Example of Sensors format
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Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format
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Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format Example of Sensors format
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Sensors — Template for authors

Categories Rank Trend in last 3 yrs
Instrumentation #13 of 128 up up by 7 ranks
Electrical and Electronic Engineering #135 of 693 down down by 4 ranks
Information Systems #69 of 329 down down by None rank
Atomic and Molecular Physics, and Optics #42 of 192 up up by 2 ranks
Analytical Chemistry #29 of 122 up up by 9 ranks
Biochemistry #133 of 415 up up by 53 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 19986 Published Papers | 116263 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 04/01/2021
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Related Journals

open access Open Access

Elsevier

Quality:  
High
CiteRatio: 4.2
SJR: 0.565
SNIP: 1.02
open access Open Access

IEEE

Quality:  
High
CiteRatio: 5.5
SJR: 0.81
SNIP: 1.008
open access Open Access

Springer

Quality:  
High
CiteRatio: 6.6
SJR: 1.392
SNIP: 1.036
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.3
SJR: 0.633
SNIP: 1.433

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.

3.275

8% from 2018

Impact factor for Sensors from 2016 - 2019
Year Value
2019 3.275
2018 3.031
2017 2.475
2016 2.677
graph view Graph view
table view Table view

5.8

16% from 2019

CiteRatio for Sensors from 2016 - 2020
Year Value
2020 5.8
2019 5.0
2018 4.3
2017 4.3
2016 4.1
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 8% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 16% 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.

0.636

3% from 2019

SJR for Sensors from 2016 - 2020
Year Value
2020 0.636
2019 0.653
2018 0.592
2017 0.584
2016 0.623
graph view Graph view
table view Table view

1.555

2% from 2019

SNIP for Sensors from 2016 - 2020
Year Value
2020 1.555
2019 1.586
2018 1.642
2017 1.593
2016 1.629
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 3% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Sensors

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Multidisciplinary Digital Publishing Institute

Sensors

Approved by publishing and review experts on SciSpace, this template is built as per for Sensors formatting guidelines as mentioned in Multidisciplinary Digital Publishing Institute author instructions. The current version was created on 04 Jan 2021 and has been used by 919 authors to write and format their manuscripts to this journal.

Electrochemical sensors

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Last updated on
04 Jan 2021
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ISSN
1424-8220
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
MDPI Custom Citation
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E.; Tinkham, M.; Klapwijk, T.M. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 1982, 25, 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.3390/S100302088
Metal oxide gas sensors: Sensitivity and influencing factors
Cheng-Xiang Wang1, Longwei Yin, Luyuan Zhang, Dong Xiang, Rui Gao
15 Mar 2010 - Sensors

Abstract:

Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, wil... Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, will change with the factors influencing the surface reactions, such as chemical components, surface-modification and microstructures of sensing layers, temperature and humidity. In this brief review, attention will be focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above. read more read less

Topics:

Oxide (53%)53% related to the paper
2,122 Citations
open accessOpen access Journal Article DOI: 10.3390/S16010115
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Fco. Javier Ordóñez1, Daniel Roggen1
18 Jan 2016 - Sensors

Abstract:

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor m... Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. read more read less

Topics:

Deep learning (62%)62% related to the paper, Convolutional neural network (58%)58% related to the paper, Recurrent neural network (57%)57% related to the paper, Artificial neural network (53%)53% related to the paper, Activity recognition (53%)53% related to the paper
1,896 Citations
open accessOpen access Journal Article DOI: 10.3390/S120201437
Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications
Kourosh Khoshelham1, Sander Oude Elberink2
01 Feb 2012 - Sensors

Abstract:

Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, ... Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements. read more read less

Topics:

Measured depth (54%)54% related to the paper
View PDF
1,671 Citations
open accessOpen access Journal Article DOI: 10.3390/S18103337
SECOND: Sparsely Embedded Convolutional Detection
Yan Yan1, Yuxing Mao1, Bo Li
06 Oct 2018 - Sensors

Abstract:

LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference ... LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed. read more read less

Topics:

Object detection (59%)59% related to the paper, Convolutional neural network (54%)54% related to the paper, Orientation (computer vision) (51%)51% related to the paper, Inference (51%)51% related to the paper
1,624 Citations
open accessOpen access Journal Article DOI: 10.3390/S140711957
Wearable Electronics and Smart Textiles: A Critical Review
Matteo Stoppa1, Alessandro Chiolerio1
07 Jul 2014 - Sensors

Abstract:

Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and... Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy. read more read less

Topics:

E-textiles (63%)63% related to the paper, Wearable technology (56%)56% related to the paper, Context awareness (54%)54% related to the paper, Wearable computer (54%)54% related to the paper, Flexibility (engineering) (54%)54% related to the paper
View PDF
1,576 Citations
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Time taken to format a paper and Compliance with guidelines

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Sensors format uses MDPI Custom Citation 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 Sensors in LaTeX?

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

2. Do you follow the Sensors guidelines?

Yes, the template is compliant with the Sensors 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 Sensors?

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 Sensors citation style.

4. Can I use the Sensors 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 Sensors.

5. Can I use a manuscript in Sensors 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 Sensors that you can download at the end.

6. How long does it usually take you to format my papers in Sensors?

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

7. Where can I find the template for the Sensors?

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 Sensors'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 Sensors'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. Sensors an online tool or is there a desktop version?

SciSpace's Sensors 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 Sensors?

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 Sensors?”

11. What is the output that I would get after using Sensors?

After writing your paper autoformatting in Sensors, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Sensors'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 Sensors?

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 Sensors. 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 Sensors?

The 5 most common citation types in order of usage for Sensors 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 Sensors?

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

16. Can I download Sensors 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 Sensors 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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