Example of Artificial Life and Robotics format
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Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format
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Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format Example of Artificial Life and Robotics format
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open access Open Access

Artificial Life and Robotics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Biochemistry, Genetics and Molecular Biology (all) #131 of 204 up up by 14 ranks
Artificial Intelligence #175 of 227 down down by 27 ranks
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 314 Published Papers | 470 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 21/06/2020
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SNIP: 0.851
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

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.

1.5

CiteRatio for Artificial Life and Robotics from 2016 - 2020
Year Value
2020 1.5
2019 1.5
2018 1.0
2017 0.7
2016 0.6
graph view Graph view
table view Table view

0.247

9% from 2019

SJR for Artificial Life and Robotics from 2016 - 2020
Year Value
2020 0.247
2019 0.27
2018 0.236
2017 0.226
2016 0.15
graph view Graph view
table view Table view

0.756

28% from 2019

SNIP for Artificial Life and Robotics from 2016 - 2020
Year Value
2020 0.756
2019 0.591
2018 0.5
2017 0.524
2016 0.301
graph view Graph view
table view Table view

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 28% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Artificial Life and Robotics

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Springer

Artificial Life and Robotics

Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the tw...... Read More

Biochemistry, Genetics and Molecular Biology

i
Last updated on
21 Jun 2020
i
ISSN
1433-5298
i
Impact Factor
Low - 0.394
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
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
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

open accessOpen access Journal Article DOI: 10.1007/S10015-017-0422-X
Deep recurrent neural network for mobile human activity recognition with high throughput
Masaya Inoue1, Sozo Inoue1, Takeshi Nishida1

Abstract:

In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The “high throughput” refers to short time at a time of recognition. We... In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The “high throughput” refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 ms, while the best traditional method required 11.031 ms which includes 11.027 ms for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN. read more read less

Topics:

Recurrent neural network (54%)54% related to the paper, Throughput (business) (53%)53% related to the paper, Feature (machine learning) (52%)52% related to the paper
184 Citations
Journal Article DOI: 10.1007/BF02480880
Children, robotics, and education
Jeff Johnson1

Abstract:

Popular interest in robotics has increased astonishingly in the last few years. Robotics is seen by many as offering major new benefits in education at all levels. Before rushing to exploit this popularity, educators should ask serious questions about the universality and longevity of the robotics phenomenon. Is it a fashion?... Popular interest in robotics has increased astonishingly in the last few years. Robotics is seen by many as offering major new benefits in education at all levels. Before rushing to exploit this popularity, educators should ask serious questions about the universality and longevity of the robotics phenomenon. Is it a fashion? To be useful, the energy released by robotics must be sustained and universal, and the means of exploiting it must be systematic. Universities define their own robotics curriculum, but most schools lack both the resources and the freedom to do this, and must work with a national curriculum. If it can be shown that robotics has sustained potential in education, it seems inevitable that new ways need to be found to integrate it into the school curriculum. read more read less

Topics:

Educational robotics (66%)66% related to the paper, Developmental robotics (59%)59% related to the paper, National curriculum (54%)54% related to the paper, Curriculum (53%)53% related to the paper, Robotics (50%)50% related to the paper
163 Citations
Journal Article DOI: 10.1007/S10015-005-0350-Z
Playware technology for physically activating play
Henrik Hautop Lund1, Thomas Klitbo, Carsten Jessen2

Abstract:

We present and use the robotic building block concept to create playware. Playware is the use of intelligent technology to create the kinds of leisure activity we normally label play, i.e., intelligent hard- and software that aims at producing play and playful experiences among users. The technological concept of physical bui... We present and use the robotic building block concept to create playware. Playware is the use of intelligent technology to create the kinds of leisure activity we normally label play, i.e., intelligent hard- and software that aims at producing play and playful experiences among users. The technological concept of physical building blocks with processing, input, and output (including communication) is derived from embodied artificial intelligence that emphasizes the role of interplay between morphology and control. We exemplify the building block concept with the tangible tiles that we created as components for a new kind of playground on which children can experience immediate feedback on their motions. Hence, this kind of playground allows the implementation of games and plays that demand physical activity amongst the users, and thereby contribute as a new tool in the fight against obesity. The tangible tiles are homogenous building blocks, which gives assembly, substitution, and production advantages. However, we may also create a system of heterogeneous building blocks, e.g., by adding special-purpose tiles such as loud-speaker tiles. We performed tests with the tangible tiles placed at a school for 2 months' continuous use. read more read less
145 Citations
Journal Article DOI: 10.1007/BF02471106
Feature extraction from EEGs associated with emotions

Abstract:

The state of mind of a person is supported by the brain activity, and hence features of the state of mind appear in the scalp potentials, as seen on an electroencephalogram (EEG). The EEG features have been extracted into a set of 135 state variables of cross-correlation coefficients of EEGs collected with ten scalp electrode... The state of mind of a person is supported by the brain activity, and hence features of the state of mind appear in the scalp potentials, as seen on an electroencephalogram (EEG). The EEG features have been extracted into a set of 135 state variables of cross-correlation coefficients of EEGs collected with ten scalp electrodes in the θ, α, and β frequency bands corresponding toanger, sadness, joy, andrelaxation. An emotion matrix is defined which transforms the set of 135 state variables into a four-element emotion vector of which the components are indexes corresponding to the four elementary emotional states. The maximum time resolution of the emotion analysis is 0.64s and it is done in real time. This new technique has a wide variety of applications in both medical and non-medical areas, and the technology suggests the possibility of direct control of systems by the human emotional state. read more read less

Topics:

Electroencephalography (51%)51% related to the paper
141 Citations
Journal Article DOI: 10.1007/S10015-009-0694-X
Electric wheelchair control with gaze direction and eye blinking
Djoko Purwanto, Ronny Mardiyanto, Kohei Arai1

Abstract:

We propose an electric wheelchair controlled by gaze direction and eye blinking. A camera is set up in front of a wheelchair user to capture image information. The sequential captured image is interpreted to obtain the gaze direction and eye blinking properties. The gaze direction is expressed by the horizontal angle of the g... We propose an electric wheelchair controlled by gaze direction and eye blinking. A camera is set up in front of a wheelchair user to capture image information. The sequential captured image is interpreted to obtain the gaze direction and eye blinking properties. The gaze direction is expressed by the horizontal angle of the gaze, and this is derived from the triangle formed by the centers of the eyes and the nose. The gaze direction and eye blinking are used to provide direction and timing commands, respectively. The direction command relates to the direction of movement of the electric wheelchair, and the timing command relates to the time when the wheelchair should move. The timing command with an eye blinking mechanism is designed to generate ready, backward movement, and stop commands for the electric wheelchair. Furthermore, to move at a certain velocity, the electric wheelchair also receives a velocity command as well as the direction and timing commands. The disturbance observer-based control system is used to control the direction and velocity. For safety purposes, an emergency stop is generated when the electric wheelchair user does not focus their gaze consistently in any direction for a specifi ed time. A number of simulations and experiments were conducted with the electric wheelchair in a laboratory environment. read more read less

Topics:

Wheelchair (58%)58% related to the paper, Gaze (52%)52% related to the paper
104 Citations
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Artificial Life and Robotics format uses SPBASIC citation style.

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Yes, the template is compliant with the Artificial Life and Robotics 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 Artificial Life and Robotics?

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 Artificial Life and Robotics citation style.

4. Can I use the Artificial Life and Robotics 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 Artificial Life and Robotics.

5. Can I use a manuscript in Artificial Life and Robotics 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 Artificial Life and Robotics that you can download at the end.

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12. Is Artificial Life and Robotics's impact factor high enough that I should try publishing my article there?

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13. What is Sherpa RoMEO Archiving Policy for Artificial Life and Robotics?

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 Artificial Life and Robotics. 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 Artificial Life and Robotics?

The 5 most common citation types in order of usage for Artificial Life and Robotics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download Artificial Life and Robotics 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 Artificial Life and Robotics Endnote style according to Elsevier guidelines.

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