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Multi-Sensor Measurement and Data Fusion
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Multi-Sensor Measurement and Data Fusion

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (1 July 2021) | Viewed by 9293

Special Issue Editors

Prof. Dr. Volodymyr Mosorov

E-Mail Website
Guest Editor
Institute of Applied Computer Science; Lodz University of Technology, Łódź, Poland
Interests: industrial engineering; images; tomography; gamma rays; detectors
Special Issues, Collections and Topics in MDPI journals
Dr. Krzysztof Strzecha

E-Mail Website
Guest Editor
Institute of Applied Computer Science, Lodz University of Technology, 90-924 Łódź, Poland
Interests: real-time systems; computer measurement and control
Prof. Dr. Lidia Jackowska-Strumiłło

E-Mail Website
Guest Editor
Institute of Applied Computer Science, Lodz University of Technology, 90-924 Łódź, Poland
Interests: real-time systems; computer measurement and control; ANN
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multisensor measurement and data fusion include multisensor measuring and data fusion technologies by introducing the architecture of different multisensor process monitoring systems and different, widely applied data fusion algorithms. It can be assumed that sensors provide measurement data to the data fusion layer, which uses advanced signal processing algorithms to combine the information into a whole. The input data can be 1D or n-dimensional signals, for instance, images. Data fusion technology has today become one of the research hot topics worldwide. Using specific standards, data fusion can obtain a consistent interpretation of a tested object, so that the multimodality sensor system performs better compared to many one-modality systems.

The development of such systems has been rapidly growing in recent decades, and a large range of applications have been identified in various sectors, such as industry, healthcare, and environmental protection. Despite progress in this field, however, much more research is required on all integration levels, especially to achieve international standards for multisensor measurements. This Special Issue on “Multisensor Measurement and Data Fusion” focuses on recent advances in multisensor system development and applications and is calling for high-impact submissions in the following areas:

  • Wireless multimodality sensor networks;
  • Multimodality measurement system;
  • n-dimensional data fusion;
  • Modeling and simulation of different sensor's types;
  • Ad hoc wireless multisensor measurement systems;
  • Novel applications of multisensor measurement and data fusion from all areas, as well as all other related areas.

Prof. Dr. Volodymyr Mosorov
Dr. Krzysztof Strzecha
Prof. Dr. Lidia Jackowska-Strumiłło
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multi-Modality Measurement System
  • Information Fusion
  • Ad Hoc Measurement Systems

Published Papers (3 papers)

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Research

20 pages, 4901 KiB  
Article
An Online Calibration Method Based on n-Tuple and Opportunistic Communication for Mine Mass Portable Gas Sensors
by Gang Wang, Yang Zhao, Zeheng Ding and Xiaohu Zhao
Sensors 2021, 21(7), 2451; https://doi.org/10.3390/s21072451 - 02 Apr 2021
Viewed by 1925
Abstract
Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration [...] Read more.
Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., ‘single-sensor, multi-position’ and ‘multi-sensor, single-position’) of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved. Full article
(This article belongs to the Special Issue Multi-Sensor Measurement and Data Fusion)
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18 pages, 14668 KiB  
Article
Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model
by Peter Krapež, Matjaž Vidmar and Marko Munih
Sensors 2021, 21(7), 2294; https://doi.org/10.3390/s21072294 - 25 Mar 2021
Cited by 12 | Viewed by 3956
Abstract
An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and [...] Read more.
An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and the channel response signal. In this paper, the neural network (NN) method for correcting the orientation-induced distance error without the need to calculate the signal strength, obtain the channel response or know any parameters of the antenna and the UWB modules is presented. The NN method utilizes only the measured distance and the tag orientation, and implements an NN model obtained by machine learning, using measurements at different distances and orientations of the two UWB modules. The verification of the experimental setup with 12 anchors and a tag shows that with the proposed NN method, 5 cm better root mean square error values (RMSEs) are obtained for the measured distance between the anchors and the tag compared to the calibration method that did not include orientation information. With the least-square estimator, 14 cm RMSE in 3D is obtained with the NN model corrected distances, with a 9 cm improvement compared to when raw distances are used. The method produces better results without the need to obtain the UWB module’s diagnostics parameters that are required to calculate the received signal strength or channel response, and in this way maintain the minimum packet size for the ranging protocol. Full article
(This article belongs to the Special Issue Multi-Sensor Measurement and Data Fusion)
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23 pages, 14928 KiB  
Article
Influence of Flow Disturbances behind the 90° Bend on the Indications of the Ultrasonic Flow Meter with Clamp-On Sensors on Pipelines
by Piotr Synowiec, Artur Andruszkiewicz, Wiesław Wędrychowicz, Piotr Piechota and Elżbieta Wróblewska
Sensors 2021, 21(3), 868; https://doi.org/10.3390/s21030868 - 28 Jan 2021
Cited by 9 | Viewed by 2558
Abstract
The subject matter of the article concerns velocities/flow rate measurements in the area of disturbed flows-behind the 90° bend. They were conducted by means of an ultrasonic flowmeter with clamp-on sensors on pipeline, for water and two different Reynolds numbers of 70,000 and [...] Read more.
The subject matter of the article concerns velocities/flow rate measurements in the area of disturbed flows-behind the 90° bend. They were conducted by means of an ultrasonic flowmeter with clamp-on sensors on pipeline, for water and two different Reynolds numbers of 70,000 and 100,000, corresponding to two velocities of approximately 1.42 m/s and 2.04 m/s. The tests were carried out at 12 distances from the disturbance. Sensors on the circumference of the pipeline were mounted 30° each. The correction factor values were calculated for the given measurement geometry. The measurements have shown that the values of this coefficient are always greater than 1, which means that the ultrasonic flow meter understates the speed values. They also showed that already at a distance of 8 nominal diameters from the disturbance, the correction factor does not exceed 1.02, so the measurement errors are within the maximum permissible error (MPE) of a typical ultrasonic flow meter. For distances less than eight nominal diameters from the disturbance, not taking the correction factor value into the account can lead to systematic errors of up to 10.8%. Studies have also proved that in each measurement plane behind the disturbance there are two mounting angles for the ultrasonic sensors, 60° and 240° respectively, for which the correction factor values are minimal. Additionally, using the laser Doppler anemometry (LDA) method, velocity solids were determined at individual distances from the disturbance, and the projections of velocity blocks on the appropriate plane represented velocity profiles and indicated the distances from the disturbance at which these profiles stabilise. Full article
(This article belongs to the Special Issue Multi-Sensor Measurement and Data Fusion)
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