The Technical Challenges for Applying Unsaturated Soil Sensors to Conduct Laboratory-Scale Seepage Experiments
Ratiometric Optical Fiber Dissolved Oxygen Sensor Based on Fluorescence Quenching Principle
Single-Molecule Surface-Enhanced Raman Spectroscopy
Single-Shot 3D Topography of Transmissive and Reflective Samples with a Dual-Mode Telecentric-Based Digital Holographic Microscope

Journal Description

Sensors

Sensors is the leading international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Embase, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 16.2 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2022).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Sections: published in 25 topical sections.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, Automation, JCP and Devices.

Impact Factor: 3.847 (2021) ; 5-Year Impact Factor: 4.050 (2021)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

Open AccessArticle

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems

Douglas Chagas da Silva

José Olimpio Rodrigues Batista, Jr.

Marco Antonio Firmino de Sousa

Gustavo Marques Mostaço

Claudio de Castro Monteiro

Graça Bressan

Carlos Eduardo Cugnasca

and

Regina Melo Silveira

Sensors 2022, 22(16), 6066; https://doi.org/10.3390/s22166066 (registering DOI) - 13 Aug 2022

Abstract

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience. Full article

(This article belongs to the Special Issue Towards Next Generation beyond 5G (B5G) Networks)

► Show Figures

Figure 1

Open AccessArticle

Propagation Characteristics of Magnetic Tomography Method Detection Signals of Oil and Gas Pipelines Based on Boundary Conditions

Linlin Liu

Lijian Yang

and

Songwei Gao

Sensors 2022, 22(16), 6065; https://doi.org/10.3390/s22166065 (registering DOI) - 13 Aug 2022

Abstract

The magnetic tomography method (MTM) is a non-contact external inspection method for detecting metal magnetic memory signals. It has great potential for application in long-distance oil pipeline and subsea pipeline inspection. However, the spatial distribution characteristics and propagation laws of magnetic signals are not yet clear, which makes the MTM passive detection. In this study, a three-dimensional mathematical model of the magnetic field distribution of the stress concentration zone outside the pipe was established based on the boundary conditions. For the two cases in which the stress concentration zone was located at the top and bottom of the inner wall of the pipe, the model was solved by finite element analysis. The variation law of the magnetic signal outside the pipe was analyzed, and experiments were designed to verify the model. The results show that the shape of the magnetic memory signal remained unchanged after passing through the pipe wall. As the magnetic permeability of the pipe medium is much larger than that of air, the magnetic memory signal is significantly attenuated after penetrating the pipe wall. As the detection height increases, the magnetic induction outside the tube decays exponentially. The results also prove that the magnetic tomography method can detect the stress concentration zone at any position of the pipeline, and the detection accuracy is higher when it is located at the top of the pipeline. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle

Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms

and

Sensors 2022, 22(16), 6064; https://doi.org/10.3390/s22166064 (registering DOI) - 13 Aug 2022

Abstract

Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed’s spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm’s optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

Open AccessArticle

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels

and

Sensors 2022, 22(16), 6063; https://doi.org/10.3390/s22166063 (registering DOI) - 13 Aug 2022

Abstract

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Ocean Monitoring (AID-OM))

Open AccessReview

Recent Progress in Long-Range Brillouin Optical Correlation Domain Analysis

Yahui Wang

and

Mingjiang Zhang

Sensors 2022, 22(16), 6062; https://doi.org/10.3390/s22166062 (registering DOI) - 13 Aug 2022

Abstract

Distributed optical fiber sensing technology has been widely applied in the areas of infrastructure health monitoring, national defense security, etc. The long-range high-spatial-resolution Brillouin optical correlation domain analysis (BOCDA) has extensive development and application prospects. In this paper, long-range BOCDAs are introduced and summarized. Several creative methods underpinning measurement range enlargement, including the interval enhancement of the adjacent correlation peak (CP), improvements in the signal-to-noise ratio, and the concurrent interrogation of multiple CPs, are discussed and experimentally verified, respectively. The main drawbacks in the present BOCDA schemes and avenues for future research and development have also been prospected. Full article

(This article belongs to the Special Issue Recent Trends in Distributed Optical Fiber Sensing Technology)

Open AccessArticle

Multi-Scale Safety Helmet Detection Based on RSSE-YOLOv3

Hongru Song

Sensors 2022, 22(16), 6061; https://doi.org/10.3390/s22166061 (registering DOI) - 13 Aug 2022

Abstract

Due to the shielding of dense and small targets, real-time detection of whether construction workers are wearing safety helmets suffers from low detection accuracy and missed detection. In this paper, a new detection algorithm based on YOLOv3 is proposed. Firstly, the parallel network RepVGG Skip Squeeze Excitation (RSSE) module is used to replace the Res8 module in the original YOLOv3 network. The RSSE module consists of 3 × 3 convolutional fusion channels and SSE branches fused. The introduction of the R-SSE module increases the network width, reduces the network depth, and improves the network detection speed and accuracy. Secondly, to avoid gradient disappearance and improve feature reuse, the residual module Res2 is used to replace the CBL×5 modules. Finally, the resolution of the input image is improved, and the four-scale feature prediction is used instead of the three-scale feature prediction to further improve the efficiency of detecting small targets. This paper also introduces the complete joint crossover (CIOU) to improve the loss function and positioning accuracy. The experimental results show that, compared with the original YOLOv3 algorithm, the improved algorithm improves the precision (P) by 3.9%, the recall (R) by 5.2%, and the average precision (mAP) by 4.7%, which significantly improves the performance of the detection. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Object Detection)

► Show Figures

Figure 1

Open AccessReview

Research Progress in Distributed Acoustic Sensing Techniques

and

Sensors 2022, 22(16), 6060; https://doi.org/10.3390/s22166060 (registering DOI) - 13 Aug 2022

Abstract

Distributed acoustic sensing techniques based on Rayleigh scattering have been widely used in many applications due to their unique advantages, such as long-distance detection, high spatial resolution, and wide sensing bandwidth. In this paper, we provide a review of the recent advancements in distributed acoustic sensing techniques. The research progress and operation principles are systematically reviewed. The pivotal technologies and solutions applied to distributed acoustic sensing are introduced in terms of polarization fading, coherent fading, spatial resolution, frequency response, signal-to-noise ratio, and sensing distance. The applications of the distributed acoustic sensing are covered, including perimeter security, earthquake monitoring, energy exploration, underwater positioning, and railway monitoring. The potential developments of the distributed acoustic sensing techniques are also discussed. Full article

(This article belongs to the Topic Advance and Applications of Fiber Optic Measurement)

► Show Figures

Figure 1

Open AccessArticle

Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers

Jorge Herranz Olazábal

Fokko Wieringa

Evelien Hermeling

and

Chris Van Hoof

Sensors 2022, 22(16), 6059; https://doi.org/10.3390/s22166059 (registering DOI) - 13 Aug 2022

Abstract

Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R² values of rPPG with rSPG (R² = 0.99) and ITA (R² = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

► Show Figures

Figure 1

Open AccessCommunication

Research on Safety Interlock System Design and Control Experiment of Combined Support and Anchor Equipment

and

Sensors 2022, 22(16), 6058; https://doi.org/10.3390/s22166058 (registering DOI) - 13 Aug 2022

Abstract

In view of the risk of collision with humans or equipment arising from a lack of protection in the operation process of combined support and anchor equipment on the heading face, this paper designs a safety interlock system for combined support and anchor equipment. Firstly, a mathematical model of hydraulic power system control and a valve control system based on feedforward–feedback optimization were established according to the power demand of the combined support and anchor equipment. Secondly, according to the reliability indexes of the safety interlock system, corresponding sensor, logic control and execution modules were designed. Ultrasonic sensor groups were arranged at the key positions of the combined support and anchor equipment to capture the position information in real time when the equipment was moving. Thus, the pump-valve hydraulic system was controlled through closed-loop feedback. The experimental results show that the safety interlock system of the combined support and anchor equipment can adjust the revolving speed of the permanent magnet synchronous motor (PMSM) in real time according to the distance from the obstacle, so as to control the pump outlet flow, and then perform interlocking safety control of the hydraulic cylinder’s movement speed. The system can effectively prevent damage to the surrounding equipment or personnel arising from equipment malfunction. Full article

(This article belongs to the Special Issue Proximity Detection through Sensing Technology)

Open AccessArticle

IEEE P2668 Compatible Evaluation Strategy for Smart Battery Management Systems

and

Sensors 2022, 22(16), 6057; https://doi.org/10.3390/s22166057 (registering DOI) - 13 Aug 2022

Abstract

In smart cities and smart industry, a Battery Management System (BMS) focuses on the intelligent supervision of the status (e.g., state of charge, temperature) of batteries (e.g., lithium battery, lead battery). Internet of Things (IoT) integration enhances the system's intelligence and convenience, making it a Smart BMS (SBMS). However, this also raises concerns regarding evaluating the SBMS in the wireless context in which these systems are installed. Considering the battery application, in particular, the SBMS will depend on several wireless communication characteristics, such as mobility, latency, fading, etc., necessitating a tailored evaluation strategy. This study proposes an IEEE P2668-Compatible SBMS Evaluation Strategy (SBMS-ES) to overcome this issue. The SBMS-ES is based on the IEEE P2668 worldwide standard, which aims to assess IoT solutions’ maturity. It evaluates the characteristics of the wireless environment for SBMS while considering battery factors. The SBMS-ES scores the candidates under numerous scenarios with various characteristics. A final score between 0 and 5 is given to indicate the performance of the SBMS regarding the application demands. The disadvantages of the SBMS solution and the most desired candidate can be found with the evaluated score. SBMS-ES provides guidance to avoid potential risks and mitigates the issues posed by an inadequate or unsatisfactory SBMS solution. A case study is depicted for illustration. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

Open AccessArticle

Speaker Adaptation on Articulation and Acoustics for Articulation-to-Speech Synthesis

Beiming Cao

Alan Wisler

and

Jun Wang

Sensors 2022, 22(16), 6056; https://doi.org/10.3390/s22166056 (registering DOI) - 13 Aug 2022

Abstract

Silent speech interfaces (SSIs) convert non-audio bio-signals, such as articulatory movement, to speech. This technology has the potential to recover the speech ability of individuals who have lost their voice but can still articulate (e.g., laryngectomees). Articulation-to-speech (ATS) synthesis is an algorithm design of SSI that has the advantages of easy-implementation and low-latency, and therefore is becoming more popular. Current ATS studies focus on speaker-dependent (SD) models to avoid large variations of articulatory patterns and acoustic features across speakers. However, these designs are limited by the small data size from individual speakers. Speaker adaptation designs that include multiple speakers’ data have the potential to address the issue of limited data size from single speakers; however, few prior studies have investigated their performance in ATS. In this paper, we investigated speaker adaptation on both the input articulation and the output acoustic signals (with or without direct inclusion of data from test speakers) using the publicly available electromagnetic articulatory (EMA) dataset. We used Procrustes matching and voice conversion for articulation and voice adaptation, respectively. The performance of the ATS models was measured objectively by the mel-cepstral distortions (MCDs). The synthetic speech samples were generated and are provided in the supplementary material. The results demonstrated the improvement brought by both Procrustes matching and voice conversion on speaker-independent ATS. With the direct inclusion of target speaker data in the training process, the speaker-adaptive ATS achieved a comparable performance to speaker-dependent ATS. To our knowledge, this is the first study that has demonstrated that speaker-adaptive ATS can achieve a non-statistically different performance to speaker-dependent ATS. Full article

(This article belongs to the Special Issue Future Speech Interfaces with Sensors and Machine Intelligence)

Open AccessReview

Smart E-Textiles: Overview of Components and Outlook

Rebecca R. Ruckdashel

Ninad Khadse

and

Jay Hoon Park

Sensors 2022, 22(16), 6055; https://doi.org/10.3390/s22166055 (registering DOI) - 13 Aug 2022

Abstract

Smart textiles have gained great interest from academia and industries alike, spanning interdisciplinary efforts from materials science, electrical engineering, art, design, and computer science. While recent innovation has been promising, unmet needs between the commercial and academic sectors are pronounced in this field, especially for electronic-based textiles, or e-textiles. In this review, we aim to address the gap by (i) holistically investigating e-textiles’ constituents and their evolution, (ii) identifying the needs and roles of each discipline and sector, and (iii) addressing the gaps between them. The components of e-textiles—base fabrics, interconnects, sensors, actuators, computers, and power storage/generation—can be made at multiscale levels of textile, e.g., fiber, yarn, fabric, coatings, and embellishments. The applications, current state, and sustainable future directions for e-textile fields are discussed, which encompasses health monitoring, soft robotics, education, and fashion applications. Full article

(This article belongs to the Special Issue State-of-the-Art Wearables Technology in USA)

Open AccessArticle

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

and

Sensors 2022, 22(16), 6054; https://doi.org/10.3390/s22166054 (registering DOI) - 13 Aug 2022

Abstract

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise. Full article

(This article belongs to the Section Internet of Things)

Open AccessArticle

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images

and

Sensors 2022, 22(16), 6053; https://doi.org/10.3390/s22166053 (registering DOI) - 13 Aug 2022

Abstract

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images. Full article

(This article belongs to the Special Issue Deep Learning for Pathology Detection and Diagnosis in Medical Imaging)

Open AccessArticle

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider:Horse Bodyweight Ratio in Horseback Riding

and

Sensors 2022, 22(16), 6052; https://doi.org/10.3390/s22166052 (registering DOI) - 13 Aug 2022

Abstract

As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features. Full article

(This article belongs to the Special Issue Applications of Image Analysis in Thermal Sensors Imaging)

Open AccessArticle

Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture

Theocharis Chatzis

Dimitrios Konstantinidis

and

Kosmas Dimitropoulos

Sensors 2022, 22(16), 6051; https://doi.org/10.3390/s22166051 (registering DOI) - 12 Aug 2022

Abstract

Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods. Full article

(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)

Open AccessArticle

Dual Stream Transmission and Downlink Power Control for Multiple LEO Satellites-Assisted IoT Networks

and

Sensors 2022, 22(16), 6050; https://doi.org/10.3390/s22166050 (registering DOI) - 12 Aug 2022

Abstract

The multi-satellites cooperative transmission can effectively increase the data rate that can be achieved by internet of things (IoT) terminals. However, the dynamic characteristics brought by low Earth orbit (LEO) satellites will seriously decrease the data rate and make the data rate fluctuate. In this paper, dual-stream transmission and downlink power control for multiple LEO satellites-assisted IoT networks are investigated. To mitigate the effects of the frequency offset caused by different LEO satellites, a multi-satellites synchronization scheme is proposed. Then, different power control schemes are given to resist the data rate fluctuation during the transmission. The simulation results show that the proposed schemes can effectively compensate for the varied frequency offset and keep the data rate stable. Full article

(This article belongs to the Special Issue Satellite Based IoT Networks for Emerging Applications)

Open AccessArticle

Electroactive Biofilms of Activated Sludge Microorganisms on a Nanostructured Surface as the Basis for a Highly Sensitive Biochemical Oxygen Demand Biosensor

and

Sensors 2022, 22(16), 6049; https://doi.org/10.3390/s22166049 (registering DOI) - 12 Aug 2022

Abstract

The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with phase contrast, scanning electron microscopy, and laser confocal microscopy. The features of charge transfer in the obtained electroactive biofilms were studied using the methods of cyclic voltammetry and electrochemical impedance spectroscopy. The rate constant of the interaction of microorganisms with the extracellular electron carrier (0.79 ± 0.03 dm³(g s)⁻¹) and the heterogeneous rate constant of electron transfer (0.34 ± 0.02 cm s⁻¹) were determined using the cyclic voltammetry method. These results revealed that the modification of the carbon nanotubes’ (CNT) electrode surface makes it possible to create electroactive biofilms. An analysis of the metrological and analytical characteristics of the created biosensors showed that the lower limit of the biosensor based on an electroactive biofilm of activated sludge is 0.41 mgO₂/dm³, which makes it possible to analyze almost any water sample. Analysis of 12 surface water samples showed a high correlation (R² = 0.99) with the results of the standard method for determining biochemical oxygen demand. Full article

(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)

Open AccessArticle

An Improved Circular Fringe Fourier Transform Profilometry

and

Sensors 2022, 22(16), 6048; https://doi.org/10.3390/s22166048 (registering DOI) - 12 Aug 2022

Abstract

Circular fringe projection profilometry (CFPP), as a branch of carrier fringe projection profilometry, has attracted research interest in recent years. Circular fringe Fourier transform profilometry (CFFTP) has been used to measure out-of-plane objects quickly because the absolute phase can be obtained by employing fewer fringes. However, the existing CFFTP method needs to solve a quadratic equation to calculate the pixel displacement amount related to the height of the object, in which the root-seeking process may get into trouble due to the phase error and the non-uniform period of reference fringe. In this paper, an improved CFFTP method based on a non-telecentric model is presented. The calculation of displacement amount is performed by solving a linear equation instead of a quadratic equation after introducing an extra projection of circular fringe with circular center translation. In addition, Gerchberg iteration is employed to eliminate phase error of the region close to the circular center, and the plane calibration technique is used to eliminate system error by establishing a displacement-to-height look-up table. The mathematical model and theoretical analysis are presented. Simulations and experiments have demonstrated the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in 3D Measurement Technology and Sensors)

Open AccessArticle

Image Classification of Wheat Rust Based on Ensemble Learning

and

Mohamed A. E. AbdelRahman

Sensors 2022, 22(16), 6047; https://doi.org/10.3390/s22166047 (registering DOI) - 12 Aug 2022

Abstract

Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat. Full article

(This article belongs to the Section Sensing and Imaging)

Journal Browser

► Journal Browser

Highly Accessed Articles

Latest Books

More Open Access Books...

E-Mail Alert

News

11 August 2022
Prof. Dr. Chris Rizos Appointed Section Editor-in-Chief of Section “Navigation and Positioning” in Sensors

25 July 2022
Meet Us at the 5th China Intelligent Optimization and Scheduling Conference, 12–14 August 2022, Taiyuan, China

11 July 2022
MDPI’s 2021 Best Paper Awards in “Engineering”—Winners Announced

More News & Announcements...

Topics

Edit a Topic

Topic in Entropy, Sensors

Advances in Biomedical Engineering from the Annual Conference of SEIB 2021 Topic Editors: Raúl Alcaraz, Elisabete Aramendi, Raimon Jane, Gema García-Sáez, Gema Prats-Boluda, Javier Reina-Tosina, Roberto Hornero, Patricia Sánchez-González
Deadline: 30 September 2022

Topic in Atmosphere, Buildings, IJERPH, Sensors

Indoor and Outdoor Air Quality in the Era of COVID-19 Topic Editors: Jiayu Li, Xilei Dai, Junjie Liu
Deadline: 31 October 2022

Topic in Biosensors, Chemosensors, Sensors

Frontiers in Fabrication of Screen-Printed Electrodes and Their Applications Topic Editors: José Manuel Dı́az-Cruz, Antonio Florido, Núria Serrano
Deadline: 15 December 2022

Topic in Remote Sensing, Sensors, Geomatics, Smart Cities, Vehicles

Information Sensing Technology for Intelligent/Driverless Vehicle Topic Editors: Shiyang Tang, Zhanye Chen, Yan Huang, Ping Guo
Deadline: 31 December 2022