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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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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.

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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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  2/2018 - 11

A Programmable Biopotential Aquisition Front-end with a Resistance-free Current-balancing Instrumentation Amplifier

FARAGO, P. See more information about FARAGO, P. on SCOPUS See more information about FARAGO, P. on IEEExplore See more information about FARAGO, P. on Web of Science, GROZA, R. See more information about  GROZA, R. on SCOPUS See more information about  GROZA, R. on SCOPUS See more information about GROZA, R. on Web of Science, HINTEA, S. See more information about  HINTEA, S. on SCOPUS See more information about  HINTEA, S. on SCOPUS See more information about HINTEA, S. on Web of Science, SOSER, P. See more information about SOSER, P. on SCOPUS See more information about SOSER, P. on SCOPUS See more information about SOSER, P. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,253 KB) | Citation | Downloads: 126 | Views: 393

Author keywords
analog processing circuits, biomedical monitoring, biomedical signal processing, operational amplifiers, programmable circuits

References keywords
amplifier(18), circuits(15), instrumentation(13), systems(11), noise(8), design(8), current(8), cmos(8), state(6), solid(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 85 - 92
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02011
Web of Science Accession Number: 000434245000011
SCOPUS ID: 85047865716

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The development of wearable biomedical equipment benefits from low-power and low-voltage circuit techniques for reduced battery size and battery, or even battery-less, operation. This paper proposes a fully-differential low-power resistance-free programmable instrumentation amplifier for the analog front-end of biopotential monitoring systems. The proposed instrumentation amplifier implements the current balancing technique. Low power consumption is achieved with subthreshold biasing. To reduce chip area and enable integration, passive resistances have been replaced with active equivalents. Accordingly, the instrumentation amplifier gain is expressed as the ratio of two transconductance values. The proposed instrumentation amplifier exhibits two degrees of freedom: one to set the desired range and the other for fine-tuning of the voltage gain. The proposed IA is employed in a programmable biopotential acquisition front-end. The programmable frequency-selective behavior is achieved by having the lower cutoff frequency of a Gm-C Tow-Thomas biquad varied in a constant-C tuning approach. The proposed solutions and the programmability of the operation parameters to the specifications of particular bio-medical signals are validated on a 350nm CMOS process.

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

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References Weight

Web of Science® Citations for all references: 689 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 19 ACR
SCOPUS® Average Citations per reference: 0

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 01:28 in 192 seconds.

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Faculty of Electrical Engineering and Computer Science
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