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Analysis of institutional authors

Bordel BAuthorAlcarria RAuthorRobles TAuthor

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July 15, 2020
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A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Publicated to: IEEE Access. 8 109360-109371 - 2020-01-01 8(), DOI: 10.1109/ACCESS.2020.3001275

Authors:

Bordel, Borja; Alcarria, Ramon; Robles, Tomas; You, Ilsun
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Affiliations

Soonchunhyang Univ, Dept Informat Secur Engn, Seoul 13557, South Korea - Author
Soonchunhyang University - Author
Univ Politecn Madrid, Dept Geospatial Engn, Madrid 28040, Spain - Author
Univ Politecn Madrid, Dept Informat Syst, Madrid 28040, Spain - Author
Universidad Politécnica de Madrid - Author
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Abstract

© 2013 IEEE. In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.
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Keywords

Artificial intelligenceBig dataBiologyClassification algorithmsData predictionInternet-of-medical-thingsLaurent seriesNeural-networkPrediction algorithmsReal-time systemsSignal processingSignal processing algorithmsStatistical learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-03:

  • WoS: 3
  • Scopus: 5
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 26 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/85535/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 144
  • Downloads: 90
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Republic of Korea.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (BORDEL SANCHEZ, BORJA) .

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