Sensors

19 pages, 5897 KB

Open AccessArticle

MS-YOLOv11: A Wavelet-Enhanced Multi-Scale Network for Small Object Detection in Remote Sensing Images

by Haitao Liu, Xiuqian Li, Lifen Wang, Yunxiang Zhang, Zitao Wang and Qiuyi Lu

Sensors 2025, 25(19), 6008; https://doi.org/10.3390/s25196008 (registering DOI) - 29 Sep 2025

In remote sensing imagery, objects smaller than

32 \times 32

pixels suffer from three persistent challenges that existing detectors inadequately resolve: (1) their weak signal is easily submerged in background clutter, causing high miss rates; (2) the scarcity of valid pixels yields few [...] Read more.

In remote sensing imagery, objects smaller than

32 \times 32

pixels suffer from three persistent challenges that existing detectors inadequately resolve: (1) their weak signal is easily submerged in background clutter, causing high miss rates; (2) the scarcity of valid pixels yields few geometric or textural cues, hindering discriminative feature extraction; and (3) successive down-sampling irreversibly discards high-frequency details, while multi-scale pyramids still fail to compensate. To counteract these issues, we propose MS-YOLOv11, an enhanced YOLOv11 variant that integrates “frequency-domain detail preservation, lightweight receptive-field expansion, and adaptive cross-scale fusion.” Specifically, a 2D Haar wavelet first decomposes the image into multiple frequency sub-bands to explicitly isolate and retain high-frequency edges and textures while suppressing noise. Each sub-band is then processed independently by small-kernel depthwise convolutions that enlarge the receptive field without over-smoothing. Finally, the Mix Structure Block (MSB) employs the MSPLCK module to perform densely sampled multi-scale atrous convolutions for rich context of diminutive objects, followed by the EPA module that adaptively fuses and re-weights features via residual connections to suppress background interference. Extensive experiments on DOTA and DIOR demonstrate that MS-YOLOv11 surpasses the baseline in mAP@50, mAP@95, parameter efficiency, and inference speed, validating its targeted efficacy for small-object detection. Full article

(This article belongs to the Section Remote Sensors)

26 pages, 35258 KB

Open AccessArticle

Reconstruction Error Guided Instance Segmentation for Infrared Inspection of Power Distribution Equipment

by Jinbin Luo, Yi Sun, Jian Zhang and Bin Sun

Sensors 2025, 25(19), 6007; https://doi.org/10.3390/s25196007 - 29 Sep 2025

Abstract

The instance segmentation of power distribution equipment in infrared images is a prerequisite for determining its overheating fault, which is crucial for urban power grids. Due to the specific characteristics of power distribution equipment, the objects are generally characterized by small scale and [...] Read more.

The instance segmentation of power distribution equipment in infrared images is a prerequisite for determining its overheating fault, which is crucial for urban power grids. Due to the specific characteristics of power distribution equipment, the objects are generally characterized by small scale and complex structure. Existing methods typically use a backbone network to extract features from infrared images. However, the inherent down-sampling operations lead to information loss of objects. The content regions of small-scale objects are compressed, and the edge regions of complex-structured objects are fragmented. In this paper, (1) the first unmanned aerial vehicle-based infrared dataset PDI for power distribution inspection is constructed with 16,596 images, 126,570 instances, and 7 categories of power equipment. It has the advantages of large data volume, rich geographic scenarios, and diverse object patterns, as well as challenges of distribution imbalance, category imbalance, and scale imbalance of objects. (2) A reconstruction error (RE)-guided instance segmentation framework, coupled with an object reconstruction decoder (ORD) and a difference feature enhancement (DFE) module, is proposed. The former reconstructs the objects, where the reconstruction result indicates the position and degree of information loss of the objects. Therefore, the difference map between the reconstruction result and the input image effectively replays the object features. The latter adaptively compensates for object features by global fusion between the difference features and backbone features, thereby enhancing the spatial representation of objects. Extensive experiments on the constructed and publicly available datasets demonstrate the strong generalization, superiority, and versatility of the proposed framework. Full article

(This article belongs to the Collection Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing)

23 pages, 364 KB

Open AccessArticle

Development of a Fully Autonomous Offline Assistive System for Visually Impaired Individuals: A Privacy-First Approach

by Fitsum Yebeka Mekonnen, Mohammad F. Al Bataineh, Dana Abu Abdoun, Ahmed Serag, Kena Teshale Tamiru, Winner Abula and Simon Darota

Sensors 2025, 25(19), 6006; https://doi.org/10.3390/s25196006 - 29 Sep 2025

Abstract

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing [...] Read more.

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments. Full article

(This article belongs to the Section Biomedical Sensors)

25 pages, 23310 KB

Open AccessArticle

Embedment of 3D Printed Self-Sensing Composites for Smart Cementitious Components

by Han Liu, Israel Sousa, Simon Laflamme, Shelby E. Doyle, Antonella D’Alessandro and Filippo Ubertini

Sensors 2025, 25(19), 6005; https://doi.org/10.3390/s25196005 - 29 Sep 2025

Abstract

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well [...] Read more.

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (

λ_{stat}

of 669,

λ_{dyn}

of 630), whereas the two-strip design produced the highest signal quality (SNR_stat = 9.5 dB, SNR_dyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Advanced Integrated Sensing Enabling Smart Structures)

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6 pages, 147 KB

Open AccessEditorial

Advanced Sensing Technologies in Structural Health Monitoring and Its Applications

by Ricardo Perera

Sensors 2025, 25(19), 6004; https://doi.org/10.3390/s25196004 - 29 Sep 2025

Abstract

Structural Health Monitoring (SHM) is an important area of research due to its strong connection with structural safety and the need to monitor and extend the lifespan of existing structures [...] Full article

(This article belongs to the Special Issue Advanced Sensing Technologies in Structural Health Monitoring and Its Applications)

22 pages, 5407 KB

Open AccessArticle

Optimization and Application of Electromagnetic Ultrasonic Transducer for Battery Non-Destructive Testing

by Xuhang Zhan, Zhangwan Li, Hongchao Chen, Guanlin Yu and Xiaoyu Li

Sensors 2025, 25(19), 6003; https://doi.org/10.3390/s25196003 - 29 Sep 2025

Abstract

The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer [...] Read more.

The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer (EMAT) technology for non-destructive battery testing, utilizing non-contact electromagnetic coupling to generate and receive ultrasonic waves. This method addresses the limitations associated with conventional piezoelectric ultrasonic coupling media, thereby facilitating highly reliable assessment of the internal condition of batteries. Specifically, this paper independently designs an EMAT featuring a Halbach magnet array and a butterfly coil. Based on this design, optimization is performed, and the amplitude of the received signal is increased fourfold compared to the pre-optimization configuration. The optimized transducer is employed to evaluate a set of retired batteries with a nominal capacity of 270 Ah. Experimental results demonstrate that batteries exhibiting capacities below 240 Ah produced average signal amplitudes more than 40% lower than those of batteries with higher capacities. This technology provides a non-contact, disassembly-free approach for rapid performance evaluation of batteries and demonstrates potential for effective application in sorting retired battery units. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System)

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23 pages, 35547 KB

Open AccessArticle

Distributed Prescribed Performance Formation Tracking for Unknown Euler–Lagrange Systems Under Input Saturation

by Athanasios K. Gkesoulis, Andreani Christopoulou, Charalampos P. Bechlioulis and George C. Karras

Sensors 2025, 25(19), 6002; https://doi.org/10.3390/s25196002 - 29 Sep 2025

Abstract

In this paper, we propose a distributed prescribed performance formation tracking control method for unknown Euler–Lagrange systems subject to input amplitude constraints. We address the challenge of maintaining formation tracking within predefined performance bounds when the agents’ inputs are subject to saturation. This [...] Read more.

In this paper, we propose a distributed prescribed performance formation tracking control method for unknown Euler–Lagrange systems subject to input amplitude constraints. We address the challenge of maintaining formation tracking within predefined performance bounds when the agents’ inputs are subject to saturation. This is achieved by designing a distributed virtual velocity reference modification mechanism, which modifies the desired velocity reference of each agent whenever saturation occurs. We establish sufficient feasibility conditions for the input constraints that ensure prescribed performance formation tracking of the desired trajectory and guarantee the boundedness of all closed-loop signals. Simulations on a team of underwater vehicles validate the method’s effectiveness. Full article

(This article belongs to the Special Issue Cooperative Perception and Planning for Swarm Robot Systems)

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19 pages, 3612 KB

Open AccessArticle

CA-YOLO: An Efficient YOLO-Based Algorithm with Context-Awareness and Attention Mechanism for Clue Cell Detection in Fluorescence Microscopy Images

by Can Cui, Xi Chen, Lijun He and Fan Li

Sensors 2025, 25(19), 6001; https://doi.org/10.3390/s25196001 - 29 Sep 2025

Abstract

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference [...] Read more.

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference between clue cells and normal epithelial cells lies in the surface texture and edge morphology. To address this specific problem, we propose an clue cell detection algorithm named CA-YOLO. The contributions of our approach lie in two synergistic and custom-designed feature extraction modules: the context-aware module (CAM) extracts and captures bacterial distribution patterns on the surface of clue cells; and the shuffle global attention mechanism (SGAM) enhances cell edge features and suppresses irrelevant information. In addition, we integrate focal loss into the classification loss to alleviate the severe class imbalance problem inherent in clinical samples. Experimental results show that the proposed CA-YOLO achieves a sensitivity of 0.778, which is 9.2% higher than the baseline model, making the automated BV detection more reliable and feasible. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Methods and Applications—2nd Edition)

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27 pages, 4168 KB

Open AccessArticle

Electromyographic Diaphragm and Electrocardiographic Signal Analysis for Weaning Outcome Classification in Mechanically Ventilated Patients

by Alejandro Arboleda, Manuel Franco, Francisco Naranjo and Beatriz Fabiola Giraldo

Sensors 2025, 25(19), 6000; https://doi.org/10.3390/s25196000 - 29 Sep 2025

Abstract

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals [...] Read more.

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management. Full article

(This article belongs to the Section Biomedical Sensors)

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43 pages, 2323 KB

Open AccessArticle

A New Methodological Approach Integrating Motion Capture and Pressure-Sensitive Gait Data to Assess Functional Mobility in Parkinson’s Disease: A Two-Phase Study

by Sabrina Köchli, Isabel Casso, Yvonne N. Delevoye-Turrell, Stefan Schmid, Dawn C. Rose and Caroline Whyatt

Sensors 2025, 25(19), 5999; https://doi.org/10.3390/s25195999 - 29 Sep 2025

Abstract

Existing clinical assessments of Parkinson’s disease (PD) primarily focus on stratifying symptom severity or progression rate, which limits their ability to capture changes in functional mobility—an important factor in evaluating rehabilitation outcomes. To address this gap, we developed a novel methodology, the Functional [...] Read more.

Existing clinical assessments of Parkinson’s disease (PD) primarily focus on stratifying symptom severity or progression rate, which limits their ability to capture changes in functional mobility—an important factor in evaluating rehabilitation outcomes. To address this gap, we developed a novel methodology, the Functional Mobility Assessment for Parkinson’s (FMA-P), which integrates motion capture and pressure-sensitive gait analysis to explore key aspects of functional mobility. Study 1. To develop the FMA-P, we conducted a pilot study involving 12 individuals with PD and 12 age-matched healthy controls, who each completed the FMA-P sequence three times. The sequence included the following tasks: rising from a chair, walking through a doorway, turning, bending to pick up and place an object, and returning to a seated position. Results from Study 1 demonstrated that the FMA-P is a sensitive tool for identifying functional impairments in PD. In particular, significant differences between people with Parkinson’s (PwP) and controls were observed during chair rise (higher peak trunk inclination, p = 0.006; lower mean trunk jerk, p = 0.003) and turning task (longer task duration, p = 0.026 and lower mean heel strike angle, p = 0.007), providing critical insights into postural stability. Study 2. To assess changes in functional mobility over time, we conducted a 12-week repeated-measures intervention study with 12 participants with PD. Results from Study 2 indicated notable improvements in turning stability and balance. Participants demonstrated reduced turning time (p = 0.006) and increased yaw rotation in the head (p = 0.001), trunk (p = 0.002), and pelvis (p = 0.012). In contrast, no significant changes were observed in standard clinical measures (i.e., Timed Up and Go and task duration). The FMA-P offers fine-grained insights into movement quality, making it a valuable tool for early diagnosis, monitoring intervention efficacy, and guiding rehabilitation strategies in individuals with PD. Full article

(This article belongs to the Special Issue Applications of Smart Sensing Textiles for Assessment and Assistance of Motion)

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20 pages, 3191 KB

Open AccessArticle

Visuomotor Control Accuracy of Circular Tracking Movement According to Visual Information in Virtual Space

by Jihyoung Lee, Kwangyong Han, Woong Choi and Jaehyo Kim

Sensors 2025, 25(19), 5998; https://doi.org/10.3390/s25195998 - 29 Sep 2025

Abstract

The VR-based circular tracking movement evaluation system (CES) was developed to quantitatively assess visuomotor control. The virtual stick, a component of the CES, provides visual cues in the virtual environment and haptic feedback when holding the controller. This study examined the effects of [...] Read more.

The VR-based circular tracking movement evaluation system (CES) was developed to quantitatively assess visuomotor control. The virtual stick, a component of the CES, provides visual cues in the virtual environment and haptic feedback when holding the controller. This study examined the effects of stick presence and presentation order on control accuracy for distance, angle, and angular velocity. Twenty-seven participants (12 females; mean age 23.3 ± 2.3 years) performed tasks in the frontal plane followed by the sagittal plane. In each plane, the stick was visible for the first 1–3 revolutions and invisible for the subsequent 4–6 revolutions in the invisible condition, with the reverse order in the visible condition. In the invisible condition, control accuracy with the stick was 1.10 times higher for distance only in the sagittal plane, and 1.13 and 1.09 times higher for angle and angular velocity in the frontal plane, and 1.11 and 1.08 times higher in the sagittal plane. No significant differences were observed in the visible condition. The improved control accuracy when the stick was visible is likely due to enhanced precision in constructing the reference frame, internal models, body coordinates, and effective multisensory integration of visual and haptic information. Such visual information may enable fine control in virtual environment-based applications, including games and surgical simulations. Full article

(This article belongs to the Special Issue Sensors Technologies for Measurements and Signal Processing)

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66 pages, 9599 KB

Open AccessReview

A Review: Absolute Linear Encoder Measurement Technology

by Maqiang Zhao, Yuyu Yuan, Linbin Luo and Xinghui Li

Sensors 2025, 25(19), 5997; https://doi.org/10.3390/s25195997 - 29 Sep 2025

Abstract

Absolute linear encoders have emerged as a core technical enabler in the fields of high-end manufacturing and precision displacement measurement, owing to their inherent advantages such as the elimination of the need for homing operations and the retention of position data even upon [...] Read more.

Absolute linear encoders have emerged as a core technical enabler in the fields of high-end manufacturing and precision displacement measurement, owing to their inherent advantages such as the elimination of the need for homing operations and the retention of position data even upon power failure. However, there remains a notable scarcity of comprehensive review materials that can provide systematic guidance for practitioners engaged in the field of absolute linear encoder measurement technology. The present study aims to address this gap by offering a practical reference to professionals in this domain. In this research, we first systematically delineate the three fundamental categories of measurement principles underlying absolute linear encoders. Subsequently, we analyze the evolutionary trajectory of coding technologies, encompassing the design logics and application characteristics of quasi-absolute coding (including non-embedded and embedded variants) as well as absolute coding (covering multi-track and single-track configurations). Furthermore, we summarize the primary error sources that influence measurement accuracy and explore the operational mechanisms of various types of errors. This study clarifies the key technical pathways and existing challenges associated with absolute linear encoders, thereby providing practitioners in relevant fields with a decision-making guide for technology selection and insights into future development directions. Moving forward, efforts should focus on achieving breakthroughs in critical technologies such as high fault-tolerant coding design, integrated manufacturing, and error compensation, so as to advance the development of absolute linear encoders toward higher precision, miniaturization, cost reduction, and enhanced reliability. Full article

(This article belongs to the Section Optical Sensors)

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15 pages, 3209 KB

Open AccessArticle

The Impact of Chinese Martial Arts Sanda Training on Cognitive Control and ERP: An EEG Sensors Study

by Yanan Li, Haojie Li and Haidong Jiang

Sensors 2025, 25(19), 5996; https://doi.org/10.3390/s25195996 - 29 Sep 2025

Abstract

Objective: This study aimed to explore the impact of sanda sports experience on cognitive control using electroencephalography (EEG). Methods: The study involved 38 male participants, including 19 sanda athletes with over 5 years of training and 19 ordinary college students. A 2 × [...] Read more.

Objective: This study aimed to explore the impact of sanda sports experience on cognitive control using electroencephalography (EEG). Methods: The study involved 38 male participants, including 19 sanda athletes with over 5 years of training and 19 ordinary college students. A 2 × 4 mixed experimental design was used, with group (sanda athletes vs. ordinary college students) as the between-subjects variable and four experimental conditions (consistent in the previous and current trials, consistent in the previous but inconsistent in the current trials, inconsistent in the previous but consistent in the current trials, and inconsistent in both previous and current trials) as the within-subjects variable. The classic color-word Stroop task was employed to measure cognitive control function through reaction time, accuracy, and event-related potential (ERP) amplitude. Results: Sanda athletes exhibited significantly shorter reaction times than ordinary college students across all conditions (p < 0.05). There was no significant difference in accuracy between the two groups (p > 0.05). ERP results showed that sanda athletes had significantly larger amplitudes for the N200 and P300 components in incongruent trials compared to congruent trials (p < 0.05), and significantly larger N400 amplitudes in incongruent trials than ordinary college students (p < 0.05). Conclusions: Sanda athletes demonstrated faster response speed and enhanced cognitive control abilities, as indicated by ERP components, without sacrificing task accuracy. Full article

(This article belongs to the Special Issue Advances in EEG Sensors: Research and Applications)

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26 pages, 7000 KB

Open AccessArticle

Agentic Search Engine for Real-Time Internet of Things Data

by Abdelrahman Elewah, Khalid Elgazzar and Said Elnaffar

Sensors 2025, 25(19), 5995; https://doi.org/10.3390/s25195995 - 28 Sep 2025

Abstract

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT [...] Read more.

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT data. Existing IoT search engines are considered device discovery tools, providing only metadata about devices rather than enabling access to IoT application data. While efforts such as IoTCrawler have striven to support IoT application data, they have largely failed due to the fragmentation of IoT systems and the heterogeneity of IoT data.To address this, we recently introduced SensorsConnect—a unified framework designed to facilitate interoperable content and sensor data sharing among collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled shared and accessible information spaces for humans. This paper presents the IoT Agentic Search Engine (IoT-ASE), a real-time semantic search engine tailored specifically for IoT environments. IoT-ASE leverages LLMs and Retrieval-Augmented Generation (RAG) techniques to address the challenges of navigating and searching vast, heterogeneous streams of real-time IoT data. This approach enables the system to process complex natural language queries and return accurate, contextually relevant results in real time. To evaluate its effectiveness, we implemented a hypothetical deployment in the Toronto region, simulating a realistic urban environment using a dataset composed of 500 services and over 37,000 IoT-like data entries. Our evaluation shows that IoT-ASE achieved 92% accuracy in retrieving intent-aligned services and consistently generated concise, relevant, and preference-aware responses, outperforming generalized outputs produced by systems such as Gemini. These results underscore the potential of IoT-ASE to make real-time IoT data both accessible and actionable, supporting intelligent decision-making across diverse application domains. Full article

(This article belongs to the Special Issue Recent Trends in AI-Based Intelligent Sensing Systems and IoTs)

27 pages, 8441 KB

Open AccessArticle

Radar in 7500 m Well Based on Channel Adaptive Algorithm

by Handing Liu, Huanyu Yang, Changjin Bai, Siming Li, Cheng Guo and Qing Zhao

Sensors 2025, 25(19), 5994; https://doi.org/10.3390/s25195994 - 28 Sep 2025

Abstract

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; [...] Read more.

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware. Full article

(This article belongs to the Section Radar Sensors)

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26 pages, 10666 KB

Open AccessArticle

FALS-YOLO: An Efficient and Lightweight Method for Automatic Brain Tumor Detection and Segmentation

by Liyan Sun, Linxuan Zheng and Yi Xin

Sensors 2025, 25(19), 5993; https://doi.org/10.3390/s25195993 - 28 Sep 2025

Abstract

Brain tumors are highly malignant diseases that severely threaten the nervous system and patients’ lives. MRI is a core technology for brain tumor diagnosis and treatment due to its high resolution and non-invasiveness. However, existing YOLO-based models face challenges in brain tumor MRI [...] Read more.

Brain tumors are highly malignant diseases that severely threaten the nervous system and patients’ lives. MRI is a core technology for brain tumor diagnosis and treatment due to its high resolution and non-invasiveness. However, existing YOLO-based models face challenges in brain tumor MRI image detection and segmentation, such as insufficient multi-scale feature extraction and high computational resource consumption. This paper proposes an improved lightweight brain tumor detection and instance segmentation model named FALS-YOLO, based on YOLOv8n-Seg and integrating three key modules: FLRDown, AdaSimAM, and LSCSHN. FLRDown enhances multi-scale tumor perception, AdaSimAM suppresses noise and improves feature fusion, and LSCSHN achieves high-precision segmentation with reduced parameters and computational burden. Experiments on the tumor-otak dataset show that FALS-YOLO achieves Precision (B) of 0.892, Recall (B) of 0.858, mAP@0.5 (B) of 0.912 in detection, and Precision (M) of 0.899, Recall (M) of 0.863, mAP@0.5 (M) of 0.917 in segmentation, outperforming YOLOv5n-Seg, YOLOv8n-Seg, YOLOv9s-Seg, YOLOv10n-Seg and YOLOv11n-Seg. Compared with YOLOv8n-Seg, FALS-YOLO reduces parameters by 31.95%, computational amount by 20.00%, and model size by 32.31%. It provides an efficient, accurate and practical solution for the automatic detection and instance segmentation of brain tumors in resource-limited environments. Full article

(This article belongs to the Special Issue Emerging MRI Techniques for Enhanced Disease Diagnosis and Monitoring)

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34 pages, 8775 KB

Open AccessReview

Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection

by Abdul Saboor Khan, Muhammad Jamshed Abbass and Abdul Haseeb Khan

Sensors 2025, 25(19), 5992; https://doi.org/10.3390/s25195992 - 28 Sep 2025

Abstract

The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is [...] Read more.

The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is readily available in today’s world. This is despite the fact that recent developments in computer vision, such as Vision Transformers (ViT) and language models using BERT and GPT, have opened up new possibilities for the field. The purpose of this review paper is to provide an overview of the present status of the field, with a specific emphasis on the use of facial expression recognition and object detection for the purpose of image captioning, particularly in the context of fault-aware systems and Prognostics and Health Management (PHM) applications within Industry 4.0 environments. However, to the best of our knowledge, no review study has focused on the significance of facial expressions in relation to image captioning, especially in industrial settings where operator facial expressions can provide valuable insights for fault detection and system health monitoring. This is something that has been overlooked in the existing body of research on image captioning, which is the primary reason why this study was conducted. During this paper, we will talk about the most important approaches and procedures that have been utilized for this task, including fault-aware methodologies that leverage visual data for PHM in smart manufacturing contexts, and we will highlight the advantages and disadvantages of each strategy. The purpose of this review is to present a comprehensive assessment of the current state of the field and to recommend topics for future research that will lead to machine-translated captions that are more detailed and accurate, particularly for Industry 4.0 applications where visual monitoring plays a crucial role in system diagnostics and maintenance. Full article

(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)

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12 pages, 5483 KB

Open AccessCommunication

An Antenna Array with Wide Flat-Top Beam and Low Sidelobes for Aerial Target Detection

by Liangzhou Li, Yan Dong, Xiao Cai and Jingqian Tian

Sensors 2025, 25(19), 5991; https://doi.org/10.3390/s25195991 - 28 Sep 2025

Abstract

The misuse of drone technology poses significant risks to public and personal safety, emphasizing the need for accurate and efficient aerial target detection to prevent detection failures due to randomly distributed airborne targets and mitigate interference from undesired directions. Unlike conventional beam-synthesis techniques [...] Read more.

The misuse of drone technology poses significant risks to public and personal safety, emphasizing the need for accurate and efficient aerial target detection to prevent detection failures due to randomly distributed airborne targets and mitigate interference from undesired directions. Unlike conventional beam-synthesis techniques that often require either a large number of array elements or iterative numerical optimization, the proposed method analytically derives the excitation distribution by solving a newly formulated weighted-constraint problem, thereby fully accounting for mutual coupling between elements and ensuring both computational efficiency and design accuracy. In this communication, a 10 × 4 planar microstrip antenna array with a wide flat-top beam and low sidelobe is designed based on the extended method of maximum power transmission efficiency. The optimized distribution of excitations for the antenna array, which achieves a shaped beam with uniform gain over the desired angular range while suppressing sidelobe levels (SLLs) outside the shaped region, is derived by analytically solving a newly formulated weighted constraint problem. To reduce the number of antenna elements and enhance radiation characteristics, the inter-element spacings in the E-plane and H-plane are set to 0.55 λ₀ and 0.75 λ₀, where λ₀ is the free-space wavelength at 3.5 GHz. Measurement results indicate that the flat-top beam in the E-plane has a wide half-power beamwidth (HPBW) of 51.2° and a low SLL of −30.1 dB, while the beam in the H-plane has a narrow HPBW of 20.1° and a low SLL of −30.5 dB, thereby demonstrating its capability in aerial target detection and airborne tracking applications. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Antennas: Second Edition)

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13 pages, 1644 KB

Open AccessArticle

Research on High-Precision PGC Demodulation Method for Fabry-Perot Sensors Based on Shifted Sampling Pre-Calibration

by Qun Li, Jian Shao, Peng Wu, Jiabi Liang, Yuncai Lu, Meng Zhang and Zongjia Qiu

Sensors 2025, 25(19), 5990; https://doi.org/10.3390/s25195990 - 28 Sep 2025

Abstract

To address the issues of quadrature component attenuation and signal-to-noise ratio (SNR) degradation caused by carrier phase delay in Phase-Generated Carrier (PGC) demodulation, this paper proposes a phase delay compensation method based on sampling-point shift pre-calibration. By establishing a discrete phase offset model, [...] Read more.

To address the issues of quadrature component attenuation and signal-to-noise ratio (SNR) degradation caused by carrier phase delay in Phase-Generated Carrier (PGC) demodulation, this paper proposes a phase delay compensation method based on sampling-point shift pre-calibration. By establishing a discrete phase offset model, we derive the mathematical relationship between sampling point shift and carrier cycle duration, and introduce a compensation mechanism that adjusts the starting point of the sampling sequence to achieve carrier phase pre-alignment. Theoretical analysis demonstrates that this method restricts the residual phase error to within Δθ_max = πf₀/f_s, thereby fundamentally avoiding the denominator-zero problem inherent in traditional compensation algorithms when θ approaches 45°. Experimental validation using an Extrinsic Fabry–Perot Interferometric (EFPI) ultrasonic sensor shows that, at a sampling rate of 10 MS/s, the proposed pre-alignment algorithm improves the minimum demodulation SNR by 35 dB and reduces phase fluctuation error to 2% of that of conventional methods. Notably, in 1100 consecutive measurements, the proposed method eliminates demodulation failures at critical phase points (e.g., π/4, π/2), which are commonly problematic in traditional techniques. By performing phase pre-compensation at the signal acquisition level, this method significantly enhances the long-term measurement stability of interferometric fiber-optic sensors in complex environments while maintaining the existing PGC demodulation architecture. Full article

(This article belongs to the Special Issue Recent Advances in Micro- and Nanofiber-Optic Sensors)

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