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Special Issue "Sensor Data Fusion Analysis for Broad Applications"

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

Deadline for manuscript submissions: 20 March 2023 | Viewed by 14870

Special Issue Editor

Dr. Natividad Duro Carralero
E-Mail Website
Guest Editor
Department of Computer Sciences and Automatic Control, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
Interests: sensor data fusion; industry applications; machine learning; data analysis algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, most applications use many different sensors as it is very common to collect as much information as possible from our various systems. New technologies allow us to analyze these data and obtain relevant information from them. Analyzing these data is very important because it allows us to modify our industrial strategy to obtain higher productivity and more efficient operations.

There are some emerging areas that would greatly benefit from sensor data fusion analysis, such as Industrial Applications, Medical or Biomedical Applications, Robotics, Monitoring Systems, Transportation Systems, Information Systems or Control Processes. It is important to note that to analyze and understand these large volumes of data from different sensors, we need special mathematical methods, algorithms and techniques.

This Special Issue encourages authors, from academia and industry, to submit new research results from the analysis of data obtained from multiple sensors in different areas and types of applications. The Special Issue topics include, but are not limited to:

  • Sensor data fusion analysis in Industrial Applications;
  • Sensor data fusion analysis in Medical or Biomedical Applications;
  • Sensor data fusion analysis in Robotics Applications;
  • Data preparation techniques for sensor data fusion analysis;
  • Mathematical algorithms for sensor data fusion analysis;
  • Principles and techniques for sensor data fusion.

Dr. Natividad Duro Carralero
Guest Editor

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 2400 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

  • sensor data fusion
  • industrial applications
  • medical or biomedical applications
  • robotics applications
  • mathematical algorithms and techniques for data fusion

Published Papers (12 papers)

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Research

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Article
Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems
Sensors 2022, 22(23), 9132; https://doi.org/10.3390/s22239132 - 24 Nov 2022
Viewed by 284
Abstract
For magnetic levitation systems subject to dynamical uncertainty and exterior perturbations, we implement a real-time Prescribed Performance Control (PPC). A modified function of Global Fast Terminal Sliding Mode Manifold (GFTSMM) based on the transformed error of the novel PPC is introduced; hence, the [...] Read more.
For magnetic levitation systems subject to dynamical uncertainty and exterior perturbations, we implement a real-time Prescribed Performance Control (PPC). A modified function of Global Fast Terminal Sliding Mode Manifold (GFTSMM) based on the transformed error of the novel PPC is introduced; hence, the error variable quickly converges to the equilibrium point with the prescribed performance, which means that maximum overshoot and steady-state of the controlled errors will be in a knowledge-defined boundary. To enhance the performance of Global Fast Terminal Sliding Mode Control (GFTSMC) and to reduce chattering in the control input, a modified third-order sliding mode observer (MTOSMO) is proposed to estimate the whole uncertainty and external disturbance. The combination of the GFTSMC, PPC, and MTOSMO generates a novel solution ensuring a finite-time stable position of the controlled ball and the possibility of performing different orbit tracking missions with an impressive performance in terms of tracking accuracy, fast convergence, stabilization, and chattering reduction. It also possesses a simple design that is suitable for real-time applications. By using the Lyapunov-based method, the stable evidence of the developed method is fully verified. We implement a simulation and an experiment on the laboratory magnetic levitation model to demonstrate the improved performance of the developed control system. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge
Sensors 2022, 22(21), 8143; https://doi.org/10.3390/s22218143 - 24 Oct 2022
Viewed by 403
Abstract
The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time [...] Read more.
The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific “wake up” triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the “edge”, where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles
Sensors 2022, 22(20), 7701; https://doi.org/10.3390/s22207701 - 11 Oct 2022
Viewed by 436
Abstract
Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate [...] Read more.
Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks
Sensors 2022, 22(8), 2858; https://doi.org/10.3390/s22082858 - 08 Apr 2022
Cited by 1 | Viewed by 709
Abstract
The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful [...] Read more.
The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition
Sensors 2022, 22(6), 2132; https://doi.org/10.3390/s22062132 - 09 Mar 2022
Cited by 2 | Viewed by 1004
Abstract
Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was [...] Read more.
Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties
Sensors 2022, 22(4), 1436; https://doi.org/10.3390/s22041436 - 13 Feb 2022
Cited by 1 | Viewed by 826
Abstract
Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, [...] Read more.
Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters’ prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm
Sensors 2022, 22(3), 1161; https://doi.org/10.3390/s22031161 - 03 Feb 2022
Cited by 1 | Viewed by 861
Abstract
This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of [...] Read more.
This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Deep Learning Approaches for Robust Time of Arrival Estimation in Acoustic Emission Monitoring
Sensors 2022, 22(3), 1091; https://doi.org/10.3390/s22031091 - 31 Jan 2022
Cited by 7 | Viewed by 1699
Abstract
In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of [...] Read more.
In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of standard statistical strategies which cannot handle, with enough robustness, very noisy scenarios and, thus, cannot be sufficiently reliable when the signal statistics are perturbed by local drifts or outliers. This concept was validated with two experiments: the pure ToA identification capability was firstly assessed on synthetic signals for which a ground truth is available, showing a 10× gain in accuracy when compared to the classical Akaike information criterion (AIC). Then, the same models were tested via experimental data acquired in the framework of a localization problem to identify targets with known coordinates on a square aluminum plate, demonstrating an overreaching precision under significant noise levels. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing
Sensors 2022, 22(2), 645; https://doi.org/10.3390/s22020645 - 14 Jan 2022
Cited by 1 | Viewed by 923
Abstract
In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. [...] Read more.
In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Article
Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh
Sensors 2022, 22(1), 126; https://doi.org/10.3390/s22010126 - 25 Dec 2021
Cited by 1 | Viewed by 2465
Abstract
The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes [...] Read more.
The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Communication
Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems
Sensors 2021, 21(24), 8437; https://doi.org/10.3390/s21248437 - 17 Dec 2021
Cited by 1 | Viewed by 1319
Abstract
In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to [...] Read more.
In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Review

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Review
A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends
Sensors 2022, 22(5), 1734; https://doi.org/10.3390/s22051734 - 23 Feb 2022
Cited by 2 | Viewed by 2188
Abstract
Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor [...] Read more.
Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified. Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Comparison of Data Fusion Techniques for Stress Level Evaluation using Wearable and Ambient Sensing
Authors: Alessandro Leone 1, Andrea  Caroppo 1, Andrea Manni 1, Alessandra Papetti 2, Marianna Ciccarelli 2, Gabriele Rescio 1
Affiliation: 1. CNR—National Research Council of Italy, IMM—Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
2. Polytechnique University of Marche, 60121 Ancona, Italy
Abstract: Stress can affect all aspects of the life, such as physical health, behaviors and thinking ability. Consequently, it is very important to detect high-level of stress at early stages to prevent its negative effects. With the emergence of smart technologies, both wearable and ambient, researchers have started detecting extreme stress of individuals with them during daily routines. This work describes a novel framework designed to provide a decision support tool for automatic stress detection system using physiological signals obtained from minimally invasive smart wearable device and video signals acquired through a commercial and low-cost vision sensor. A comparison of data fusion techniques was performed to the overall multi-modal feature set extracted from the heterogeneous sensing technologies.

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