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JCR Impact Factor: 0.699
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Issues per year: 4
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Next issue: May 2019
Avg review time: 81 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2018 - 1
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Computational Balancing between Wearable Sensor and Smartphone towards Energy-Efficient Remote Healthcare Monitoring

SECERBEGOVIC, A. See more information about SECERBEGOVIC, A. on SCOPUS See more information about SECERBEGOVIC, A. on IEEExplore See more information about SECERBEGOVIC, A. on Web of Science, GOGIC, A. See more information about  GOGIC, A. on SCOPUS See more information about  GOGIC, A. on SCOPUS See more information about GOGIC, A. on Web of Science, SULJANOVIC, N. See more information about  SULJANOVIC, N. on SCOPUS See more information about  SULJANOVIC, N. on SCOPUS See more information about SULJANOVIC, N. on Web of Science, ZAJC, M. See more information about  ZAJC, M. on SCOPUS See more information about  ZAJC, M. on SCOPUS See more information about ZAJC, M. on Web of Science, MUJCIC, A. See more information about MUJCIC, A. on SCOPUS See more information about MUJCIC, A. on SCOPUS See more information about MUJCIC, A. on Web of Science
 
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Download PDF pdficon (1,647 KB) | Citation | Downloads: 623 | Views: 449

Author keywords
wearable sensors, mobile computing, body sensor networks, biomedical signal processing, performance evaluation

References keywords
mobile(7), computing(6), sensor(5), wearable(4), time(4), systems(4), selection(4), recognition(4), real(4), feature(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04001
Web of Science Accession Number: 000451843400001
SCOPUS ID: 85058816909

Abstract
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Recent advances in the development of wearable sensors and smartphones open up opportunities for executing computing operations on the devices instead of using them for streaming raw data. By minimizing power consumption due to the wireless transmission, limited energy resources of wearable devices can be utilized not only for sensing, but also for processing physiological signals. Computational tasks between a wearable sensor and a smartphone can be distributed efficiently in order to provide balance between power consumption of both processing and transmission of the data. In this paper, we have analyzed the computational balancing between a wearable sensor and a smartphone. Presented models show different trade-offs between classification accuracy, processing time and power consumption due to different number and types of extracted features and classification models. Our results are based on a physiological dataset, where electrocardiogram and electro dermal activity signals were collected from 24 individuals in short-term stress and mental workload detection scenario. Our findings show that placing a feature extraction on a wearable sensor is efficient when processing cost of the extracted features is small. On the other hand, moving classification task to the smartphone can improve accuracy of recognition without compromising the overall power consumption.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 15]


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[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 39]


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[CrossRef]


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References Weight

Web of Science® Citations for all references: 7,273 TCR
SCOPUS® Citations for all references: 9,646 TCR

Web of Science® Average Citations per reference: 316 ACR
SCOPUS® Average Citations per reference: 419 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-03-19 08:35 in 126 seconds.




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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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