-
Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management
-
Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results
-
Development of Magnetocardiograph without Magnetically Shielded Room Using High-Detectivity TMR Sensors
-
FPGA-Based Smart Sensor to Detect Current Transformer Saturation during Inrush Current Measurement
-
Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
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 is provided to authors approximately 15 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second 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 Targets.
Impact Factor:
3.847 (2021);
5-Year Impact Factor:
4.050 (2021)
Latest Articles
Detection of Missing Insulator Caps Based on Machine Learning and Morphological Detection
Sensors 2023, 23(3), 1557; https://doi.org/10.3390/s23031557 (registering DOI) - 31 Jan 2023
Abstract
Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper
[...] Read more.
Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper proposes a detection method for missing insulator caps in these materials. First, according to the grayscale and color characteristics of these insulators, similar characteristic regions of the insulators are extracted from inspection images, and candidate boxes are generated based on these characteristic regions. Second, the images captured by these boxes are input into the classifier composed of SVM (Support Vector Machine) to identify and locate the insulators. The accuracy, recall and average accuracy of the classifier are all higher than 90%. Finally, this paper proposes a processing method based on the insulator morphology to determine whether an insulator cap is missing. The proposed method can also detect the number of remaining insulators, which can help power supply enterprises to evaluate the degree of insulator damage.
Full article
(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)
Open AccessArticle
Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEE-II and SLE Strategy
Sensors 2023, 23(3), 1556; https://doi.org/10.3390/s23031556 (registering DOI) - 31 Jan 2023
Abstract
The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed
[...] Read more.
The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study’s approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.
Full article
(This article belongs to the Topic Wireless Sensor Networks)
Open AccessArticle
PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation
Sensors 2023, 23(3), 1555; https://doi.org/10.3390/s23031555 (registering DOI) - 31 Jan 2023
Abstract
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework,
[...] Read more.
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50–100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson’s Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset.
Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
Open AccessArticle
K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks
Sensors 2023, 23(3), 1554; https://doi.org/10.3390/s23031554 (registering DOI) - 31 Jan 2023
Abstract
Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems
[...] Read more.
Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.
Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
Open AccessArticle
Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking
by
, , , , and
Sensors 2023, 23(3), 1553; https://doi.org/10.3390/s23031553 (registering DOI) - 31 Jan 2023
Abstract
The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion
[...] Read more.
The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.
Full article
(This article belongs to the Special Issue Advanced Electromagnetic Sensors in Environmental, Industrial and Medical Applications II)
Open AccessArticle
A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer
by
, , , , , , , , , and
Sensors 2023, 23(3), 1552; https://doi.org/10.3390/s23031552 (registering DOI) - 31 Jan 2023
Abstract
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen
[...] Read more.
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
Open AccessArticle
Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector
by
and
Sensors 2023, 23(3), 1551; https://doi.org/10.3390/s23031551 (registering DOI) - 31 Jan 2023
Abstract
This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid−40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar.
[...] Read more.
This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid−40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non−repetitive scanning pattern of the unmodified sensor. Due to preserving the non−repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid−state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution.
Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City)
Open AccessArticle
Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish
by
, , , , , and
Sensors 2023, 23(3), 1550; https://doi.org/10.3390/s23031550 (registering DOI) - 31 Jan 2023
Abstract
Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement
[...] Read more.
Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement of fish to facilitate human interaction. This study proposed an interactive robot, a robotic fish, to alter fish swarm behaviors by performing an effective, unobstructed, yet necessary, defined set of actions to enhance human interaction. The approach incorporated a minimalistic but futuristic physical design of the robotic fish with cameras and infrared (IR) sensors, and developed a fish-detecting and swarm pattern-recognizing algorithm. The fish-detecting algorithm was implemented using background subtraction and moving average algorithms with an accuracy of 78%, while the swarm pattern detection implemented with a Convolutional Neural Network (CNN) resulted in a 77.32% accuracy rate. By effectively controlling the behavior and swimming patterns of fish through the smooth movements of the robotic fish, we evaluated the success through repeated trials. Feedback from a randomly selected unbiased group of subjects revealed that the robotic fish improved human interaction with fish by using the proposed set of maneuvers and behavior.
Full article
(This article belongs to the Special Issue Marine Environmental Perception and Underwater Detection)
Open AccessCommunication
Peculiarities of Resonant Absorption of Electromagnetic Signals in Multilayer Bolometric Sensors
by
, , , , and
Sensors 2023, 23(3), 1549; https://doi.org/10.3390/s23031549 (registering DOI) - 31 Jan 2023
Abstract
We examine the effect of resonant absorption of electromagnetic signals in a silicon semiconductor plasma layer when the dielectric plate is placed behind it both experimentally and numerically. It is shown that such plate acts as a dielectric resonator and can significantly increase
[...] Read more.
We examine the effect of resonant absorption of electromagnetic signals in a silicon semiconductor plasma layer when the dielectric plate is placed behind it both experimentally and numerically. It is shown that such plate acts as a dielectric resonator and can significantly increase the electromagnetic energy absorption in the semiconductor for certain frequencies determined by the dielectric plate parameters. Numerical modelling of the effect is performed under the conditions of conducted experiment. The numerical results are found to be in qualitative agreement with experimental ones. This study confirms the proposed earlier method of increasing the efficiency of bolometric-type detectors of electromagnetic radiation.
Full article
(This article belongs to the Special Issue Technologies of Highly Efficient Telecommunication Systems and Devices)
Open AccessArticle
Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images
Sensors 2023, 23(3), 1548; https://doi.org/10.3390/s23031548 (registering DOI) - 31 Jan 2023
Abstract
Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to
[...] Read more.
Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method.
Full article
(This article belongs to the Topic Intelligent Transportation Systems)
►▼
Show Figures

Figure 1
Open AccessArticle
Validation of Inertial Sensors to Evaluate Gait Stability
Sensors 2023, 23(3), 1547; https://doi.org/10.3390/s23031547 (registering DOI) - 31 Jan 2023
Abstract
The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an
[...] Read more.
The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an inertial sensor-based motion-capture suit could accurately assess gait stability. Healthy adult participants were asked to walk normally, with eyes closed, with approximately twice their normal step width, and in tandem. Their motion was simultaneously measured by inertial measurement units (IMU) and optical motion capture (Optical). Gait stability was assessed by calculating the margin of stability (MoS), short-term Lyapunov exponents, and step variability, along with basic gait parameters, using each system. We found that IMUs were able to detect the same differences among conditions as Optical for all but one of the measures. Bland–Altman and intraclass correlation (ICC) analysis demonstrated that mediolateral parameters (step width and mediolateral MoS) were measured less accurately by IMUs compared to their anterior-posterior equivalents (step length and anterior-posterior MoS). Our results demonstrate that IMUs can be used to evaluate gait stability through detecting changes in stability-related measures, but that the magnitudes of these measures might not be accurate or reliable, especially in the mediolateral direction.
Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
►▼
Show Figures

Figure 1
Open AccessArticle
Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Element Reinforcement Learning
Sensors 2023, 23(3), 1546; https://doi.org/10.3390/s23031546 (registering DOI) - 31 Jan 2023
Abstract
Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data
[...] Read more.
Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta−reinforcement learning (MRL) is realized. The meta−reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta−reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.
Full article
(This article belongs to the Section Sensor Networks)
►▼
Show Figures

Figure 1
Open AccessReview
Development and Prospect of Smart Materials and Structures for Aerospace Sensing Systems and Applications
Sensors 2023, 23(3), 1545; https://doi.org/10.3390/s23031545 (registering DOI) - 31 Jan 2023
Abstract
The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique
[...] Read more.
The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique physical properties and excellent integration properties, and they perform well as sensors or actuators in the aviation industry, providing a solid material foundation for various intelligent applications in the aviation industry. As a popular smart material, piezoelectric materials have a large number of application research in structural health monitoring, energy harvest, vibration and noise control, damage control, and other fields. As a unique material with deformation ability, shape memory materials have their own outstanding performance in the field of shape control, low-shock release, vibration control, and impact absorption. At the same time, as a material to assist other structures, it also has important applications in the fields of sealing connection and structural self-healing. Giant magnetostrictive material is a representative advanced material, which has unique application advantages in guided wave monitoring, vibration control, energy harvest, and other directions. In addition, giant magnetostrictive materials themselves have high-resolution output, and there are many studies in the direction of high-precision actuators. Some smart materials are summarized and discussed in the above application directions, aiming at providing a reference for the initial development of follow-up related research.
Full article
(This article belongs to the Special Issue Smart Materials and Structures for Aerospace Sensing Systems and Applications)
►▼
Show Figures

Figure 1
Open AccessArticle
Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence
Sensors 2023, 23(3), 1544; https://doi.org/10.3390/s23031544 (registering DOI) - 31 Jan 2023
Abstract
The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs.
[...] Read more.
The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.
Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Railway Infrastructures)
►▼
Show Figures

Figure 1
Open AccessArticle
Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration
Sensors 2023, 23(3), 1543; https://doi.org/10.3390/s23031543 (registering DOI) - 31 Jan 2023
Abstract
An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The
[...] Read more.
An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.
Full article
(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)
►▼
Show Figures

Figure 1
Open AccessArticle
A TinyML Deep Learning Approach for Indoor Tracking of Assets
Sensors 2023, 23(3), 1542; https://doi.org/10.3390/s23031542 (registering DOI) - 31 Jan 2023
Abstract
Positioning systems have gained paramount importance for many different productive sector; however, traditional systems such as Global Positioning System (GPS) have failed to offer accurate and scalable solutions for indoor positioning requirements. Nowadays, alternative solutions such as fingerprinting allow the recognition of the
[...] Read more.
Positioning systems have gained paramount importance for many different productive sector; however, traditional systems such as Global Positioning System (GPS) have failed to offer accurate and scalable solutions for indoor positioning requirements. Nowadays, alternative solutions such as fingerprinting allow the recognition of the characteristic signature of a location based on RF signal acquisition. In this work, a machine learning (ML) approach has been considered in order to classify the RSSI information acquired by multiple scanning stations from TAG broadcasting messages. TinyML has been considered for this project, as it is a rapidly growing technological paradigm that aims to assist the design and implementation of ML mechanisms in resource-constrained embedded devices. Hence, this paper presents the design, implementation, and deployment of embedded devices capable of communicating and sending information to a central system that determines the location of objects in a defined environment. A neural network (deep learning) is trained and deployed on the edge, allowing the multiple external error factors that affect the accuracy of traditional position estimation algorithms to be considered. Edge Impulse is selected as the main platform for data standardization, pre-processing, model training, evaluation, and deployment. The final deployed system is capable of classifying real data from the installed TAGs, achieving a classification accuracy of , which can be increased to when a post-processing stage is implemented.
Full article
(This article belongs to the Section Internet of Things)
►▼
Show Figures

Figure 1
Open AccessArticle
Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
by
, , , , and
Sensors 2023, 23(3), 1541; https://doi.org/10.3390/s23031541 (registering DOI) - 31 Jan 2023
Abstract
Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector
[...] Read more.
Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.
Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming)
►▼
Show Figures

Figure 1
Open AccessArticle
An Advanced Rider-Cornering-Assistance System for PTW Vehicles Developed Using ML KNN Method
Sensors 2023, 23(3), 1540; https://doi.org/10.3390/s23031540 (registering DOI) - 31 Jan 2023
Abstract
The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics,
[...] Read more.
The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.
Full article
(This article belongs to the Special Issue Pattern Recognition Using Neural Networks)
►▼
Show Figures

Figure 1
Open AccessArticle
A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation
Sensors 2023, 23(3), 1539; https://doi.org/10.3390/s23031539 - 31 Jan 2023
Abstract
A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode
[...] Read more.
A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode and piezo-film, respectively. NIRS measurements are performed using the photosensor. Muscular activity is then analyzed in detail using the three types of data obtained. In experiments, the EMG, MMG and NIRS signals were measured for isometric ramp contraction at the forearm and cycling exercise of the lateral vastus muscle with stepped increments of the load using the layered sensor. The results showed that it was possible to perform simultaneous EMG, MMG and NIRS measurements at a local position using the proposed sensor. It is suggested that the proposed sensor has the potential to evaluate muscular activity during exercise, although the detection of the anaerobic threshold has not been clearly addressed.
Full article
(This article belongs to the Section Sensors Development)
►▼
Show Figures

Figure 1
Open AccessArticle
Methodology and Tool Development for Mobile Device Cameras Calibration and Evaluation of the Results
Sensors 2023, 23(3), 1538; https://doi.org/10.3390/s23031538 - 30 Jan 2023
Abstract
In this paper, a procedure for calibrating the image sensors of mobile devices and evaluating their results was developed and implemented in a software application. Regarding the calibration, two methods were used, an OpenCV function and a photogrammetry method, which used the same
[...] Read more.
In this paper, a procedure for calibrating the image sensors of mobile devices and evaluating their results was developed and implemented in a software application. Regarding the calibration, two methods were used, an OpenCV function and a photogrammetry method, which used the same camera model. In evaluating the calibration results, a method is proposed that uses single-image rectification to examine the performance of the calibration parameters in a practical and supervisory way. After an experiment followed by a study, a standard is proposed regarding the number and shooting angles of the photographs that should be used in the calibration. During the development, problems related to processing large images and automating processes were solved. Finally, the procedure and software application were tested in a case study.
Full article
(This article belongs to the Special Issue Sensing Technologies for Precision Measurements)
►▼
Show Figures

Figure 1

Journal Menu
► ▼ Journal Menu-
- Sensors Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor's Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal Browser-
arrow_forward_ios
Forthcoming issue
arrow_forward_ios Current issue - Vol. 23 (2023)
- Vol. 22 (2022)
- Vol. 21 (2021)
- Vol. 20 (2020)
- Vol. 19 (2019)
- Vol. 18 (2018)
- Vol. 17 (2017)
- Vol. 16 (2016)
- Vol. 15 (2015)
- Vol. 14 (2014)
- Vol. 13 (2013)
- Vol. 12 (2012)
- Vol. 11 (2011)
- Vol. 10 (2010)
- Vol. 9 (2009)
- Vol. 8 (2008)
- Vol. 7 (2007)
- Vol. 6 (2006)
- Vol. 5 (2005)
- Vol. 4 (2004)
- Vol. 3 (2003)
- Vol. 2 (2002)
- Vol. 1 (2001)
Highly Accessed Articles
Latest Books
E-Mail Alert
News
31 January 2023
Meet Us at the IEEE International Conference on Robotics and Automation (ICRA 2023), 29 May–2 June 2023, London, UK
Meet Us at the IEEE International Conference on Robotics and Automation (ICRA 2023), 29 May–2 June 2023, London, UK

13 January 2023
Sensors | Season’s Greetings from Dr. Edgar Muñoz—Section Editor-in-Chief of “Nanosensors”
Sensors | Season’s Greetings from Dr. Edgar Muñoz—Section Editor-in-Chief of “Nanosensors”
Topics
Topic in
Sustainability, Materials, Sensors, Applied Sciences, Processes
Sustainability in Buildings: New Trends in the Management of Construction and Demolition Waste
Topic Editors: Carlos Morón Fernández, Daniel Ferrández VegaDeadline: 31 January 2023
Topic in
Algorithms, Games, Information, Mathematics, Sensors
Game Theory and Applications
Topic Editors: Mahendra Piraveenan, Samit BhattacharryaDeadline: 28 February 2023
Topic in
JCP, Sensors, Future Internet, Algorithms, Cryptography
Next Generation of Security and Privacy in IoT, Industry 4.0, 5G Systems and Beyond
Topic Editors: Savio Sciancalepore, Giuseppe Piro, Nicola ZannoneDeadline: 31 March 2023
Topic in
Energies, Sensors, Processes, Electronics, Smart Cities
Digitalization for Energy Systems
Topic Editors: Shengrong Bu, Shichao Liu, Dawei QiuDeadline: 15 April 2023

Conferences
Special Issues
Special Issue in
Sensors
Sensors for Construction Automation and Management
Guest Editors: Reza Maalek, Derek Lichti, Shahrokh MaalekDeadline: 31 January 2023
Special Issue in
Sensors
Wearable Devices and Sensors for Innovative Monitoring Systems in the 4.0 Era
Guest Editors: Annarita Tedesco, Leopoldo Angrisani, Egidio De BenedettoDeadline: 20 February 2023
Special Issue in
Sensors
Sensors for Distributed Monitoring
Guest Editors: Nicola Giaquinto, Francesco Adamo, Maurizio SpadavecchiaDeadline: 28 February 2023
Special Issue in
Sensors
Internet of Things Based Multimedia Sensor Networks
Guest Editor: Hussain Al-RizzoDeadline: 20 March 2023
Topical Collections
Topical Collection in
Sensors
Robotic and Sensor Technologies in Environmental Exploration and Monitoring
Collection Editors: Jacopo Aguzzi, Corrado Costa, Sergio Stefanni, Valerio Funari
Topical Collection in
Sensors
Microfluidic Sensors
Collection Editors: Sabina Merlo, Klaus Stefan Drese