Journal Description
Sensors
Sensors
is an 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, 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 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- 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.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring
Sensors 2024, 24(7), 2185; https://doi.org/10.3390/s24072185 (registering DOI) - 28 Mar 2024
Abstract
The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important
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The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs’ health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs’ health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs’ health and welfare.
Full article
(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)
Open AccessArticle
Piezoresistive Porous Composites with Triply Periodic Minimal Surface Structures Prepared by Self-Resistance Electric Heating and 3D Printing
by
Ke Peng, Tianyu Yu, Pan Wu and Mingjun Chen
Sensors 2024, 24(7), 2184; https://doi.org/10.3390/s24072184 (registering DOI) - 28 Mar 2024
Abstract
Three-dimensional flexible piezoresistive porous sensors are of interest in health diagnosis and wearable devices. In this study, conductive porous sensors with complex triply periodic minimal surface (TPMS) structures were fabricated using the 3D printed sacrificial mold and enhancement of MWCNTs. A new curing
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Three-dimensional flexible piezoresistive porous sensors are of interest in health diagnosis and wearable devices. In this study, conductive porous sensors with complex triply periodic minimal surface (TPMS) structures were fabricated using the 3D printed sacrificial mold and enhancement of MWCNTs. A new curing routine by the self-resistance electric heating was implemented. The porous sensors were designed with different pore sizes and unit cell types of the TPMS (Diamond (D), Gyroid (G), and I-WP (I)). The impact of pore characteristics and the hybrid fabrication technique on the compressive properties and piezoresistive response of the developed porous sensors was studied. The results indicate that the porous sensors cured by the self-resistance electric heating could render a uniform temperature distribution in the composites and reduce the voids in the walls, exhibiting a higher elastic modulus and a better piezoresistive response. Among these specimens, the specimen with the D-based structure cured by self-resistance electric heating showed the highest responsive strain (61%), with a corresponding resistance response value of 0.97, which increased by 10.26% compared to the specimen heated by the external heat sources. This study provides a new perspective on design and fabrication of porous materials with piezoresistive functionalities, particularly in the realm of flexible and portable piezoresistive sensors.
Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
Open AccessArticle
Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment
by
Jonathan Flores, Ivan Gonzalez-Hernandez, Sergio Salazar, Rogelio Lozano and Christian Reyes
Sensors 2024, 24(7), 2183; https://doi.org/10.3390/s24072183 (registering DOI) - 28 Mar 2024
Abstract
This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation
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This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms.
Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
Open AccessArticle
Ancient Chinese Character Recognition with Improved Swin-Transformer and Flexible Data Enhancement Strategies
by
Yi Zheng, Yi Chen, Xianbo Wang, Donglian Qi and Yunfeng Yan
Sensors 2024, 24(7), 2182; https://doi.org/10.3390/s24072182 (registering DOI) - 28 Mar 2024
Abstract
The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat
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The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.
Full article
(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)
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Open AccessArticle
Piezoceramics Actuator with Attached Mass for Active Vibration Diagnostics of Reinforced Concrete Structures
by
Igor Shardakov, Aleksey Shestakov, Irina Glot, Georgii Gusev, Valery Epin and Roman Tsvetkov
Sensors 2024, 24(7), 2181; https://doi.org/10.3390/s24072181 (registering DOI) - 28 Mar 2024
Abstract
One of the effective methods of non-destructive testing of structures is active vibration diagnostics. This approach consists of the local dynamic impact of the actuator on the structure and the registration of the vibration response. Testing of massive reinforced concrete structures is carried
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One of the effective methods of non-destructive testing of structures is active vibration diagnostics. This approach consists of the local dynamic impact of the actuator on the structure and the registration of the vibration response. Testing of massive reinforced concrete structures is carried out with the use of actuators, which are able to create sufficiently high-impact loads. The actuators, which are based on piezoelectric elements, cannot provide a sufficient level of force and the areas where it is possible to register the vibrations excited by such actuators are quite small. In this paper, we propose a variant of a piezoactuator with attached mass, which ensures an increase in the level of dynamic impact on the structure. The effectiveness of this version is verified by numerical modeling of the dynamic interaction of the actuator with a concrete slab. The simulation was carried out within the framework of the theory of elasticity and coupled electroelasticity. An algorithm for selecting the value of the attached mass is described. It is shown that when vibrations are excited in a massive concrete slab, an actuator with an attached mass of 1.3 kg provides a 10,000-fold increase in the force compared to an actuator without attached mass. In the pulse mode, a 100-fold increase in force is achieved.
Full article
(This article belongs to the Special Issue Recent Developments and Applications of Advanced Sensors in Buildings)
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Open AccessReview
Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems
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Jialin Liu, Yitao Wang, Yiwei Liu, Yuanzhao Wu, Baoru Bian, Jie Shang and Runwei Li
Sensors 2024, 24(7), 2180; https://doi.org/10.3390/s24072180 (registering DOI) - 28 Mar 2024
Abstract
As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people’s daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant
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As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people’s daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.
Full article
(This article belongs to the Special Issue Wearable and Implantable Electrochemical Sensors)
Open AccessArticle
Feedback Beamforming in the Time Domain
by
Zvi Aharon Herscovici and Israel Cohen
Sensors 2024, 24(7), 2179; https://doi.org/10.3390/s24072179 (registering DOI) - 28 Mar 2024
Abstract
Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands.
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Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.
Full article
(This article belongs to the Special Issue Feature Papers in Physical Sensors 2023)
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Open AccessArticle
Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot
by
Xinxing Tang, Hongxin Pei and Deyong Zhang
Sensors 2024, 24(7), 2178; https://doi.org/10.3390/s24072178 (registering DOI) - 28 Mar 2024
Abstract
Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential
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Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.
Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
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Open AccessArticle
Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation for ADAS/AD Development
by
Kelvin Walenta, Simon Genser and Selim Solmaz
Sensors 2024, 24(7), 2177; https://doi.org/10.3390/s24072177 (registering DOI) - 28 Mar 2024
Abstract
In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for
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In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar’s perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar’s perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model’s effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model’s applicability.
Full article
(This article belongs to the Section Vehicular Sensing)
Open AccessArticle
Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring
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Jacopo Corona, Stefano Tondini, Duccio Gallichi Nottiani, Riccardo Scilla, Andrea Gambaro, Wilmer Pasut, Francesco Babich and Roberto Lollini
Sensors 2024, 24(7), 2176; https://doi.org/10.3390/s24072176 (registering DOI) - 28 Mar 2024
Abstract
The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users’ health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of
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The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users’ health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.
Full article
(This article belongs to the Special Issue Integrated Sensor Systems for Environmental Applications)
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Open AccessArticle
Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks
by
Hyunseok Choi, Yoonhyeong Lee, Gayeong Kim, Euisin Lee and Youngju Nam
Sensors 2024, 24(7), 2175; https://doi.org/10.3390/s24072175 (registering DOI) - 28 Mar 2024
Abstract
Vehicular clouds represent an appealing approach, leveraging vehicles’ resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address
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Vehicular clouds represent an appealing approach, leveraging vehicles’ resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.
Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)
Open AccessArticle
A Polyvinylpyrrolidone Nanofibrous Sensor Doubly Decorated with Mesoporous Graphene to Selectively Detect Acetic Acid Vapors
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Paolo Papa, Emiliano Zampetti, Fabricio Nicolas Molinari, Fabrizio De Cesare, Corrado Di Natale, Giovanna Tranfo and Antonella Macagnano
Sensors 2024, 24(7), 2174; https://doi.org/10.3390/s24072174 (registering DOI) - 28 Mar 2024
Abstract
An original approach has been proposed for designing a nanofibrous (NF) layer using UV-cured polyvinylpyrrolidone (PVP) as a matrix, incorporating mesoporous graphene carbon (MGC) nanopowder both inside and outside the fibers, creating a sandwich-like structure. This architecture is intended to selectively adsorb and
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An original approach has been proposed for designing a nanofibrous (NF) layer using UV-cured polyvinylpyrrolidone (PVP) as a matrix, incorporating mesoporous graphene carbon (MGC) nanopowder both inside and outside the fibers, creating a sandwich-like structure. This architecture is intended to selectively adsorb and detect acetic acid vapors, which are known to cause health issues in exposed workers. The nanocomposite MGC-PVP-NFs layer was fabricated through electrospinning deposition onto interdigitated microelectrodes (IDEs) and stabilized under UV–light irradiation. To enhance the adhesion of MGC onto the surface of the nanocomposite polymeric fibers, the layer was dipped in a suspension of polyethyleneimine (PEI) and MGC. The resulting structure demonstrated promising electrical and sensing properties, including rapid responses, high sensitivity, good linearity, reversibility, repeatability, and selectivity towards acetic acid vapors. Initial testing was conducted in a laboratory using a bench electrometer, followed by validation in a portable sensing device based on consumer electronic components (by ARDUINO®). This portable system was designed to provide a compact, cost-effective solution with high sensing capabilities. Under room temperature and ambient air conditions, both laboratory and portable tests exhibited favorable linear responses, with detection limits of 0.16 and 1 ppm, respectively.
Full article
(This article belongs to the Special Issue Sensor Systems for the Chemical and Biochemical Safety of Working Places)
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Open AccessArticle
An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System
by
Bin Wu, Nobuo Funabiki, Sujan Chandra Roy, Md. Mahbubur Rahman, Dezheng Kong and Shihao Fang
Sensors 2024, 24(7), 2173; https://doi.org/10.3390/s24072173 (registering DOI) - 28 Mar 2024
Abstract
With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the
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With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.
Full article
(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT)
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Open AccessArticle
Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms
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Eric Rodene, Gayara Demini Fernando, Ved Piyush, Yufeng Ge, James C. Schnable, Souparno Ghosh and Jinliang Yang
Sensors 2024, 24(7), 2172; https://doi.org/10.3390/s24072172 (registering DOI) - 28 Mar 2024
Abstract
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field
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Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.
Full article
(This article belongs to the Special Issue AI-Driven Sensing for Image Processing and Recognition)
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Open AccessArticle
Neurophysiological and Autonomic Correlates of Metacognitive Control of and Resistance to Distractors in Ecological Setting: A Pilot Study
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Michela Balconi, Carlotta Acconito, Roberta A. Allegretta and Laura Angioletti
Sensors 2024, 24(7), 2171; https://doi.org/10.3390/s24072171 (registering DOI) - 28 Mar 2024
Abstract
In organisational contexts, professionals are required to decide dynamically and prioritise unexpected external inputs deriving from multiple sources. In the present study, we applied a multimethodological neuroscientific approach to investigate the ability to resist and control ecological distractors during decision-making and to explore
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In organisational contexts, professionals are required to decide dynamically and prioritise unexpected external inputs deriving from multiple sources. In the present study, we applied a multimethodological neuroscientific approach to investigate the ability to resist and control ecological distractors during decision-making and to explore whether a specific behavioural, neurophysiological (i.e., delta, theta, alpha and beta EEG band), or autonomic (i.e., heart rate—HR, and skin conductance response—SCR) pattern is correlated with specific personality profiles, collected with the 10-item Big Five Inventory. Twenty-four participants performed a novel Resistance to Ecological Distractors (RED) task aimed at exploring the ability to resist and control distractors and the level of coherence and awareness of behaviour (metacognition ability), while neurophysiological and autonomic measures were collected. The behavioural results highlighted that effectiveness in performance did not require self-control and metacognition behaviour and that being proficient in metacognition can have an impact on performance. Moreover, it was shown that the ability to resist ecological distractors is related to a specific autonomic profile (HR and SCR decrease) and that the neurophysiological and autonomic activations during task execution correlate with specific personality profiles. The agreeableness profile was negatively correlated with the EEG theta band and positively with the EEG beta band, the conscientiousness profile was negatively correlated with the EEG alpha band, and the extroversion profile was positively correlated with the EEG beta band. Taken together, these findings describe and disentangle the hidden relationship that lies beneath individuals’ decision to inhibit or activate intentionally a specific behaviour, such as responding, or not, to an external stimulus, in ecological conditions.
Full article
(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging: 2nd Edition)
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Open AccessArticle
HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm
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Hongmei Chen, Haifeng Wang, Zilong Liu, Dongbing Gu and Wen Ye
Sensors 2024, 24(7), 2170; https://doi.org/10.3390/s24072170 (registering DOI) - 28 Mar 2024
Abstract
Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception
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Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.
Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
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Open AccessArticle
Molecular Weights of Polyethyleneimine-Dependent Physicochemical Tuning of Gold Nanoparticles and FRET-Based Turn-On Sensing of Polymyxin B
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Atul Kumar Tiwari, Munesh Kumar Gupta, Ramovatar Meena, Prem C. Pandey and Roger J. Narayan
Sensors 2024, 24(7), 2169; https://doi.org/10.3390/s24072169 (registering DOI) - 28 Mar 2024
Abstract
Environmental monitoring and the detection of antibiotic contaminants require expensive and time-consuming techniques. To overcome these challenges, gold nanoparticle-mediated fluorometric “turn-on” detection of Polymyxin B (PMB) in an aqueous medium was undertaken. The molecular weight of polyethyleneimine (PEI)-dependent physicochemical tuning of gold nanoparticles
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Environmental monitoring and the detection of antibiotic contaminants require expensive and time-consuming techniques. To overcome these challenges, gold nanoparticle-mediated fluorometric “turn-on” detection of Polymyxin B (PMB) in an aqueous medium was undertaken. The molecular weight of polyethyleneimine (PEI)-dependent physicochemical tuning of gold nanoparticles (PEI@AuNPs) was achieved and employed for the same. The three variable molecular weights of branched polyethyleneimine (MW 750, 60, and 1.3 kDa) molecules controlled the nano-geometry of the gold nanoparticles along with enhanced stabilization at room temperature. The synthesized gold nanoparticles were characterized through various advanced techniques. The results revealed that polyethyleneimine-stabilized gold nanoparticles (PEI@AuNP-1-3) were 4.5, 7.0, and 52.5 nm in size with spherical shapes, and the zeta potential values were 29.9, 22.5, and 16.6 mV, respectively. Accordingly, the PEI@AuNPs probes demonstrated high sensitivity and selectivity, with a linear relationship curve over a concentration range of 1–6 μM for polymyxin B. The limit of detection (LOD) was calculated as 8.5 nM. This is the first unique report of gold nanoparticle nano-geometry-dependent FRET-based turn-on detection of PMB in an aqueous medium. We believe that this approach would offer a complementary strategy for the development of a highly sophisticated and advanced sensing system for PMB and act as a template for the development of new nanomaterial-based engineered sensors for rapid antibiotic detection in environmental as well as biological samples.
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(This article belongs to the Section Sensor Materials)
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Open AccessArticle
Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds
by
Alejandro Morales-Martín, Francisco-Javier Mesas-Carrascosa, Pedro Antonio Gutiérrez, Fernando-Juan Pérez-Porras, Víctor Manuel Vargas and César Hervás-Martínez
Sensors 2024, 24(7), 2168; https://doi.org/10.3390/s24072168 (registering DOI) - 28 Mar 2024
Abstract
Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing
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Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student’s t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).
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(This article belongs to the Special Issue Remote Sensing for Spatial Information Extraction and Process)
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Coded Excitation for Ultrasonic Testing: A Review
by
Chenxin Weng, Xu Gu and Haoran Jin
Sensors 2024, 24(7), 2167; https://doi.org/10.3390/s24072167 (registering DOI) - 28 Mar 2024
Abstract
Originating in the early 20th century, ultrasonic testing has found increasingly extensive applications in medicine, industry, and materials science. Achieving both a high signal-to-noise ratio and high efficiency is crucial in ultrasonic testing. The former means an increase in imaging clarity as well
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Originating in the early 20th century, ultrasonic testing has found increasingly extensive applications in medicine, industry, and materials science. Achieving both a high signal-to-noise ratio and high efficiency is crucial in ultrasonic testing. The former means an increase in imaging clarity as well as the detection depth, while the latter facilitates a faster refresh of the image. It is difficult to balance these two indicators with a conventional short pulse to excite the probe, so in general handling methods, these two factors have a trade-off. To solve the above problems, coded excitation (CE) can increase the pulse duration and offers great potential to improve the signal-to-noise ratio with equivalent or even higher efficiency. In this paper, we first review the fundamentals of CE, including signal modulation, signal transmission, signal reception, pulse compression, and optimization methods. Then, we introduce the application of CE in different areas of ultrasonic testing, with a focus on industrial bulk wave single-probe detection, industrial guided wave detection, industrial bulk wave phased array detection, and medical phased array imaging. Finally, we point out the advantages as well as a few future directions of CE.
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(This article belongs to the Special Issue Ultrasound Imaging and Sensing for Nondestructive Testing)
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Open AccessCommunication
The Impact of Nozzle Opening Thickness on Flow Characteristics and Primary Electron Beam Scattering in an Environmental Scanning Electron Microscope
by
Jiří Maxa, Pavla Šabacká, Jan Mazal, Vilém Neděla, Tomáš Binar, Petr Bača, Jaroslav Talár, Robert Bayer and Pavel Čudek
Sensors 2024, 24(7), 2166; https://doi.org/10.3390/s24072166 (registering DOI) - 28 Mar 2024
Abstract
This paper describes the methodology of combining experimental measurements with mathematical–physics analyses in the investigation of flow in the aperture and nozzle. The aperture and nozzle separate the differentially pumped chamber from the specimen chamber in an environmental scanning electron microscope (ESEM). Experimental
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This paper describes the methodology of combining experimental measurements with mathematical–physics analyses in the investigation of flow in the aperture and nozzle. The aperture and nozzle separate the differentially pumped chamber from the specimen chamber in an environmental scanning electron microscope (ESEM). Experimental measurements are provided by temperature and pressure sensors that meet the demanding conditions of cryogenic temperature zones and low pressures. This aperture maintains the required pressure difference between the chambers. Since it separates the large pressure gradient, critical flow occurs on it and supersonic gas flow with the characteristic properties of critical flow in the state variables occurs behind it. As a primary electron beam passes through the differential pumped chamber and the given aperture, the aperture is equipped with a nozzle. The shape of the nozzle strongly influences the character of the supersonic flow. The course of state variables is also strongly influenced by this shape; thus, it affects the number of collisions the primary beam’s electrons have with gas molecules, and so the resulting image. This paper describes experimental measurements made using sensors under laboratory conditions in a specially created experimental chamber. Then, validation using mathematical–physical analysis in the Ansys Fluent system is described.
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(This article belongs to the Section Physical Sensors)
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