal Processing Systems from 2016 - 2020
Year	Value
2020	2.7
2019	2.4
2018	1.7
2017	1.7
2016	1.6

Graph view

Insights

Impact factor of this journal has decreased by 2% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has increased by 13% 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.

0.276

7% from 2019

SJR for Journal of Signal Processing Systems from 2016 - 2020
Year	Value
2020	0.276
2019	0.298
2018	0.203
2017	0.216
2016	0.212

Graph view

0.681

18% from 2019

SNIP for Journal of Signal Processing Systems from 2016 - 2020
Year	Value
2020	0.681
2019	0.833
2018	0.61
2017	0.632
2016	0.677

Graph view

Insights

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

Insights

SNIP of this journal has decreased by 18% in last years.
This journal’s SNIP is in the top 10 percentile category.

Journal of Signal Processing Systems

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Springer

Journal of Signal Processing Systems

The Journal of Signal Processing Systems for Signal, Image, and Video Technology publishes research, survey and short papers on the design and implementation of signal processing systems, with or without VLSI circuits. The journal is published in twelve issues and is distributed to engineers, researchers, and educators in the general field of signal processing systems. The key subject areas of interest to the journal are: System Design and Implementation • system design methodologies • SoC Designs • VLSI and other advanced technologies • system performance analysis (including measurement, modeling, and simulation) of signal processing systems Algorithms • DSP algorithms • Image, video, and multimedia processing • pattern recognition algorithms • machine learning techniques Architectures • FPGA • design of arithmetic circuits and VLSI components used in signal processing (such as multipliers, dividers, and digital filter sections) • specialized number systems (including on-line arithmetic, residue number systems, and logarithmic number systems) • embedded architectures and systems • multi-processor architectures • parallel, pipelined, and array processors • multi-core signal processors • generic and programmable processors • special purpose signal processor architectures algorithms and architecture co-design Applications • acoustics/speech/image/video/multimedia • internet and wireless security • biometric recognition • wireless communication and sensor networks • genomic signal processing The Journal of Signal Processing Systems for Signal, Image, and Video Technology considers original, peer-reviewed content as well as high quality workshop, symposium, and conference papers with an emphasis on system design. Among other areas, the journal provides an excellent forum for special issues on applications, algorithms, and architectures of timely interest. There is no page charge for authors. Authors benefit from Online FirstTM Publication ? in which articles are available and citable online by their DOI (Digital Object Identifier) in advance of print publication. This ensures a fast time-to-publication for accepted papers and enables a wide electronic readership for the journal content. Read Less

Engineering

Last updated on	11 Jun 2020
ISSN	1939-8018
Impact Factor	Medium - 0.891
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	SPBASIC
Citation Type	Author Year (Blonder et al, 1982)
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: 10.1007/S11265-018-1378-3 •

A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier

Levent Eren¹, Turker Ince¹, •

01 Feb 2019 - Journal of Signal Processing Systems

Abstract:

Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the lit... Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many studies have developed highly accurate algorithms for detecting bearing faults, their results have generally been limited to relatively small train/test data sets. As opposed to conventional intelligent fault diagnosis systems that usually encapsulate feature extraction, feature selection and classification as distinct blocks, the proposed system takes directly raw time-series sensor data as input and it can efficiently learn optimal features with the proper training. The main advantages of the 1D CNN based approach are 1) its compact architecture configuration (rather than the complex deep architectures) which performs only 1D convolutions making it suitable for real-time fault detection and monitoring, 2) its cost effective and practical real-time hardware implementation, 3) its ability to work without any pre-determined transformation (such as FFT or DWT), hand-crafted feature extraction and feature selection, and 4) its capability to provide efficient training of the classifier with limited size of training data set and limited number of BP iterations. Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods. read more

Topics:

Fault detection and isolation (64%)64% related to the paper, Intelligent decision support system (54%)54% related to the paper, Feature extraction (53%)53% related to the paper,

362 Citations

Journal Article • DOI: 10.1007/S11265-019-01508-Y •

Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features

Xiong Zeng-gang¹, Tang Zhiwen¹, •

01 Mar 2021 - Journal of Signal Processing Systems

Abstract:

With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retri... With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retrieved, its retrieval efficiency still cannot meet people’s needs. In this paper, we propose an image retrieval algorithm based on the combination of color and shape features. The cumulative histogram method is used to calculate the color features of the image, and 7 Hu invariant moments are calculated as shape features. The color and shape features are combined with certain weights, and the Euclidean distance is used as the similarity measure. Finally, the image is retrieved, and the related experiments are passed. By comparing with related experiments, the algorithm effectively improves the accuracy of image retrieval. read more

Topics:

Image retrieval (67%)67% related to the paper, Similarity measure (54%)54% related to the paper

91 Citations

Open access • Journal Article • DOI: 10.1007/S11265-018-1357-8 •

Finding Maximum Cliques on the D-Wave Quantum Annealer

Guillaume Chapuis¹, Hristo N. Djidjev¹, •

01 Mar 2019 - Journal of Signal Processing Systems

Abstract:

This paper assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms inte... This paper assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, and compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches read more

Topics:

Clique problem (60%)60% related to the paper, Quantum annealing (58%)58% related to the paper, Quadratic unconstrained binary optimization (57%)57% related to the paper,

View PDF

81 Citations

Journal Article • DOI: 10.1007/S11265-020-01610-6 •

An Equivalent Exchange Based Data Forwarding Incentive Scheme for Socially Aware Networks

Zenggang Xiong¹, Nan Xiao¹, •

01 Mar 2021 - Journal of Signal Processing Systems

Abstract:

As nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. It is main that me... As nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. It is main that messages forwarding will be abstracted into a transaction in EEIS. The buyer and seller respectively make a price about the message according to its own resource state and negotiate twice the pricing both side until they agree, then the buyer will send the message and pay a certain virtual currency to the seller. Otherwise, the next message will continue to be traded. Meanwhile, both parties’ resource status, wealth status and the price of messages must be open and transparent to prevent the nodes from making false pricing during the transaction. Ultimately, the experimental results show the delivery ratio about messages is improved significantly and verify the effectiveness of EEIS. read more

65 Citations

Journal Article • DOI: 10.1007/S11265-019-01461-W •

Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

Wang Daichao¹, Guo Qingwen¹, •

01 Oct 2019 - Journal of Signal Processing Systems

Abstract:

With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, a... With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements. read more

Topics:

Deep learning (59%)59% related to the paper, Feature extraction (57%)57% related to the paper, Artificial neural network (57%)57% related to the paper,

56 Citations

Also popular among researchers

“

SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds

Choose a template

Select a template from a library of 40,000+ templates

Import a MS-Word file or start fresh

It takes only few seconds to import

View and edit your final output

SciSpace will automatically format your output to meet journal guidelines

Submit directly or Download

Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

With SciSpace, you do not need a word template for Journal of Signal Processing Systems.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

Easy support from all your favorite tools

Journal of Signal Processing Systems format uses SPBASIC 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 Journal of Signal Processing Systems in LaTeX?

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

2. Do you follow the Journal of Signal Processing Systems guidelines?

Yes, the template is compliant with the Journal of Signal Processing Systems 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 Journal of Signal Processing Systems?

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 Journal of Signal Processing Systems citation style.

4. Can I use the Journal of Signal Processing Systems 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 Journal of Signal Processing Systems.

5. Can I use a manuscript in Journal of Signal Processing Systems 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 Journal of Signal Processing Systems that you can download at the end.

6. How long does it usually take you to format my papers in Journal of Signal Processing Systems?

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

7. Where can I find the template for the Journal of Signal Processing Systems?

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 Journal of Signal Processing Systems'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 Journal of Signal Processing Systems'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. Journal of Signal Processing Systems an online tool or is there a desktop version?

SciSpace's Journal of Signal Processing Systems 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 Journal of Signal Processing Systems?

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 Journal of Signal Processing Systems?”

11. What is the output that I would get after using Journal of Signal Processing Systems?

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

12. Is Journal of Signal Processing Systems'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 Journal of Signal Processing Systems?

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 Journal of Signal Processing Systems. 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:

Pre-prints as being the version of the paper before peer review and
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 Journal of Signal Processing Systems?

The 5 most common citation types in order of usage for Journal of Signal Processing Systems 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 Journal of Signal Processing Systems?

16. Can I download Journal of Signal Processing Systems 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 Journal of Signal Processing Systems Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to Journal of Signal Processing Systems formatting guidelines and citation style.

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

“

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.

”

Andreas Frutiger
Researcher & Ex MS Word user

Journal of Signal Processing Systems — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

1.013

2.7

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

0.276

0.681

Insights

Insights

Journal of Signal Processing Systems

Journal of Signal Processing Systems

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Abstract:

Topics:

What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

Freedom from formatting guidelines

Easy support from all your favorite tools

Frequently asked questions

Fast and reliable, built for complaince.

No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
built for complaince.