Sensors

24 pages, 5776 KB

Open AccessArticle

RISE-VIO: Robust Initialization and Targeted Pose Robustification for INS-Centric Visual–Inertial Odometry Under Degraded Visual Conditions

by Xiaowei Xu, Ran Ju, Wenhua Jiao and Lijuan Li

Sensors 2026, 26(8), 2305; https://doi.org/10.3390/s26082305 (registering DOI) - 8 Apr 2026

Abstract

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system [...] Read more.

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system that improves robustness by introducing GNC-style robustification into two failure-critical stages: initialization and per-frame pose estimation. For robust initialization, we develop a GNC-based decoupled rotation–translation initialization module with a two-stage observability gate, consisting of (i) rotation-compensated parallax-rate screening and (ii) a spectral-stability test on the linear global translation (LiGT) system. For online robustness, we design an IMU-prior-guided GNC-EPnP module to selectively downweight or reject outlier correspondences during pose estimation. Experiments on public benchmark datasets show that RISE-VIO achieves more reliable initialization and more stable trajectory estimation in challenging visual conditions while maintaining real-time performance. Additional Monte Carlo perspective-n-point (PnP) evaluations further support the robustness of the proposed pose estimation module under severe outlier contamination. Full article

(This article belongs to the Special Issue Multi-Sensor Resilient Fusion Perception, Positioning and Navigation Systems)

19 pages, 3130 KB

Open AccessArticle

SGMLN: Sentiment-Guided Mutual Learning Network for Multimodal Sarcasm Detection

by Yiran Wang, Xin Zhao and Yongtang Bao

Sensors 2026, 26(8), 2304; https://doi.org/10.3390/s26082304 - 8 Apr 2026

Abstract

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of [...] Read more.

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

(This article belongs to the Section Sensing and Imaging)

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22 pages, 5849 KB

Open AccessArticle

Multi-Scale Fourier Temporal Network for Multi-Source Precipitation Nowcasting

by Jing Huang, Shanmin Yang, Xiaojie Li and Xi Wu

Sensors 2026, 26(8), 2303; https://doi.org/10.3390/s26082303 - 8 Apr 2026

Abstract

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively [...] Read more.

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively exploit multi-source observations or learn physically meaningful representations. To address these limitations, this study proposes a Multi-Scale Frequency–Temporal Network (MS-FTNet) for precipitation nowcasting. The framework leverages Fourier transform-based frequency-domain modeling to achieve an interpretable multi-scale decomposition of precipitation dynamics. Specifically, low-frequency components capture large-scale stratiform patterns and their temporal evolution, while high-frequency components represent localized convective structures and abrupt variations. Building on this, a Global Feature Collaboration (GFC) module integrates global frequency-domain representations with multi-scale convolutional features, and an Adaptive Temporal Fusion (ATF) module enhances temporal dependency modeling. Experiments on the SEVIR dataset demonstrate that MS-FTNet consistently outperforms representative baseline models in terms of MSE, CSI, and LPIPS, particularly for heavy precipitation events and longer forecast lead times. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

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24 pages, 628 KB

Open AccessArticle

Vehicle-Conditional Split-Conformal Calibration for Risk-Budgeted Sub-Second Proxy-Triggered Vehicle Instability Warnings from Past-Only Sensor Slices

by Jinzhe Yang, Jianzheng Liu, Kai Tian, Yier Lin and Junxia Zhang

Sensors 2026, 26(8), 2302; https://doi.org/10.3390/s26082302 - 8 Apr 2026

Abstract

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy [...] Read more.

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next

τ = 0.2

s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor (

w = d = 0.1

s,

k = 2

) is warnable on only

18.3 %

of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget

α

with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at

τ = 0.2

s, the scorer achieves AUPRC

\approx 0.251

against a base rate of

0.638 %

, AUROC

\approx 0.986

, and ECE

\approx 0.034

. After calibration at

α = 5 %

, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR

_{run}

, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported

μ

-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

(This article belongs to the Section Vehicular Sensing)

37 pages, 1059 KB

Open AccessReview

Non-Invasive Electrochemical Biosensors for Fibromyalgia: A Path Toward Objective Physiological Monitoring and Personalized Management

by María Moreno-Guzmán, Juan Pablo Hervás-Pérez, Edurne Úbeda-DÒcasar and Marta Sánchez-Paniagua

Sensors 2026, 26(8), 2301; https://doi.org/10.3390/s26082301 - 8 Apr 2026

Abstract

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, [...] Read more.

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, neuroendocrine imbalance, and systemic immune-inflammatory alterations. A wide array of candidate biomarkers has been reported in FM, encompassing neurotransmitters (serotonin, norepinephrine), excitatory and inhibitory amino acids, metabolic and glycolytic enzymes, stress-related proteins, autoantibodies, oxidative stress markers and pro-inflammatory cytokines. This molecular heterogeneity reflects the systemic and multidimensional nature of FM. However, most of these biomarkers have been primarily investigated in serum or plasma, where analytical validation and reference ranges are more established. In contrast, the exploration of salivary biomarkers—although highly attractive due to its non-invasive, stress-free, and repeatable collection—remains comparatively limited. Saliva contains a reduced concentration range of many systemic markers and is strongly influenced by circadian rhythms, stress, flow rate, and oral health conditions. While promising candidates such as α-amylase, cortisol, calgranulins, and selected metabolic enzymes have shown potential in saliva, many proposed FM-related biomarkers lack full analytical validation, standardized protocols, and clinically defined reference intervals in this matrix. In this context, non-invasive electrochemical biosensors represent a transformative technological approach. Advanced electrode architectures incorporating nucleic acid probes, redox reporters, and nanostructured materials offer high sensitivity in low-volume and low-concentration biofluids such as saliva. The integration of multiplexed biomarker panels into portable platforms could enable real-time, longitudinal monitoring of FM pathophysiology, supporting phenotype stratification, personalized therapeutic adjustment, and objective disease activity tracking. Full article

27 pages, 4791 KB

Open AccessArticle

Combining Fast Orthogonal Search with Deep Learning to Improve Low-Cost IMU Signal Accuracy

by Jialin Guan, Eslam Mounier, Umar Iqbal and Michael J. Korenberg

Sensors 2026, 26(8), 2300; https://doi.org/10.3390/s26082300 - 8 Apr 2026

Abstract

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system [...] Read more.

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

(This article belongs to the Special Issue Innovative Sensor Systems and Applications: Enabling Navigation, Perception, and Decision-Making Across Domains)

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11 pages, 481 KB

Open AccessProtocol

AI-Guided Remission: Protocol for a Conversational Agent (Chatbot) for Dosing Activity and Footwear Progression After Diabetic Limb Reconstruction

by Lucian M. Feraru, David C. Klonoff, Bijan Najafi, Magdalena Antoszewska and David G. Armstrong

Sensors 2026, 26(8), 2299; https://doi.org/10.3390/s26082299 - 8 Apr 2026

Abstract

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home [...] Read more.

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies. Full article

(This article belongs to the Special Issue AI-Enabled Sensing Technology for Smart Healthcare and Precision Diagnosis)

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Graphical abstract

14 pages, 1436 KB

Open AccessArticle

Non-Linear Center-of-Pressure Features Associated with Fall History in Older Adults: An Exploratory Analysis

by Dai Wakabayashi and Yohei Okada

Sensors 2026, 26(8), 2298; https://doi.org/10.3390/s26082298 - 8 Apr 2026

Abstract

Postural sway derived from center-of-pressure (CoP) trajectories is widely used to assess balance and fall risk in older adults, but conventional linear metrics mainly quantify sway magnitude and may overlook temporal organization. Guided by the loss-of-complexity hypothesis, we re-examined associations between fall history [...] Read more.

Postural sway derived from center-of-pressure (CoP) trajectories is widely used to assess balance and fall risk in older adults, but conventional linear metrics mainly quantify sway magnitude and may overlook temporal organization. Guided by the loss-of-complexity hypothesis, we re-examined associations between fall history and linear and non-linear CoP metrics in an open-access dataset. Quiet-standing trials under eyes-open and eyes-closed conditions were analyzed in adults ≥60 years (fallers n = 19; non-fallers n = 57). To reduce confounding, propensity score matching was performed using age, sex, body mass index, activities of daily living level, illness status, number of medications, disability status, and orthosis/prosthesis use. Linear and non-linear indices, including recurrence quantification analysis, detrended fluctuation analysis, fractal dimension, multiscale entropy, stabilogram diffusion analysis, and sway density measures, were examined. After matching, no CoP metric differed significantly between groups. However, SHAP-based exploratory analysis suggested that non-linear features related to temporal structure and multiscale organization contributed more prominently to model output than conventional magnitude-based metrics. Given the limited sample size, these findings should be interpreted as exploratory and hypothesis-generating. Full article

(This article belongs to the Special Issue Sensor-Based Methods for Kinematics, Kinetics, and Physiology in Motion: Applications and Metrological Characterization)

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41 pages, 16325 KB

Open AccessReview

Three-Dimensional Surveying with Optical Sensors in Heritage Science: A Review

by Emma Vannini, Alice Dal Fovo and Raffaella Fontana

Sensors 2026, 26(8), 2297; https://doi.org/10.3390/s26082297 - 8 Apr 2026

Abstract

This review provides a comprehensive overview of the most adopted 3D surveying techniques in Cultural Heritage, offering practical guidance for the selection of appropriate methods when three-dimensional documentation of artworks is required. The analysis focuses on the most effective technologies for the 3D [...] Read more.

This review provides a comprehensive overview of the most adopted 3D surveying techniques in Cultural Heritage, offering practical guidance for the selection of appropriate methods when three-dimensional documentation of artworks is required. The analysis focuses on the most effective technologies for the 3D documentation of sites and objects of artistic value, with selection criteria primarily centred on non-invasiveness, given the uniqueness and cultural significance of the case studies, and the instrument flexibility, a crucial requirement for non-transportable items. A broad spectrum of 3D techniques is currently available for the multiscale diagnostic investigation of artworks, providing information at both macroscopic and microscopic levels. This review reports on the state of the art of such systems and evaluates the main characteristics of each technology in relation to its applicability in the heritage field. Particular attention is given to highlighting advantages and limitations, and to assessing performance in terms of resolution, gauge volume/area, acquisition time, and cost. In addition, the review discusses exemplary cases in which 3D methods are integrated with other analytical techniques to enable a more comprehensive understanding of the object under investigation. Finally, recent studies are examined to identify the most suitable approaches and the specific requirements for the digitization of real-world heritage assets. Full article

(This article belongs to the Special Issue Feature Review Papers in Optical Sensors 2026)

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30 pages, 4987 KB

Open AccessArticle

AT-BSS: A Broker Selection Strategy for Efficient Cross-Shard Processing in Sharded IoT–Blockchain Systems

by Yue Su, Yang Xiang, Kien Nguyen and Hiroo Sekiya

Sensors 2026, 26(8), 2296; https://doi.org/10.3390/s26082296 - 8 Apr 2026

Abstract

The deep integration of the Internet of Things (IoT) and blockchain technology enables emerging applications in multi-party collaboration and trusted data sharing. However, the scalability constraints of blockchain networks remain a major bottleneck when handling high-frequency interactions in IoT–blockchain systems. Sharding addresses this [...] Read more.

The deep integration of the Internet of Things (IoT) and blockchain technology enables emerging applications in multi-party collaboration and trusted data sharing. However, the scalability constraints of blockchain networks remain a major bottleneck when handling high-frequency interactions in IoT–blockchain systems. Sharding addresses this challenge by partitioning the blockchain network into parallel sub-networks. Nevertheless, it introduces significant coordination overhead for cross-shard transactions. Among mitigation strategies, Broker-based mechanisms (e.g., BrokerChain) have attracted increasing attention for their efficiency in handling cross-shard communication by reducing verification overhead and communication latency. Despite these advantages, existing research typically treats the Broker group as a fixed configuration, neglecting the impact of Broker selection on system performance. To bridge this gap, this paper proposes the Accumulative Activity–Temporal Liveness Broker Selection Strategy (AT-BSS) to optimize cross-shard transaction processing in sharded IoT–blockchains. Specifically, we formally characterize the Accumulative Activity and Temporal Liveness of accounts in the account–transaction network and use these two metrics to identify accounts that maximize transaction-aggregation efficiency. We implement AT-BSS on the BlockEmulator platform and evaluate it against two baselines, namely, ABChain and BrokerChain. Under different settings of the number of Brokers (BrokerNum), number of shards (ShardNum), transaction arrival rate (InjectSpeed), and maximum block size (MaxBlockSize), AT-BSS consistently outperforms both baselines in terms of Transactions Per Second (TPS), Transaction Confirmation Latency (TCL), and Cross-shard Transaction Ratio (CTX). Compared with ABChain, AT-BSS achieves up to 15.5% higher TPS and reduces TCL and CTX by up to 80.2% and 28.7%, respectively. AT-BSS yields more pronounced results over BrokerChain, with TPS improvements of up to 229% and reductions of up to 97.7% in TCL and 80.5% in CTX. Full article

(This article belongs to the Special Issue Blockchain-Based Security System Applied to IoT Used in Intelligent Systems)

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25 pages, 2368 KB

Open AccessArticle

Multi-Probing Opportunistic Routing in Buffer-Constrained Wireless Sensor Networks

by Nannan Sun, Shouxin Cao, Xiaoyuan Liu, Yue Gao, Yang Xu and Jia Liu

Sensors 2026, 26(8), 2295; https://doi.org/10.3390/s26082295 - 8 Apr 2026

Abstract

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data [...] Read more.

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data delivery across WSNs. In this paper, we propose a general multi-probing opportunistic routing strategy tailored for buffer-constrained WSNs, aiming to enhance transmission opportunity utilization under realistic sensing device limitations. With the help of Queueing Theory and Markov Chain Theory, we capture the sophisticated queueing processes for the buffer space of sensors, which enables the limiting distribution of the buffer occupation state to be determined. On this basis, we develop a theoretical performance modeling framework to evaluate the fundamental performance metrics of the WSN with the multi-probing opportunistic routing, including the per-flow throughput and the expected end-to-end delay. The validity of the performance modeling framework is verified by network simulations. Moreover, extensive numerical results demonstrate the network performance behaviors comprehensively and reveal some insightful findings that can serve as important guidelines for the configuration and operation of WSNs. Full article

(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)

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49 pages, 675 KB

Open AccessReview

Automated Assembly of Large-Scale Aerospace Components: A Structured Narrative Survey of Emerging Technologies

by Kuai Zhou, Wenmin Chu, Peng Zhao, Xiaoxu Ji and Lulu Huang

Sensors 2026, 26(8), 2294; https://doi.org/10.3390/s26082294 - 8 Apr 2026

Abstract

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace [...] Read more.

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace manufacturing. This paper presents a structured literature review on the automated assembly of large-scale aerospace components, summarizing advances in three core domains: pose adjustment and positioning mechanisms, digital measurement technologies, and trajectory planning and control. Particular emphasis is placed on two cross-cutting themes: measurement uncertainty analysis and flexible assembly, which are critical for high-quality docking. The review classifies pose adjustment mechanisms into four categories (NC positioners, parallel kinematic machines, industrial robots, and novel mechanisms) and digital measurement into five branches (vision metrology, large-scale metrology, measurement field construction, uncertainty analysis, and auxiliary techniques). It also outlines five trajectory planning and control routes, covering traditional methods, multi-sensor fusion, digital twins, flexible assembly, and emerging intelligent approaches. The analysis reveals that current research suffers from fragmentation among mechanism design, metrology, and control, with insufficient integration of uncertainty propagation and flexible deformation modeling. Future systems will rely on heterogeneous equipment collaboration, uncertainty-aware closed-loop control, high-fidelity flexible modeling, and digital twin-driven decision-making. This review provides a unified framework and a technical reference for developing reliable, flexible, and scalable automated assembly systems for next-generation aerospace structures. Full article

(This article belongs to the Section Sensors and Robotics)

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30 pages, 489 KB

Open AccessArticle

Performance Optimization of Nonorthogonal MFSK for Symbol-by-Symbol Coherent Detection

by Luca Rugini

Sensors 2026, 26(8), 2293; https://doi.org/10.3390/s26082293 - 8 Apr 2026

Abstract

M-ary frequency-shift keying (MFSK) is employed for several applications, including Internet-of-Things (IoT) and sensor-based communications. Previous studies have demonstrated that coherent detection of well-designed nonorthogonal MFSK signals outperforms orthogonal MFSK. This paper optimizes the error performance of nonorthogonal MFSK signals when the receiver [...] Read more.

M-ary frequency-shift keying (MFSK) is employed for several applications, including Internet-of-Things (IoT) and sensor-based communications. Previous studies have demonstrated that coherent detection of well-designed nonorthogonal MFSK signals outperforms orthogonal MFSK. This paper optimizes the error performance of nonorthogonal MFSK signals when the receiver uses a simple coherent detector on a symbol-by-symbol basis. First, we derive the theoretical conditions on the frequency separations to produce M symbol waveforms with negative crosscorrelation. Second, assuming equispaced frequencies, we analytically determine the optimum modulation index that maximizes the minimum distance among the symbol waveforms. Third, assuming non-equispaced frequencies, we optimize both nonorthogonal 4FSK and 8FSK signal sets. The optimized signal waveforms reduce the symbol error probability with respect to the current-best MFSK schemes existing in the literature, at the price of a bandwidth increase. For additive white Gaussian noise (AWGN) channels, an accurate expression for the symbol error probability of nonorthogonal 4FSK is also proposed. Full article

(This article belongs to the Section Communications)

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33 pages, 736 KB

Open AccessArticle

Analysis of Chip Electronic Components’ Typical Yield in Taping Process Based on Virtual Metrology

by Shiqi Zhang, Lizhen Chen, Jiangcheng Fu, Chenghu Yang and Guangli Chen

Sensors 2026, 26(8), 2292; https://doi.org/10.3390/s26082292 - 8 Apr 2026

Abstract

This study addresses virtual metrology for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework [...] Read more.

This study addresses virtual metrology for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework that reformulates the task as the prediction of operating-condition-level typical yield. First, physically relevant features are retained based on process knowledge and analyzed using Pearson correlation, Spearman correlation, and mutual information. We then perform multidimensional equal-frequency binning to partition the observable feature space into locally homogeneous operating condition groups, and define the within-bin median yield as the typical yield, thereby constructing an operating condition dictionary. Based on this dictionary-based representation, low-yield-oriented sample weighting is combined with nested cross-validation and Bayesian optimization for model comparison and hyperparameter tuning. Using desensitized production data from an electronic component taping process, the results under this representation show more stable prediction than direct modeling on unbinned batch samples while also improving tail-oriented fitting relative to unweighted baselines. These findings suggest that, for partially observable manufacturing data, operating condition stratification provides a practical basis for stabilizing VM prediction, while low-yield-oriented sample weighting further improves sensitivity to the low-yield tail, supporting picture yield early warning and process-level decision making. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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18 pages, 35497 KB

Open AccessArticle

Hierarchical YOLO-SAM: A Scalable Pipeline for Automated Segmentation and Morphometric Tracking of Coral Recruits in Time-Series Microscopy

by Richard S. Zhao, Cuixian Chen, Meg Van Horn and Nicole D. Fogarty

Sensors 2026, 26(8), 2291; https://doi.org/10.3390/s26082291 - 8 Apr 2026

Abstract

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which [...] Read more.

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)

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15 pages, 3365 KB

Open AccessArticle

Interface Quality Control of Self-Assembled Monolayer for Highly Sensitive Protein Detection Based on EGOFETs

by Xinyu Dong, Xingyu Jiang, Jiaqi Su, Zhongyou Lu, Cheng Shi, Dianjue Liu, Lizhen Huang and Lifeng Chi

Sensors 2026, 26(8), 2290; https://doi.org/10.3390/s26082290 - 8 Apr 2026

Abstract

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various [...] Read more.

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various strategies, the covalent immobilization of biorecognition elements onto gold surfaces via thiol chemistry is one of the most widely used approaches. In this study, we report the optimization of a mixed self-assembled monolayer (SAM) composed of 11-mercaptoundecanoic acid (11-MUA) and 3-mercaptopropionic acid (3-MPA) for label-free detection of human IgG using EGOFETs. The quality of the SAM was systematically modulated by varying the total concentration from 10 to 400 mM and characterized using X-ray Photoelectron Spectroscopy (XPS), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Atomic Force Microscopy (AFM). The results revealed that a concentration of 50 mM yielded a densely packed and well-ordered monolayer. After covalent immobilization of anti-IgG antibodies via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry and subsequent blocking with ethanolamine and bovine serum albumin (BSA), the functionalized gate electrodes were integrated into poly(3-hexylthiophene) (P3HT)-based EGOFETs. Electrical measurements demonstrated that EGOFET biosensors functionalized with the 50 mM SAM achieved optimal sensing performance. The devices exhibited a highly linear response (R² = 0.998) over a wide concentration range from 1 fM to 10 nM, with a LOD of 2.82 fM, and showed excellent selectivity against non-target immunoglobulins A and M (IgA and IgM). This SAM concentration optimization strategy provides a versatile approach for engineering high-performance EGOFET biosensors, with potential applicability to a broad range of disease biomarkers. Full article

(This article belongs to the Section Biosensors)

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22 pages, 2845 KB

Open AccessReview

Development of Pulsed Eddy Current Nondestructive Testing: A Review

by Qian Huang, Ruilin Wang, Jingxi Hu, Hao Jiao, Chi Zhang, Zhitao Hou, Chenxi Duan, Xueyuan Long and Liangchen Lv

Sensors 2026, 26(8), 2289; https://doi.org/10.3390/s26082289 - 8 Apr 2026

Abstract

As a branch of nondestructive testing (NDT), Pulsed Eddy Current Testing (PECT) is characterized by its wide frequency spectrum and high penetration depth. After years of development, it has been widely applied to defect detection and material characterization of key components in industries [...] Read more.

As a branch of nondestructive testing (NDT), Pulsed Eddy Current Testing (PECT) is characterized by its wide frequency spectrum and high penetration depth. After years of development, it has been widely applied to defect detection and material characterization of key components in industries such as petrochemicals, new energy, and aerospace. With the large-scale application of new energy sources like liquefied natural gas (LNG), methanol, and liquid hydrogen, the demand for NDT of non-ferromagnetic materials (e.g., austenitic stainless steel) has surged. However, challenges such as electromagnetic leakage caused by low magnetic permeability and the lift-off effect induced by protective layers impose stricter requirements on inspection technologies, driving the evolution of PECT towards adaptability in complex scenarios. This paper systematically reviews the latest advances in PECT technology, covering detection sensors, modeling methods, detection signal processing, and engineering applications. With a particular emphasis on research outcomes from the past decade, this paper also proposes potential directions for future development, aiming to provide a reference for innovative research and the industrial promotion of PECT technology. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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Graphical abstract

16 pages, 2428 KB

Open AccessArticle

A Device for Characterizing Skin Physiological Response to Mechanical Loading in Transtibial Prosthesis Users

by Molly E. Baumann, Mathew J. Weissinger, Joseph L. Garbini, Conor L. Lanahan, Joseph C. Mertens, Bailey Ramesh, W. Lee Childers and Joan E. Sanders

Sensors 2026, 26(8), 2288; https://doi.org/10.3390/s26082288 - 8 Apr 2026

Abstract

The skin’s physiological response to repetitive stress is not well understood in prosthesis users. Improving this understanding could facilitate the design of a diagnostic tool to determine if the skin is adapting to tolerate stress from a prosthetic socket. The objective of this [...] Read more.

The skin’s physiological response to repetitive stress is not well understood in prosthesis users. Improving this understanding could facilitate the design of a diagnostic tool to determine if the skin is adapting to tolerate stress from a prosthetic socket. The objective of this research was to develop a physical system that mechanically stresses the skin in a controlled manner and then implements the imaging modalities of infrared (IR) imaging and optical coherence tomography (OCT). IR imaging characterizes the skin’s temperature response, while OCT characterizes vessel diameter changes over time in the skin. The system was implemented in a single individual with a transtibial amputation. The system reliably maintained the force profile throughout testing. IR and OCT imaging were initiated after load application, and all curves demonstrated an initial rise in temperature immediately after load removal followed by a decrease towards baseline. The system was able to effectively detect a peak outcome (temperature and vessel area) with both imaging modalities. The system’s ability to maintain the loading throughout and begin imaging to capture the peak provides promise for expanded use to better understand the skin’s physiological response to loading in prosthesis users. This improved understanding may better inform treatment strategies to optimize patient outcomes. Full article

(This article belongs to the Section Biomedical Sensors)

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19 pages, 7072 KB

Open AccessArticle

Research on Tail Rotor Load Test Flight Technology for Helicopters Based on Strain Sensor Measurement

by Shuaike Jiao, Jiahong Zheng, Kang Li and Xiaoqing Hu

Sensors 2026, 26(8), 2287; https://doi.org/10.3390/s26082287 - 8 Apr 2026

Abstract

The load characteristics of the helicopter tail rotor system are critical to flight safety and handling performance, and flight testing remains the most direct and reliable means to obtain authentic load data. In this paper, the well-established Wheatstone bridge strain measurement method is [...] Read more.

The load characteristics of the helicopter tail rotor system are critical to flight safety and handling performance, and flight testing remains the most direct and reliable means to obtain authentic load data. In this paper, the well-established Wheatstone bridge strain measurement method is adopted to carry out accurate load testing on the helicopter tail rotor system. The tail rotor assembly mainly consists of the tail rotor shaft, pitch link, and tail rotor blades, which undertake different load transfer tasks during flight. Under actual operating conditions, the tail rotor shaft bears significant axial tension as well as combined lateral and vertical bending moments; the pitch link is primarily subjected to alternating axial tension and compression; and the tail rotor blades withstand complex loads including flapping bending, lagwise bending, and torsional moments. According to the distinct stress characteristics and force transmission paths of each component, targeted flight test maneuvers are reasonably designed. These maneuvers include steady-level flight at low, medium, and high speeds, zigzag climbing flight, near-ground side-rear flight, as well as deceleration-to-sprint and obstacle slope maneuvers specified in ADS-33E. Key flight parameters are selected for in-depth analysis to reveal the load distribution and dynamic variation patterns of the tail rotor under typical operating conditions. On this basis, a helicopter load risk test point matrix is established to identify high-risk working conditions and key monitoring positions. This study provides a solid theoretical and data foundation for subsequent flight test monitoring and structural strength verification. It effectively reduces flight test risks, improves monitoring efficiency and accuracy, and helps cut down the human, material, and financial costs associated with flight test monitoring. The research results can also provide important references for the design optimization and safety evaluation of helicopter tail rotor systems. Full article

(This article belongs to the Collection Sensors and Sensing Technology for Industry 4.0)

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