Research

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13 pages, 3514 KiB

Open AccessArticle

A Preliminary Exploration of the Placental Position Influence on Uterine Electromyography Using Fractional Modelling

by Müfit Şan, Arnaldo Batista, Sara Russo, Filipa Esgalhado, Catarina R. Palma dos Reis, Fátima Serrano and Manuel Ortigueira

Sensors 2022, 22(5), 1704; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051704 - 22 Feb 2022

Cited by 3 | Viewed by 1744

Abstract

The uterine electromyogram, also called electrohysterogram (EHG), is the electrical signal generated by uterine contractile activity. The EHG has been considered an expanding technique for pregnancy monitoring and preterm risk evaluation. Data were collected on the abdominal surface. It has been speculated the [...] Read more.

The uterine electromyogram, also called electrohysterogram (EHG), is the electrical signal generated by uterine contractile activity. The EHG has been considered an expanding technique for pregnancy monitoring and preterm risk evaluation. Data were collected on the abdominal surface. It has been speculated the effect of the placenta location on the characteristics of the EHG. In this work, a preliminary exploration method is proposed using the average spectra of Alvarez waves contractions of subjects with anterior and non-anterior placental position as a basis for the triple-dispersion Cole model that provides a best fit for these two cases. This leads to the uterine impedance estimation for these two study cases. Non-linear least square fitting (NLSF) was applied for this modelling process, which produces electric circuit fractional models’ representations. A triple-dispersion Cole-impedance model was used to obtain the uterine impedance curve in a frequency band between 0.1 and 1 Hz. A proposal for the interpretation relating the model parameters and the placental influence on the myometrial contractile action is provided. This is the first report regarding in silico estimation of the uterine impedance for cases involving anterior or non-anterior placental positions. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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15 pages, 2459 KiB

Open AccessArticle

Fractional Derivatives Application to Image Fusion Problems

by Szymon Motłoch, Grzegorz Sarwas and Andrzej Dzieliński

Sensors 2022, 22(3), 1049; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031049 - 28 Jan 2022

Cited by 4 | Viewed by 2239

Abstract

In this paper, an analysis of the method that uses a fractional order calculus to multispectral images fusion is presented. We analyze some correct basic definitions of the fractional order derivatives that are used in the image processing context. Several methods of determining [...] Read more.

In this paper, an analysis of the method that uses a fractional order calculus to multispectral images fusion is presented. We analyze some correct basic definitions of the fractional order derivatives that are used in the image processing context. Several methods of determining fractional derivatives of digital images are tested, and the influence of fractional order change on the quality of fusion is presented. Results achieved are compared with the results obtained for methods where the integer order derivatives were used. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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20 pages, 1547 KiB

Open AccessArticle

MEMS Accelerometer Noises Analysis Based on Triple Estimation Fractional Order Algorithm

by Michal Macias, Dominik Sierociuk and Wiktor Malesza

Sensors 2022, 22(2), 527; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020527 - 11 Jan 2022

Cited by 7 | Viewed by 1640

Abstract

This paper is devoted to identifying parameters of fractional order noises with application to noises obtained from MEMS accelerometer. The analysis and parameters estimation will be based on the Triple Estimation algorithm, which can simultaneously estimate state, fractional order, and parameter estimates. The [...] Read more.

This paper is devoted to identifying parameters of fractional order noises with application to noises obtained from MEMS accelerometer. The analysis and parameters estimation will be based on the Triple Estimation algorithm, which can simultaneously estimate state, fractional order, and parameter estimates. The capability of the Triple Estimation algorithm to fractional noises estimation will be confirmed by the sets of numerical analyses for fractional constant and variable order systems with Gaussian noise input signal. For experimental data analysis, the MEMS sensor SparkFun MPU9250 Inertial Measurement Unit (IMU) was used with data obtained from the accelerometer in x, y and z-axes. The experimental results clearly show the existence of fractional noise in this MEMS’ noise, which can be essential information in the design of filtering algorithms, for example, in inertial navigation. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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15 pages, 1075 KiB

Open AccessCommunication

Triple Estimation of Fractional Variable Order, Parameters, and State Variables Based on the Unscented Fractional Order Kalman Filter

by Dominik Sierociuk and Michal Macias

Sensors 2021, 21(23), 8159; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238159 - 06 Dec 2021

Cited by 4 | Viewed by 2238

Abstract

In this paper, a method for states, parameters, and fractional order estimation is presented. The proposed method is an extension of the traditional dual estimation method and uses three blocks of filters with appropriate data interconnections. As the main part of the estimation [...] Read more.

In this paper, a method for states, parameters, and fractional order estimation is presented. The proposed method is an extension of the traditional dual estimation method and uses three blocks of filters with appropriate data interconnections. As the main part of the estimation algorithm, the Fractional Unscented Kalman Filter was used. The proposed Triple Estimation algorithm might be treated as a convenient tool for estimation and analysis of a wide range of dynamical systems with fractional constants or variable order nature, especially when knowledge about the identified system is very restricted and both order and system parameters are unknown. In order to show the performance of the proposed algorithm, sets of numerical results are presented. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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18 pages, 2588 KiB

Open AccessArticle

Fractional-Order Sensing and Control: Embedding the Nonlinear Dynamics of Robot Manipulators into the Multidimensional Scaling Method

by António M. Lopes and José A. Tenreiro Machado

Sensors 2021, 21(22), 7736; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227736 - 20 Nov 2021

Cited by 5 | Viewed by 1436

Abstract

This paper studies the use of multidimensional scaling (MDS) to assess the performance of fractional-order variable structure controllers (VSCs). The test bed consisted of a revolute planar robotic manipulator. The fractional derivatives required by the VSC can be obtained either by adopting numerical [...] Read more.

This paper studies the use of multidimensional scaling (MDS) to assess the performance of fractional-order variable structure controllers (VSCs). The test bed consisted of a revolute planar robotic manipulator. The fractional derivatives required by the VSC can be obtained either by adopting numerical real-time signal processing or by using adequate sensors exhibiting fractional dynamics. Integer (fractional) VCS and fractional (integer) sliding mode combinations with different design parameters were tested. Two performance indices based in the time and frequency domains were adopted to compare the system states. The MDS generated the loci of objects corresponding to the tested cases, and the patterns were interpreted as signatures of the system behavior. Numerical experiments illustrated the feasibility and effectiveness of the approach for assessing and visualizing VSC systems. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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16 pages, 9680 KiB

Open AccessArticle

IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering

by Santanu Metia, Huynh A. D. Nguyen and Quang Phuc Ha

Sensors 2021, 21(16), 5313; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165313 - 06 Aug 2021

Cited by 18 | Viewed by 5543

Abstract

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. [...] Read more.

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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24 pages, 4662 KiB

Open AccessArticle

PatientDataChain: A Blockchain-Based Approach to Integrate Personal Health Records

by Alexandra Cernian, Bogdan Tiganoaia, Ioan Sacala, Adrian Pavel and Alin Iftemi

Sensors 2020, 20(22), 6538; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226538 - 16 Nov 2020

Cited by 36 | Viewed by 6335

Abstract

Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult [...] Read more.

Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients’ traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients’ medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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Review

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26 pages, 403 KiB

Open AccessReview

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

by Cristina I. Muresan, Isabela R. Birs, Eva H. Dulf, Dana Copot and Liviu Miclea

Sensors 2021, 21(17), 5920; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175920 - 02 Sep 2021

Cited by 36 | Viewed by 3707

Abstract

The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a [...] Read more.

The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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