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), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) and more 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 (Chemistry, Analytical) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
- 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, JCP and Targets.
Impact Factor:
3.4 (2023);
5-Year Impact Factor:
3.7 (2023)
Latest Articles
Enhancing Adversarial Defense via Brain Activity Integration Without Adversarial Examples
Sensors 2025, 25(9), 2736; https://doi.org/10.3390/s25092736 - 25 Apr 2025
Abstract
Adversarial attacks on large-scale vision–language foundation models, such as the contrastive language–image pretraining (CLIP) model, can significantly degrade performance across various tasks by generating adversarial examples that are indistinguishable from the original images to human perception. Although adversarial training methods, which train models
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Adversarial attacks on large-scale vision–language foundation models, such as the contrastive language–image pretraining (CLIP) model, can significantly degrade performance across various tasks by generating adversarial examples that are indistinguishable from the original images to human perception. Although adversarial training methods, which train models with adversarial examples, have been proposed to defend against such attacks, they typically require prior knowledge of the attack. These methods also lead to a trade-off between robustness to adversarial examples and accuracy for clean images. To address these challenges, we propose an adversarial defense method based on human brain activity data by hypothesizing that such adversarial examples are not misrecognized by humans. The proposed method employs an encoder that integrates the features of brain activity and augmented images from the original images. Then, by maximizing the similarity between features predicted by the encoder and the original visual features, we obtain features with the visual invariance of the human brain and the diversity of data augmentation. Consequently, we construct a model that is robust against adversarial attacks and maintains accuracy for clean images. Unlike existing methods, the proposed method is not trained on any specific adversarial attack information; thus, it is robust against unknown attacks. Extensive experiments demonstrate that the proposed method significantly enhances robustness to adversarial attacks on the CLIP model without degrading accuracy for clean images. The primary contribution of this study is that the performance trade-off can be overcome using brain activity data.
Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessArticle
Research on the Improvement of the Signal Time Delay Estimation Method of Acoustic Positioning for Anti-Low Altitude UAVs
by
Miao Liu, Jiyan Yu and Zhengpeng Yang
Sensors 2025, 25(9), 2735; https://doi.org/10.3390/s25092735 - 25 Apr 2025
Abstract
With the popularity of low-altitude small unmanned aerial vehicles (UAVs), UAVs are often used to take candid photos or even carry out malicious attacks. Acoustic detection can be used to locate UAVs in order to prevent malicious attacks by UAVs. Aiming at the
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With the popularity of low-altitude small unmanned aerial vehicles (UAVs), UAVs are often used to take candid photos or even carry out malicious attacks. Acoustic detection can be used to locate UAVs in order to prevent malicious attacks by UAVs. Aiming at the problem of a large error in the time delay estimation algorithm under a low SNR, a time delay estimation algorithm based on an improved weighted function combined with a generalized cubic cross-correlation is introduced. By analyzing and comparing the performance of generalized cross-correlation time delay estimation of different traditional weighting functions, an improved weighting function that combines improved smooth coherent transform (SCOT) and phase transform (PHAT) is proposed. Compared with the traditional generalized cross-correlation weighted function, the improved weighted function has a sharper and higher peak value, and the time delay estimation error is smaller at a low SNR. Secondly, by combining the improved weight function with the generalized cubic cross-correlation, the main peak value is further increased and sharpened, and the time delay estimation performance is better than that when combined with the generalized cubic cross-correlation and the generalized quadratic correlation. Experimental results show that in complex outdoor scenes, the positioning error of the unimproved GCC PHAT method is 45.22 cm, and the positioning error of the improved weighted function generalized cubic cross-correlation algorithm is no more than 22.1 cm. Compared with the unimproved GCC PHAT method, the performance is improved by 35.55%. It is proven that this method is helpful for improving the positioning ability of low-flying UAVs and can provide help for anti-terrorism security against malicious attacks by UAVs.
Full article
(This article belongs to the Section Remote Sensors)
Open AccessArticle
Identification of Sarin Simulant DMMP Based on a Laminated MOS Sensor Using Article Swarm Optimization-Backpropagation Neural Network
by
Ting Liang, Yelin Qi, Shuya Cao, Rui Yan, Jin Gu and Yadong Liu
Sensors 2025, 25(9), 2734; https://doi.org/10.3390/s25092734 - 25 Apr 2025
Abstract
A Pt@CeLaCoNiOx/Co@SnO2 laminated MOS sensor was prepared using Co@SnO2 as the gas-sensitive film material and Pt@CeLaCoNiOx as the catalytic film material. The sensor was verified to exhibit good sensing performances for dimethyl methylphosphonate, a simulant of Sarin, under a temperature modulation,
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A Pt@CeLaCoNiOx/Co@SnO2 laminated MOS sensor was prepared using Co@SnO2 as the gas-sensitive film material and Pt@CeLaCoNiOx as the catalytic film material. The sensor was verified to exhibit good sensing performances for dimethyl methylphosphonate, a simulant of Sarin, under a temperature modulation, and characteristic peaks appeared in the resistance response curves only for dimethyl methylphosphonate. The Article Swarm Optimization-Backpropagation Neural Network had a good ability to identify the resistance response data of dimethyl methylphosphonate. The identification accuracy increased as the concentration of dimethyl methylphosphonate increased. This scheme can effectively identify whether the test gas contained dimethyl methylphosphonate or not, which provided a reference for achieving the high selectivity of the MOS sensor for Sarin.
Full article
(This article belongs to the Special Issue Advanced Sensors in Atomic Level)
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Open AccessArticle
Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning
by
Ming Sun, Yihe Zhong, Xiaoou He and Jie Zhang
Sensors 2025, 25(9), 2733; https://doi.org/10.3390/s25092733 - 25 Apr 2025
Abstract
Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of
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Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms.
Full article
(This article belongs to the Section Communications)
Open AccessArticle
HERMEES: A Holistic Evaluation and Ranking Model for Energy-Efficient Systems Applied to Selecting Optimal Lightweight Cryptographic and Topology Construction Protocols in Wireless Sensor Networks
by
Petar Prvulovic, Nemanja Radosavljevic, Djordje Babic and Dejan Drajic
Sensors 2025, 25(9), 2732; https://doi.org/10.3390/s25092732 - 25 Apr 2025
Abstract
This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based
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This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based on user-defined scenarios. The proposed model is evaluated using a scenario based on a medium-sized agricultural field. The Simple Additive Weighting (SAW) method is used to assign scores to the candidate algorithm pairs by weighting the scenario-specific criteria according to their significance in the decision-making process. To further refine the selection, mean shift clustering is utilized to group and identify the highest scored candidates. The resulting model is versatile and adaptable, enabling WSNs to be configured according to specific operational needs. The provided pseudocode elucidates the model workflow and aids in an effective implementation. The presented model establishes a solid foundation for the development of guided self-configuring context-aware WSNs capable of dynamically adapting to a wide range of application requirements.
Full article
(This article belongs to the Special Issue Efficient Resource Allocation in Wireless Sensor Networks)
Open AccessArticle
BaCo0.06Bi0.94O3-Doped NiZn Ferrites for High Frequency Low Loss Current Sensors: LTCC Sintering and Magnetic Properties
by
Shao-Pu Jiang, Chang-Lai Yuan, Wei Liu, Lin Li, Huan Li and Jing-Tai Zhao
Sensors 2025, 25(9), 2731; https://doi.org/10.3390/s25092731 - 25 Apr 2025
Abstract
In order to meet the demand for high-frequency current sensors in 5G communication and new energy fields, there is an urgent need to develop high-performance nickel-zinc ferrite-based co-fired ceramic magnetic cores. In this study, a nickel-zinc ferrite core based on low temperature co-fired
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In order to meet the demand for high-frequency current sensors in 5G communication and new energy fields, there is an urgent need to develop high-performance nickel-zinc ferrite-based co-fired ceramic magnetic cores. In this study, a nickel-zinc ferrite core based on low temperature co-fired ceramic (LTCC) technology was developed. The regulation mechanism of BaCo0.06Bi0.94O3 doping on the low-temperature sintering characteristics of NiZn ferrites was systematically investigated. The results show that the introduction of BaCo0.06Bi0.94O3 reduces the sintering temperature to 900 °C and significantly improves the density and grain uniformity of ceramics. When the doping amount is 0.75 wt%, the sample exhibits the lowest coercivity of 35.61 Oe and the following optimal soft magnetic properties: initial permeability of 73.74 (at a frequency of 1 MHz) and quality factor of 19.64 (at a frequency of 1 MHz). The highest saturation magnetization reaches 66.07 emu/g at 1 wt% doping. The results show that BaCo0.06Bi0.94O3 doping can regulate the grain boundary liquid phase distribution and modulate the magnetocrystalline anisotropy, which provides an experimental basis and optimization strategy for the application of LTCC technology in high-frequency current sensors.
Full article
(This article belongs to the Special Issue New Sensors Based on Inorganic Material)
Open AccessArticle
MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats
by
Zhengsheng Zhan, Dangyue Lai, Canjian Huang, Zhixiang Zhang, Yongle Deng and Jian Yang
Sensors 2025, 25(9), 2730; https://doi.org/10.3390/s25092730 - 25 Apr 2025
Abstract
To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis
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To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis hybrid exploration mechanisms, simulated annealing–particle swarm hybrid exploitation strategies, and elite mutation techniques. These strategies not only significantly enhance the convergence speed while ensuring algorithmic precision but also provide effective avenues for enhancing the performance of SCSO. We successfully apply these modifications to UAV path planning scenarios in complex environments. Experimental results on 18 benchmark functions demonstrate the enhanced convergence speed and stability of MSCSO. The proposed method has a superior performance in multimodal optimization tasks. The performance of MSCSO in eight complex scenarios that derived from real-world terrain data by comparing MSCSO with three state-of-the-art algorithms, MSCSO generates shorter average path lengths, reduces collision risks by 21–35%, and achieves higher computational efficiency. Its robustness in obstacle-dense and multi-waypoint environments confirms its practicality in engineering contexts. Overall, MSCSO demonstrates substantial potential in low-altitude resource exploration and emergency rescue operations. These innovative strategies offer theoretical and technical foundations for autonomous decision-making in intelligent unmanned systems.
Full article
(This article belongs to the Section Sensors and Robotics)
Open AccessArticle
A Tree Crown Segmentation Approach for Unmanned Aerial Vehicle Remote Sensing Images on Field Programmable Gate Array (FPGA) Neural Network Accelerator
by
Jiayi Ma, Lingxiao Yan, Baozhe Chen and Li Zhang
Sensors 2025, 25(9), 2729; https://doi.org/10.3390/s25092729 - 25 Apr 2025
Abstract
Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning
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Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture.
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(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Agility in Handball: Position- and Age-Specific Insights in Performance and Kinematics Using Proximity and Wearable Inertial Sensors
by
Pieter Heuvelmans, Alli Gokeler, Anne Benjaminse, Jochen Baumeister and Daniel Büchel
Sensors 2025, 25(9), 2728; https://doi.org/10.3390/s25092728 - 25 Apr 2025
Abstract
Handball is a dynamic team sport characterized by high agility requirements, which feature complex motor–cognitive demands. The ability to meet these demands is critical for performance in handball but remains under-represented in research. Existing studies highlight that cognitive demands can strongly interfere with
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Handball is a dynamic team sport characterized by high agility requirements, which feature complex motor–cognitive demands. The ability to meet these demands is critical for performance in handball but remains under-represented in research. Existing studies highlight that cognitive demands can strongly interfere with motor behavior, particularly in dynamic sport-specific movement tasks. Furthermore, high motor–cognitive load is associated with risk of lower limb injury. Therefore, to gain insight in the mechanisms between movement and performance dynamics in the presence of cognitive demands, this study investigated the performance of elite handball players in a novel planned and reactive agility task. Four FitLight proximity sensors (FitLight Corp, Aurora, ON, Canada) recorded execution time. Nine Noraxon Myomotion wearable inertial sensors (Noraxon U.S.A. Inc., Scottsdale, AZ, USA) tracked the motion of the players’ trunk, pelvis, and lower extremities at 200 Hz. Execution time and kinematics were compared between adult and youth players and between different playing positions. Adult players demonstrated faster performance than youth players and exhibited differences in hip and knee flexion, potentially reflecting variations in acceleration and deceleration strategies. Backcourt players and wings demonstrated faster performance compared to pivots, who showed distinct patterns of hip, knee, and ankle flexion, possibly due to differences in body composition. These findings highlight the influence of motor and cognitive demands on agility performance and offer valuable insights into age- and position-specific differences among elite handball players. Furthermore, these findings support the use of wearable inertial sensors for the purpose of athlete evaluation. Future research should explore the implementation into athlete monitoring.
Full article
(This article belongs to the Section Wearables)
Open AccessArticle
TCCDNet: A Multimodal Pedestrian Detection Network Integrating Cross-Modal Complementarity with Deep Feature Fusion
by
Shipeng Han, Chaowen Chai, Min Hu, Yanni Wang, Teng Jiao, Jianqi Wang and Hao Lv
Sensors 2025, 25(9), 2727; https://doi.org/10.3390/s25092727 - 25 Apr 2025
Abstract
Multimodal pedestrian detection has garnered significant attention due to its potential applications in complex scenarios. The complementarity characteristics between infrared and visible modalities can enhance detection performance. However, the design of cross-modal fusion mechanisms and the in-depth exploration of inter-modal complementarity still pose
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Multimodal pedestrian detection has garnered significant attention due to its potential applications in complex scenarios. The complementarity characteristics between infrared and visible modalities can enhance detection performance. However, the design of cross-modal fusion mechanisms and the in-depth exploration of inter-modal complementarity still pose challenges. To address this, we propose TCCDNet, a novel network integrating cross-modal complementarity. Specifically, the efficient multi-scale attention C2f (EMAC) is designed for the backbone, which combines the C2f structure with an efficient multi-scale attention mechanism to achieve feature weighting and fusion, thereby enhancing the model’s feature extraction capacity. Subsequently, the cross-modal complementarity (CMC) module is proposed, which enhances feature discriminability and object localization accuracy through a synergistic mechanism combining channel attention and spatial attention. Additionally, a deep semantic fusion module (DSFM) based on a cross-attention mechanism is incorporated to achieve deep semantic feature fusion. The experimental results demonstrate that TCCDNet achieves a MR−2 of 7.87% on the KAIST dataset, representing a 3.83% reduction compared to YOLOv8. For the other two multimodal pedestrian detection datasets, TCCDNet attains mAP50 scores of 83.8% for FLIR ADAS and 97.3% for LLVIP, outperforming the baseline by 3.6% and 1.9% respectively. These results fully validate the effectiveness and advancement of the proposed method.
Full article
(This article belongs to the Section Sensing and Imaging)
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Open AccessArticle
Preliminary Study on Sensor-Based Detection of an Adherent Cell’s Pre-Detachment Moment in a MPWM Microfluidic Extraction System
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Marius-Alexandru Dinca, Mihaita Nicolae Ardeleanu, Dan Constantin Puchianu and Gabriel Predusca
Sensors 2025, 25(9), 2726; https://doi.org/10.3390/s25092726 - 25 Apr 2025
Abstract
The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion
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The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation.
Full article
(This article belongs to the Special Issue AI and Neural Networks for Advanced Biomedical Sensor Applications)
Open AccessArticle
Research on Intrusion Detection Method Based on Transformer and CNN-BiLSTM in Internet of Things
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Chunhui Zhang, Jian Li, Naile Wang and Dejun Zhang
Sensors 2025, 25(9), 2725; https://doi.org/10.3390/s25092725 - 25 Apr 2025
Abstract
With the widespread deployment of Internet of Things (IoT) devices, their complex network environments and open communication modes have made them prime targets for cyberattacks. Traditional Intrusion Detection Systems (IDS) face challenges in handling complex attack types, data imbalance, and feature extraction difficulties
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With the widespread deployment of Internet of Things (IoT) devices, their complex network environments and open communication modes have made them prime targets for cyberattacks. Traditional Intrusion Detection Systems (IDS) face challenges in handling complex attack types, data imbalance, and feature extraction difficulties in IoT environments. Accurately detecting abnormal traffic in IoT has become increasingly critical. To address the limitation of single models in comprehensively capturing the diverse features of IoT traffic, this paper proposes a hybrid model based on CNN-BiLSTM-Transformer, which better handles complex features and long-sequence dependencies in intrusion detection. To address the issue of data class imbalance, the Borderline-SMOTE method is introduced to enhance the model’s ability to recognize minority class attack samples. To tackle the problem of redundant features in the original dataset, a comprehensive feature selection strategy combining XGBoost, Chi-square (Chi2), and Mutual Information is adopted to ensure the model focuses on the most discriminative features. Experimental validation demonstrates that the proposed method achieves 99.80% accuracy on the CIC-IDS 2017 dataset and 97.95% accuracy on the BoT-IoT dataset, significantly outperforming traditional intrusion detection methods, proving its efficiency and accuracy in detecting abnormal traffic in IoT environments.
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(This article belongs to the Section Internet of Things)
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Open AccessReview
Conducting Polymers-Based Gas Sensors: Principles, Materials, and Applications
by
Rongqing Dong, Mingna Yang, Yinxiu Zuo, Lishan Liang, Huakun Xing, Xuemin Duan and Shuai Chen
Sensors 2025, 25(9), 2724; https://doi.org/10.3390/s25092724 - 25 Apr 2025
Abstract
Conducting polymers (CPs) have emerged as promising materials for gas sensors due to their organic nature coupled with unique and versatile optical, electrical, chemical, and electrochemical properties. This review provides a comprehensive overview of the latest developments in conducting polymer-based gas sensors. First,
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Conducting polymers (CPs) have emerged as promising materials for gas sensors due to their organic nature coupled with unique and versatile optical, electrical, chemical, and electrochemical properties. This review provides a comprehensive overview of the latest developments in conducting polymer-based gas sensors. First, the fundamental gas sensing mechanisms in CPs-based sensors are elucidated, covering diverse transduction modes including electrochemical, chemiresistive, optical, piezoelectric, and field-effect transistor-based sensing. Next, the various types of conducting polymers employed in gas sensors, such as polypyrrole, polyaniline, polythiophene, and their composites are introduced, with emphasis on their synthesis methods, structural characteristics, and gas sensing response properties. Finally, the wide range of applications of these sensors is discussed, spanning industrial process control, environmental monitoring, food safety, biomedical diagnosis, and other fields, as well as existing issues such as long-term stability and humidity interference, and a summary of the biocompatibility and regulatory standards of these conductive polymers is provided. By integrating insights from sensing mechanisms, materials, and applications, this review offers a holistic understanding of CPs-based gas sensors. It also highlights future research directions, including device miniaturization, AI-assisted gas identification, multifunctional integrated sensing systems, wearable and flexible sensor platforms, and enhanced sensitivity, selectivity, and on-site detection capabilities.
Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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Open AccessArticle
The Fabrication and Characterization of Surface-Acoustic-Wave and Resistive Types of Ozone Sensors Based on Zinc Oxide: A Comparative Study
by
Sheng-Hua Yan and Chia-Yen Lee
Sensors 2025, 25(9), 2723; https://doi.org/10.3390/s25092723 - 25 Apr 2025
Abstract
Micro-Electro-Mechanical System (MEMS) technology is employed to fabricate surface acoustic wave (SAW)-type and resistive-type ozone sensors on quartz glass (SiO2) substrates. The fabrication process commences by using a photolithography technique to define interdigitated electrodes (IDEs) on the substrates. Electron-beam evaporation (EBE)
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Micro-Electro-Mechanical System (MEMS) technology is employed to fabricate surface acoustic wave (SAW)-type and resistive-type ozone sensors on quartz glass (SiO2) substrates. The fabrication process commences by using a photolithography technique to define interdigitated electrodes (IDEs) on the substrates. Electron-beam evaporation (EBE) followed by radio frequency (RF) magnetron sputtering is then used to deposit platinum (Pt) and chromium (Cr) electrode layers as well as a zinc oxide (ZnO) sensing layer, respectively. Finally, annealing is performed to improve the crystallinity and sensing performance of the ZnO films. The experimental results reveal that the ZnO thin films provide an excellent ozone-concentration sensing capability in both sensors. The SAW-type sensor demonstrates a peak sensitivity at a frequency of 200 kHz, with a rapid response time of just 35 s. Thus, it is suitable for applications requiring a quick response and high sensitivity, such as real-time monitoring and high-precision environmental detection. The resistive-type sensor shows optimal sensitivity at a relatively low operating temperature of 180 °C, but has a longer response time of approximately 103 s. Therefore, it is better suited for low-cost and large-scale applications such as industrial-gas-concentration monitoring.
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(This article belongs to the Special Issue Advanced Sensors for Gas Monitoring)
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Open AccessArticle
Using Wearable Devices to Examine the Associations of Sedentary Behavior with Perceived and Performance Fatigability Among Older Adults: The Study of Muscle, Mobility and Aging (SOMMA)
by
Reagan E. Garcia, Anne B. Newman, Eileen Johnson, Yujia Susanna Qiao, Peggy M. Cawthon, Barbara J. Nicklas, Bret H. Goodpaster and Nancy W. Glynn
Sensors 2025, 25(9), 2722; https://doi.org/10.3390/s25092722 - 25 Apr 2025
Abstract
Fatigability, a phenotype of poor energy regulation, is associated with lower physical activity in older adults, but independent associations with sedentary behavior are unknown. We examined whether sedentary behavior was associated with fatigability using cross-sectional data from the Study of Muscle, Mobility and
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Fatigability, a phenotype of poor energy regulation, is associated with lower physical activity in older adults, but independent associations with sedentary behavior are unknown. We examined whether sedentary behavior was associated with fatigability using cross-sectional data from the Study of Muscle, Mobility and Aging. Mean sedentary time, sedentary bout length, and sedentary breaks/day were measured using 7-day waking hour data collected from a thigh-worn accelerometer. Fatigability was assessed using the Pittsburgh Fatigability Scale Physical subscale (PFS, score 0–50, higher = greater fatigability) and the Pittsburgh Performance Fatigability Index (PPFI), a percentage decline of observed cadence to maximal cadence from a wrist-worn accelerometer captured during a usual-paced 400 m walk (range 0–100%, higher = more performance deterioration). The participants (N = 663; 76.4 ± 5.1 years, 58% women, 54% high PFS, median PPFI 1.4%) were sedentary for 614.8 ± 111.7 min/day, with a mean 15.0 ± 5.5 min/day bout length and mean 46.1 ± 13.2 sedentary breaks/day. Higher total sedentary time was associated with greater PFS Physical score (β = 0.71, p = 0.0368), but this association was not independent of step count/day. After adjusting for step count/day, higher sedentary time was associated with lower PPFI score (β = −0.44, p = 0.0039). Sedentary bout length and breaks/day were not associated with perceived or performance fatigability. Future studies should aim to better understand the inter-relatedness of these behaviors.
Full article
(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)
Open AccessArticle
Energy Distribution Optimization in Heterogeneous Networks with Min–Max and Local Constraints as Support of Ambient Intelligence
by
Alessandro Aloisio, Domenico D. Bloisi, Marco Romano and Cosimo Vinci
Sensors 2025, 25(9), 2721; https://doi.org/10.3390/s25092721 - 25 Apr 2025
Abstract
In recent years, ambient intelligence (AmI) has gained significant attention from both academia and industry. AmI seeks to create environments that automatically adapt to individuals’ needs, improving comfort and efficiency. These systems typically rely on Internet of Things (IoT) frameworks, where sensors and
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In recent years, ambient intelligence (AmI) has gained significant attention from both academia and industry. AmI seeks to create environments that automatically adapt to individuals’ needs, improving comfort and efficiency. These systems typically rely on Internet of Things (IoT) frameworks, where sensors and actuators enable seamless interaction between people and their surroundings. To ensure the effective operation of AmI systems, robust wireless networks are essential, capable of integrating a wide range of devices across different environments. However, designing such networks presents challenges due to varying communication protocols, power limitations, and the computational capacities of connected devices. This paper introduces a novel approach that leverages multi-interface networks to design a heterogeneous wireless network supporting AmI systems within the IoT ecosystem. The approach centers on selecting the most appropriate communication protocols, such as Wi-Fi, Bluetooth, or 5G, to connect devices. Since many devices are battery-powered, choosing the right communication interface is critical for optimizing energy efficiency. Our primary objective is to improve network performance while extending its operational lifespan by identifying an optimal set of interfaces that balance power consumption and efficiency. We present a new model within the well-established field of multi-interface networks, designed to reduce battery consumption while maximizing network performance. Additionally, we examine the computational complexity of this model and propose two solution algorithms grounded in fixed-parameter tractability theory for specific network classes.
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(This article belongs to the Section Internet of Things)
Open AccessArticle
Substation Inspection Safety Risk Identification Based on Synthetic Data and Spatiotemporal Action Detection
by
Chengcheng Liu, Weihua Zhang, Weijin Xu, Bo Lu, Weijie Li and Xuefeng Zhao
Sensors 2025, 25(9), 2720; https://doi.org/10.3390/s25092720 - 25 Apr 2025
Abstract
During substation inspection, operators are often exposed to hazardous working environments. It is necessary to use visual sensors to determine work status and perform action detection to distinguish between normal and dangerous actions in order to ensure the safety of operators. However, due
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During substation inspection, operators are often exposed to hazardous working environments. It is necessary to use visual sensors to determine work status and perform action detection to distinguish between normal and dangerous actions in order to ensure the safety of operators. However, due to information security, privacy protection, and the rarity of dangerous scenarios, there is a scarcity of related visual action datasets. To address this issue, this study first introduces a virtual work platform, which includes a controller for the parameterized control of scenarios and human resources. It can simulate realistic substation inspection operations and generate synthetic action datasets using domain randomization and behavior tree logic. Subsequently, a spatiotemporal action detection algorithm is utilized for action detection, employing YOLOv8 as the human detector, Vision Transformer as the backbone network, and SlowFast as the action detection architecture. Model training is conducted using three datasets: a real dataset, a synthetic dataset generated via a VWP, and a mixed dataset comprising both real and synthetic data. Finally, using the model trained on the real dataset as a baseline, the evaluation results on the test set shows that the use of synthetic datasets in training improves the model’s average precision by up to 10.7%, with a maximum average precision of 73.61%. This demonstrates the feasibility, effectiveness, and robustness of synthetic data.
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(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)
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Open AccessArticle
User Experience of Virtual Human and Immersive Virtual Reality Role-Playing in Psychological Testing and Assessment: A Case Study of ‘EmpathyVR’
by
Sunny Thapa Magar, Haejung Suk and Teemu H. Laine
Sensors 2025, 25(9), 2719; https://doi.org/10.3390/s25092719 - 25 Apr 2025
Abstract
Recent immersive virtual reality (IVR) technologies provide users with an enhanced sense of spatial and social presence by integrating various modern technologies into virtual spaces and virtual humans (VHs). Researchers and practitioners in psychology are attempting to understand the psychological processes underlying human
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Recent immersive virtual reality (IVR) technologies provide users with an enhanced sense of spatial and social presence by integrating various modern technologies into virtual spaces and virtual humans (VHs). Researchers and practitioners in psychology are attempting to understand the psychological processes underlying human behavior by allowing users to engage in realistic experiences within illusions enabled by IVR technologies. This study examined the user experience of role-playing with VHs in the context of IVR-based psychological testing and assessment (PTA) with a focus on EmpathyVR, an IVR-based empathy-type assessment tool developed in an interdisciplinary project. This study aimed to evaluate the advantages and disadvantages of integrating IVR-based role-playing with VHs into PTA by examining user immersion, embodiment, and satisfaction. A mixed-method approach was used to collect data from 99 Korean adolescents. While the participants reported high levels of immersion and satisfaction, the sense of embodiment varied across the correspondents, suggesting that users may have had disparate experiences in terms of their connection to the virtual body. This study highlights the potential of IVR-based role-playing with VHs to enhance PTA, particularly in empathy-related assessments, while underscoring areas for improvement in user adaptation and VH realism. The results suggest that IVR experiences based on role-playing with VHs may be feasible for PTA to advance user experience and engagement.
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(This article belongs to the Special Issue Virtual Reality and Sensing Techniques for Human)
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Open AccessArticle
Early Detection of Alzheimer’s Disease via Machine Learning-Based Microwave Sensing: An Experimental Validation
by
Leonardo Cardinali, Valeria Mariano, David O. Rodriguez-Duarte, Jorge A. Tobón Vasquez, Rosa Scapaticci, Lorenzo Crocco and Francesca Vipiana
Sensors 2025, 25(9), 2718; https://doi.org/10.3390/s25092718 - 25 Apr 2025
Abstract
The early diagnosis of Alzheimer’s disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and suitable treatment. We investigate microwave sensing for low-cost, non-intrusive
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The early diagnosis of Alzheimer’s disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and suitable treatment. We investigate microwave sensing for low-cost, non-intrusive early detection and assessment of Alzheimer’s disease. This study is based on the emerging evidence that the electromagnetic properties of cerebrospinal fluid are affected by abnormal concentrations of proteins recognized as early-stage biomarkers. We design a conformal six-element antenna array placed on the upper portion of the head, operating in the 500 MHz to 6.5 GHz band. It measures scattering response due to changes in the dielectric properties of intracranial cerebrospinal fluid. A multi-layer perceptron network extracts the diagnostic information. Data classification consists of two steps: binary classification to identify the disease presence and multi-class classification to evaluate its stage. The algorithm is trained and validated through controlled experiments mimicking various pathological severities with an anthropomorphic multi-tissue head phantom. Results support the feasibility of the proposed method using only amplitude data and lay the foundation for more extensive studies on microwave sensing for early Alzheimer’s detection.
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(This article belongs to the Section Biomedical Sensors)
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Validity of the Quarq Cycling Power Meter
by
Jon Oteo-Gorostidi, Jesús Camara, Diego Ojanguren-Rodríguez, Jon Iriberri, Iván Vadillo-Ventura and Almudena Montalvo-Pérez
Sensors 2025, 25(9), 2717; https://doi.org/10.3390/s25092717 - 25 Apr 2025
Abstract
Technological advancements have led to the development of various devices designed to monitor training loads and athletic performance. Power meters, particularly in cycling, allow for the precise quantification of power output, which is crucial for managing training loads and evaluating performance improvements. This
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Technological advancements have led to the development of various devices designed to monitor training loads and athletic performance. Power meters, particularly in cycling, allow for the precise quantification of power output, which is crucial for managing training loads and evaluating performance improvements. This study evaluates the validity of the Quarq D-Zero power meter for measuring cycling power output by comparing it with two previously validated devices—the Favero Assioma Duo (FAD) and the Hammer Saris H3 (H3)—noting that, although it shares the same measurement location as the SRM (the gold standard), it has not been directly validated against it. Thirty-one trained male cyclists participated in this study, undergoing tests across various power outputs (100–500 W) and three 10-s sprint efforts. The protocol incorporated different cadences (70, 85, and 100 revolutions per minute), randomized in order, and two cycling positions (seated and standing). Significant differences (p < 0.05) in power readings were observed among the three power meters, except during sprint efforts. However, pairwise comparisons revealed no significant differences (p > 0.05) between the FAD and Quarq power meters, except for the 500 W block. Strong to very strong correlations were observed between the FAD and Quarq power meters (r > 0.883, ICC > 0.879). The coefficient of variation (CV) between the FAD and Quarq devices ranged from 0.62% to 4.89%, and from 0.39% to 6.59% between the H3 and Quarq power meters. In conclusion, the Quarq power meter, integrated into the spider of the bicycle’s bottom bracket, provides valid power output measurements in cycling.
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(This article belongs to the Special Issue Sensors in 2025)
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