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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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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.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

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.

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IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  4/2016 - 14

Testing of a Hybrid FES-Robot Assisted Hand Motor Training Program in Sub-Acute Stroke Survivors

GRIGORAS, A. V. See more information about GRIGORAS, A. V. on SCOPUS See more information about GRIGORAS, A. V. on IEEExplore See more information about GRIGORAS, A. V. on Web of Science, IRIMIA, D. C. See more information about  IRIMIA, D. C. on SCOPUS See more information about  IRIMIA, D. C. on SCOPUS See more information about IRIMIA, D. C. on Web of Science, POBORONIUC, M. S. See more information about  POBORONIUC, M. S. on SCOPUS See more information about  POBORONIUC, M. S. on SCOPUS See more information about POBORONIUC, M. S. on Web of Science, POPESCU, C. D. See more information about POPESCU, C. D. on SCOPUS See more information about POPESCU, C. D. on SCOPUS See more information about POPESCU, C. D. on Web of Science
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Download PDF pdficon (1,787 KB) | Citation | Downloads: 167 | Views: 457

Author keywords
electrical stimulation, mechatronic hand, neuromuscular stimulation, rehabilitation robotics, robot control

References keywords
stroke(15), rehabilitation(10), upper(8), patients(6), limb(5), therapy(4), stimulation(4), neurol(4), movement(4), exoskeleton(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-11-30
Volume 16, Issue 4, Year 2016, On page(s): 89 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.04014
Web of Science Accession Number: 000390675900014
SCOPUS ID: 85007565874

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While hands-on therapy is the most commonly used technique for upper limb rehabilitation after stroke, it requires a therapist and residual activity and is best suited for active-assisted exercises. Robotic therapy on the other hand, can provide intention driven training in a motivating environment. We compared a robotic and standard therapy group, allowing intention driven finger flexion/extention respectively active-assisted exercises and a standard therapy only group. A total of 25 patients, 2 to 6 months post-stroke, with moderate motor deficit (Fugl-Meyer Assessment or FMA between 15 and 50), were randomly assigned in one of the groups. Patients practiced 30 minutes of hands-on therapy each day for 2 weeks with a supplementary 30 minutes of robotic therapy each day for patients in the experimental group. Subjects were evaluated using the FMA, Box and Blocks test (BBT) and Stroke Impact Scale (SIS) before and after the treatment. Patients in the experimental group showed higher average gain in all tests than those in the control group but only the SIS average gain was on the limit of statistical significance. This study shows the potential efficacy of robotic therapy for hand rehabilitation in subacute stroke patients.

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

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References Weight

Web of Science® Citations for all references: 1,481 TCR
SCOPUS® Citations for all references: 1,756 TCR

Web of Science® Average Citations per reference: 59 ACR
SCOPUS® Average Citations per reference: 70 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 2017-12-13 03:40 in 124 seconds.

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

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