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20 pages, 13406 KiB

Open AccessArticle

Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements

by Mustafa Khudhair and Nenad Gucunski

Sensors 2023, 23(19), 8052; https://doi.org/10.3390/s23198052 - 24 Sep 2023

Cited by 1 | Viewed by 742

Abstract

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. To achieve this, a parametric study was first conducted using numerical simulations to investigate [...] Read more.

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. Subsequently, this algorithm was applied to data collected from an actual bridge deck in the BEAST^® facility, showcasing a significant advancement in ER measurement interpretation through the incorporation of information from other NDE technologies. Such strides are pivotal in advancing the reliability of assessments of structural elements for their durability and safety. Full article

(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)

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21 pages, 969 KiB

Open AccessArticle

Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications

by Ewerton L. de Sousa, Leonardo A. de Aquino Marques, Israel da S. Felix de Lima, Ana Beatriz M. Neves, Eduardo Nogueira Cunha, Márcio Eduardo Kreutz and Augusto José V. Neto

Sensors 2023, 23(16), 7210; https://doi.org/10.3390/s23167210 - 16 Aug 2023

Cited by 1 | Viewed by 2849

Abstract

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, [...] Read more.

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications. Full article

(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)

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31 pages, 3816 KiB

Open AccessArticle

Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contact Tracing to Monitor Infection Spread among Hospital and Office Staff

by Maggie Ezzat Gaber Gendy, Phi Tham, Flynn Harrison and Mehmet Rasit Yuce

Sensors 2023, 23(3), 1397; https://doi.org/10.3390/s23031397 - 26 Jan 2023

Cited by 3 | Viewed by 2269

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)

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18 pages, 507 KiB

Open AccessArticle

EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers

by Luis Hernández-Álvarez, Elena Barbierato, Stefano Caputo, Lorenzo Mucchi and Luis Hernández Encinas

Sensors 2023, 23(1), 186; https://doi.org/10.3390/s23010186 - 24 Dec 2022

Cited by 3 | Viewed by 1899

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

χ^{2}

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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26 pages, 879 KiB

Open AccessArticle

Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise

by Scarlett Abramson, William Moran, Robin Evans and Andrew Melatos

Sensors 2022, 22(16), 6103; https://doi.org/10.3390/s22166103 - 15 Aug 2022

Viewed by 1236

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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28 pages, 3150 KiB

Open AccessArticle

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

by Naruephorn Tengtrairat, Wai Lok Woo, Phetcharat Parathai, Damrongsak Rinchumphu and Chatchawan Chaichana

Sensors 2022, 22(14), 5161; https://doi.org/10.3390/s22145161 - 10 Jul 2022

Cited by 7 | Viewed by 2929

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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26 pages, 1035 KiB

Open AccessArticle

Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting

by M. Zulfiqar, Kelum A. A. Gamage, M. Kamran and M. B. Rasheed

Sensors 2022, 22(12), 4446; https://doi.org/10.3390/s22124446 - 12 Jun 2022

Cited by 11 | Viewed by 3260

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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16 pages, 3935 KiB

Open AccessArticle

Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes

by Fabio Frustaci, Fanny Spagnolo, Stefania Perri, Giuseppe Cocorullo and Pasquale Corsonello

Sensors 2022, 22(8), 2839; https://doi.org/10.3390/s22082839 - 07 Apr 2022

Cited by 4 | Viewed by 2218

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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14 pages, 1194 KiB

Open AccessArticle

Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks

by Panagiotis Tsinganos, Bart Jansen, Jan Cornelis and Athanassios Skodras

Sensors 2022, 22(5), 1694; https://doi.org/10.3390/s22051694 - 22 Feb 2022

Cited by 8 | Viewed by 3058

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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19 pages, 16508 KiB

Open AccessArticle

The Challenge of Data Annotation in Deep Learning—A Case Study on Whole Plant Corn Silage

by Christoffer Bøgelund Rasmussen, Kristian Kirk and Thomas B. Moeslund

Sensors 2022, 22(4), 1596; https://doi.org/10.3390/s22041596 - 18 Feb 2022

Cited by 11 | Viewed by 4227

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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19 pages, 3075 KiB

Open AccessArticle

Development of Imaging System for Online Detection of Chicken Meat with Wooden Breast Condition

by Seung-Chul Yoon, Brian C. Bowker, Hong Zhuang and Kurt C. Lawrence

Sensors 2022, 22(3), 1036; https://doi.org/10.3390/s22031036 - 28 Jan 2022

Cited by 2 | Viewed by 2807

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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31 pages, 462 KiB

Open AccessReview

Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT

by Ivan Rodriguez-Conde, Celso Campos and Florentino Fdez-Riverola

Sensors 2023, 23(4), 1911; https://doi.org/10.3390/s23041911 - 08 Feb 2023

Cited by 4 | Viewed by 2998

Abstract

Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development [...] Read more.

Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them. Full article

(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)

24 pages, 1234 KiB

Open AccessReview

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

by Didem Cicek and Burak Kantarci

Sensors 2023, 23(3), 1699; https://doi.org/10.3390/s23031699 - 03 Feb 2023

Cited by 10 | Viewed by 5483

Abstract

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big [...] Read more.

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big “sensed” data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management. Full article

(This article belongs to the Special Issue Feature Papers in Smart and Intelligent Sensors Systems)

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33 pages, 4902 KiB

Open AccessReview

Printing Technologies as an Emerging Approach in Gas Sensors: Survey of Literature

by Nikolay P. Simonenko, Nikita A. Fisenko, Fedor S. Fedorov, Tatiana L. Simonenko, Artem S. Mokrushin, Elizaveta P. Simonenko, Ghenadii Korotcenkov, Victor V. Sysoev, Vladimir G. Sevastyanov and Nikolay T. Kuznetsov

Sensors 2022, 22(9), 3473; https://doi.org/10.3390/s22093473 - 03 May 2022

Cited by 20 | Viewed by 4397

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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