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Special Issue "Feature Papers in Smart and Intelligent Sensors Systems"

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

Deadline for manuscript submissions: 30 June 2023 | Viewed by 11310

Special Issue Editors

Prof. Dr. Juan M. Corchado
E-Mail Website
Guest Editor
BISITE Research Group, Edificio Multiusos I+D+i, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial Intelligence; machine learning; edge computing; distributed computing; Blockchain; consensus model; smart cities; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Section Intelligent Sensors is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our Section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that showcase the most insightful and influential original articles or reviews, where our Section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be carefully promoted. 

We would also like to take this opportunity to call on more scholars to join the Section Intelligent Sensors so that we can work together to further develop this exciting field of research. Potential topics include but are not limited to the following:

  • Sensor signal processing;
  • Deep learning/machine learning;
  • Data processing;
  • Computer vision;
  • Integrated circuit;
  • Human–robot/machine/computer interaction;
  • Artificial Intelligence;
  • Intelligent Instrumentation;
  • Intelligent control;
  • Intelligent Portable Platforms;
  • Intelligent computing;
  • Wireless sensor network (WSN);
  • Intelligent Environmental monitoring;
  • Smart cities;
  • Smart home/home automation;
  • Smart manufacturing and Industry;
  • Smart energy management/smart grids;
  • Smart agriculture;
  • Smart health monitoring;
  • E-health;
  • Intelligent emotion recognition;
  • Smart building/smart civil infrastructure;
  • Edge computing;
  • Precision Farming;
  • Data Science;
  • Blockchain 5G/6G.

Prof. Dr. Antonio Fernández-Caballero
Prof. Dr. Juan M. Corchado
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 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.

Published Papers (10 papers)

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Research

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Article
Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff
Sensors 2023, 23(3), 1397; https://doi.org/10.3390/s23031397 (registering DOI) - 26 Jan 2023
Abstract
COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, [...] Read more.
COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
Article
EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers
Sensors 2023, 23(1), 186; https://doi.org/10.3390/s23010186 - 24 Dec 2022
Viewed by 364
Abstract
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this [...] Read more.
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user’s inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise
Sensors 2022, 22(16), 6103; https://doi.org/10.3390/s22166103 - 15 Aug 2022
Viewed by 496
Abstract
Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the [...] Read more.
Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models
Sensors 2022, 22(14), 5161; https://doi.org/10.3390/s22145161 - 10 Jul 2022
Viewed by 1004
Abstract
Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, [...] Read more.
Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia’s extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia’s weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting
Sensors 2022, 22(12), 4446; https://doi.org/10.3390/s22124446 - 12 Jun 2022
Cited by 1 | Viewed by 1036
Abstract
This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random [...] Read more.
This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE). Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes
Sensors 2022, 22(8), 2839; https://doi.org/10.3390/s22082839 - 07 Apr 2022
Cited by 1 | Viewed by 921
Abstract
This paper addresses the problem of automatic quality inspection in assembly processes by discussing the design of a computer vision system realized by means of a heterogeneous multiprocessor system-on-chip. Such an approach was applied to a real catalytic converter assembly process, to detect [...] Read more.
This paper addresses the problem of automatic quality inspection in assembly processes by discussing the design of a computer vision system realized by means of a heterogeneous multiprocessor system-on-chip. Such an approach was applied to a real catalytic converter assembly process, to detect planar, translational, and rotational shifts of the flanges welded on the central body. The manufacturing line imposed tight time and room constraints. The image processing method and the features extraction algorithm, based on a specific geometrical model, are described and validated. The algorithm was developed to be highly modular, thus suitable to be implemented by adopting a hardware–software co-design strategy. The most timing consuming computational steps were identified and then implemented by dedicated hardware accelerators. The entire system was implemented on a Xilinx Zynq heterogeneous system-on-chip by using a hardware–software (HW–SW) co-design approach. The system is able to detect planar and rotational shifts of welded flanges, with respect to the ideal positions, with a maximum error lower than one millimeter and one sexagesimal degree, respectively. Remarkably, the proposed HW–SW approach achieves a 23× speed-up compared to the pure software solution running on the Zynq embedded processing system. Therefore, it allows an in-line automatic quality inspection to be performed without affecting the production time of the existing manufacturing process. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks
Sensors 2022, 22(5), 1694; https://doi.org/10.3390/s22051694 - 22 Feb 2022
Cited by 1 | Viewed by 1520
Abstract
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using [...] Read more.
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
The Challenge of Data Annotation in Deep Learning—A Case Study on Whole Plant Corn Silage
Sensors 2022, 22(4), 1596; https://doi.org/10.3390/s22041596 - 18 Feb 2022
Cited by 1 | Viewed by 1326
Abstract
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple [...] Read more.
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidelines for annotating object instances in respective classes and statistics of gathered annotations. Given the challenging image conditions, where objects are present in large amounts of occlusion and clutter, the datasets appear appropriate for training models. However, we experience annotator inconsistency, which can hamper evaluation. Based on this we argue the importance of having an evaluation form independent of the manual annotation where we evaluate our models with physically based sieving metrics. Additionally, instead of the traditional time-consuming manual annotation approach, we evaluate Semi-Supervised Learning as an alternative, showing competitive results while requiring fewer annotations. Specifically, given a relatively large supervised set of around 1400 images we can improve the Average Precision by a number of percentage points. Additionally, we show a significantly large improvement when using an extremely small set of just over 100 images, with over 3× in Average Precision and up to 20 percentage points when estimating the quality. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Article
Development of Imaging System for Online Detection of Chicken Meat with Wooden Breast Condition
Sensors 2022, 22(3), 1036; https://doi.org/10.3390/s22031036 - 28 Jan 2022
Cited by 2 | Viewed by 1709
Abstract
In recent years, the wooden breast condition has emerged as a major meat quality defect in the poultry industry worldwide. Broiler pectoralis major muscle with the wooden breast condition is characterized by hardness upon human palpation, which can lead to decrease in meat [...] Read more.
In recent years, the wooden breast condition has emerged as a major meat quality defect in the poultry industry worldwide. Broiler pectoralis major muscle with the wooden breast condition is characterized by hardness upon human palpation, which can lead to decrease in meat value or even reduced consumer acceptance. The current method of wooden breast detection involves a visual and/or tactile evaluation. In this paper, we present a sideview imaging system for online detection of chicken breast fillets affected by the wooden breast condition. The system can measure a physical deformation (bending) of an individual chicken-breast fillet through high-speed imaging at about 200 frames per second and custom image processing techniques. The developed image processing algorithm shows the over 95% classification performance in detecting wooden breast fillets. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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Review

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Review
Printing Technologies as an Emerging Approach in Gas Sensors: Survey of Literature
Sensors 2022, 22(9), 3473; https://doi.org/10.3390/s22093473 - 03 May 2022
Cited by 3 | Viewed by 1844
Abstract
Herein, we review printing technologies which are commonly approbated at recent time in the course of fabricating gas sensors and multisensor arrays, mainly of chemiresistive type. The most important characteristics of the receptor materials, which need to be addressed in order to achieve [...] Read more.
Herein, we review printing technologies which are commonly approbated at recent time in the course of fabricating gas sensors and multisensor arrays, mainly of chemiresistive type. The most important characteristics of the receptor materials, which need to be addressed in order to achieve a high efficiency of chemisensor devices, are considered. The printing technologies are comparatively analyzed with regard to, (i) the rheological properties of the employed inks representing both reagent solutions or organometallic precursors and disperse systems, (ii) the printing speed and resolution, and (iii) the thickness of the formed coatings to highlight benefits and drawbacks of the methods. Particular attention is given to protocols suitable for manufacturing single miniature devices with unique characteristics under a large-scale production of gas sensors where the receptor materials could be rather quickly tuned to modify their geometry and morphology. We address the most convenient approaches to the rapid printing single-crystal multisensor arrays at lab-on-chip paradigm with sufficiently high resolution, employing receptor layers with various chemical composition which could replace in nearest future the single-sensor units for advancing a selectivity. Full article
(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)
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