Search Results (1,035)

Electrode-level compressibility may not imply transferable hardware redundancy in biomimetic myoelectric interfaces. This study tested whether sensor-count sufficiency discovered by trial-level analysis survives participant-grouped evaluation. Dataset A comprised 398 archived Myo Armband trials from eight gestures. Dataset B contained 864 one-second trials from 36 participants and six gestures. A timestamp audit identified extensive repeated channel values; Dataset B was therefore analyzed on a conservative 100 Hz grid with 20–45 Hz filtering. Sensor subsets and RBF-SVM parameters were selected exclusively within grouped training data using repeated nested validation. Electrode-level NMF, all 28 fixed six-sensor layouts, cyclic re-indexing, channel-block ablation, participant-cluster bootstrap, PCA, and time-domain-only sensitivity analyses were evaluated. Dataset A yielded 97.74% accuracy with six sensors and 97.93% with eight. In Dataset B, accuracy was 75.96% ± 7.47% with six sensors and 77.93% ± 6.49% with eight; the paired difference was −1.97 percentage points (corrected 95% CI, −5.50 to 1.56). The participant-cluster bootstrap interval was −3.70 to −0.31 points. Active-gesture accuracy was 71.67% and 74.35%, respectively. All fixed six-sensor layouts averaged 74.59%. Three NMF components reconstructed 89.95% ± 1.78% of held-out-participant normalized RMS patterns, with no nonconverged folds. One-position cyclic re-indexing reduced accuracy to 40.28%; channel-block ablation caused losses of 0.62–6.71 points. Low-dimensional electrode-level RMS structure did not establish removable sensors across unseen users. Compact biomimetic interfaces require registration, adaptation, or equivariant processing before physical sensor reduction. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

Thailand has become a prominent destination for contemporary Chinese mobility through international education, employment, entrepreneurship, family relocation, retirement, and lifestyle-oriented movement. However, these pathways are usually studied separately, obscuring how education, work, legal status, digital infrastructures, everyday adaptation, and belonging interact over time. This article reports a structured integrative review of 53 empirical, theoretical, methodological, and policy sources identified through targeted searches of publisher platforms, scholarly discovery services, Thai journal repositories, and institutional sources, supplemented by citation chaining. The synthesis develops a framework of layered belonging under negotiated mobility. Mobility begins with aspirations and capabilities situated within a regional opportunity structure; educational and other entry pathways provide uneven access to linguistic, social, and professional resources; and the conversion of these resources is mediated by language, digital and interpersonal networks, institutional support, and everyday competence while being filtered through visa rules, work authorization, employer dependence, occupational access, and public narratives. Belonging is therefore differentiated across six interdependent dimensions: affective-place, relational-social, functional-everyday, professional-economic, legal-institutional, and future-temporal. This framework explains why migrants may feel emotionally at home and socially connected in Thailand while remaining legally temporary, professionally dependent, or uncertain about long-term residence. The review clarifies the added value of the framework relative to place-belongingness, integration domains, social anchoring, and differentiated embedding; proposes six empirically testable propositions; and identifies implications for universities, employers, public agencies, and migrant-serving organizations. Full article

Pantograph arcing is both a vehicle current-collection problem and a railway-infrastructure condition-monitoring problem because repeated loss of electrical contact can accelerate wear of the overhead contact wire and pantograph strip, degrade traction power quality, and increase maintenance demand. This study develops a leakage-safe diagnostic framework for 3-kV DC railway operation using 13 independent high-frequency recordings from the public Trenitalia E464 pantograph-arcing dataset. Because the repository does not provide synchronized optical/contact-force ground truth, the machine-learning target is consistently treated as a physics-guided candidate interval rather than an independently verified arc label. Pantograph voltage, pantograph current, filter voltage, and braking-rheostat current were sampled at 50 kSa/s and transformed into 207 event-preserving analysis windows. A total of 849 candidate features were extracted across time, frequency, time-frequency, nonlinear, and physics-informed electrical domains. The strongest leave-one-recording-out configuration was Extra Trees with frequency-domain features, with mean event-level accuracy of 0.9936, balanced accuracy of 0.9952, Macro-F1 of 0.9932, MCC of 0.9874, ROC-AUC of 0.9994, and PR-AUC of 0.9989. Ten-repeat grouped five-fold validation, with complete recordings retained as groups, produced a mean Macro-F1 of 0.9910 (SD 0.0193) across 50 grouped test folds. Five hundred recording-grouped bootstrap resamples yielded a Macro-F1 mean of 0.9893 with a 95% confidence interval of 0.9694–1.0000. A dedicated guard audit found zero candidate-interval overlap in all 138 retained normal 100 ms feature windows. Sensitivity analysis showed that 100 ms spectral features were materially more stable than 20 ms features, while 25% and 50% candidate-overlap thresholds produced nearly identical performance. The dataset does not contain long-duration independently verified arc-free operation, chainage/GPS catenary position, or synchronized contact-force measurements; consequently, the results are interpreted as proof-of-concept electrical screening of candidate current-collection disturbances rather than fleet-wide ground-truth arc detection. Full article

In this paper, a fractional-order active disturbance rejection repetitive control (FADRRC) scheme is proposed for fractional-order time-delay systems. The unified control framework integrates a bandwidth-parameterized fractional extended state observer (FESO), modified repetitive control and a filtered Smith predictor. The FESO online estimates system states and lumped disturbances, the filtered Smith predictor compensates time-delay deviation to weaken sensitivity to model mismatch, and repetitive control eliminates steady-state error for periodic reference signals. By fractional frequency-domain stability theory and the small-gain theorem, two BIBO stability criteria for nominal and mismatched cases are derived, reformulating the controller design as a pole placement problem of fractional-order transfer functions. Numerical simulations on a fractional-order time-delay PMSM servo system verify that the proposed FADRRC possesses superior periodic tracking accuracy and disturbance rejection capability compared with existing ESO-RC and DOB-RC methods, which confirms the validity of the presented control strategy. Full article

This contribution presents a methodology for optimal placement (OP) of meters in power systems, using the state-space reference frame. The goal is to minimize the state estimation error, specifically, the mean squared error (MSE), through OP of a limited number of measurement devices and keep the total observability of the system. The measurement set is applied to the time-domain state estimation based on the Kalman filter (KF) to obtain voltage and current waveforms in real time, and the harmonic content is evaluated through the application of the Fast Fourier Transform (FFT). The effectiveness of harmonic state estimation (HSE) is demonstrated in case studies considering different operating points in a test electrical network, particularly in estimating the dynamic behavior of nonlinear electrical loads. The HSE method is implemented in physical tests using Lab-Volt^® equipment to monitor the system in real time using MATLAB/Simulink^® software through the RL-LAB^® platform; the real-time experimental tests (RTE) allow validation of the real-time digital simulation (RTS). Full article

Surface defect detection across diverse surface types, such as biological shells, photovoltaic panels, and pavement infrastructure, is essential for industrial quality control and structural health monitoring. Although Deep Learning (DL) models perform well in this domain, high computational and memory requirements limit their deployment on resource-constrained edge devices. To address this, we propose a computationally efficient descriptor-level feature extraction framework combining Local Binary Pattern (LBP), Gabor filters, and Discrete Wavelet Transform (DWT) to capture complementary textural, directional, and frequency characteristics of surface defects. An AdaBoost-driven feature selection strategy reduces the high-dimensional hybrid feature pool to the 25 most discriminative attributes, and SHapley Additive exPlanations (SHAP) analysis is applied to interpret feature contributions. The proposed framework achieved 100% accuracy on the EggCrack dataset and 79.0% accuracy on the ELPV dataset, which is more challenging. On ELPV, it outperformed the MCU-optimized MCUNet-In0 (78.16%) and remained within 4.16 percentage points of EfficientNetV2-B0 (83.16%), while achieving approximately 7577× fewer floating-point operations (FLOPs) and 89× smaller model size. Deployed on a Raspberry Pi 5 CPU, the framework achieved an end-to-end inference time of 37 ms per image, enabling real-time inference. These results indicate that the proposed hybrid descriptor and selection framework offers a favorable accuracy-efficiency trade-off for real-time surface defect detection on CPU-based edge devices. Full article

Vision-based traffic accident anticipation is critical for active vehicle safety systems, yet existing architectures frequently conflate performance gains with heavy parameter scaling optimized from scratch on compact domains. Consequently, during real-time inference, these frameworks suffer from severe prediction volatility and early triggering biases that induce dangerous control instability. To address these limitations, this paper shifts the research focus away from network modifications toward a highly controlled, strategy-driven training pipeline executed under a completely invariant spatial–temporal neural backbone. Our proposed paradigm establishes a robust framework through three decoupled milestones. First, an out-of-domain initialization strategy transferred generalized driving kinetics from a large-scale sequence domain (Mapillary) to serve as a stable temporal anchor. Second, a target-domain generative enrichment step injected synthetic nighttime scenes to decouple hazard features from low-light ambient noise. Third, progressive temporal supervision paradigm scaling targeted labels monotonically to align with continuous kinetic risk accumulation. Overall evaluations on the Car Crash Dataset (CCD) benchmark demonstrate that the fully integrated configuration (C4) pipeline achieves 69.89% in frame-level Mean Average Precision (mAP), which is an improvement of +22.81 percentage points over the baseline configuration. Continuous temporal measurements prove that our framework can adapt to tracking volatility, compressing Temporal Confidence Variance to 0.00328, and dropping the Prediction Instability Count to 0.66. While hyper-sensitive baselines report early raw latency averages driven by premature trigger noise, our model purposefully filters this early-frame variability to deliver a secure warning profile, achieving an absolute zero false alarm rate (FAR = 0.00%) across evaluated non-hazardous driving sequences, establishing the sequence-level trustworthiness required for practical autonomous deployment. Full article

Reliable detection of event-related potentials (ERPs) at the single-trial level remains a challenge due to low signal-to-noise ratio and high variability in electroencephalography (EEG) recordings. This work investigates the use of the Deep-Match framework (Deep-MF) for ERP detection. We examine whether incorporating prior knowledge of an ERP template into deep learning models improves detection performance. As a proof-of-concept study, the framework was evaluated on a single dataset with multi-channel EEG recordings during laser stimulation. The model was trained in two stages. First, an encoder–decoder architecture was trained to reconstruct input EEG signals in order to learn compact signal representations. In the second stage, the decoder was replaced with a detection module and the network was fine-tuned for ERP identification. Two model variants were evaluated: a standard model with randomly initialized filters and a Deep-MF model in which input kernels were initialized using ERP templates. Models performance was assessed on a single-trial ERP detection task during leave-one-out validation, and then compared with matched filter detector. The neural network models outperformed the matched filter detector and proposed that the Deep-MF model slightly outperformed the detector with standard kernel initialization for the majority of held-out subjects. Although both approaches exhibited substantial inter-subject variability, Deep-MF achieved a higher average F1-score (0.37) compared to the standard network (0.34), indicating improved robustness to cross-subject differences. Performance varied considerably across participants. The best performance obtained by Deep-MF reached an F1-score of 0.71, exceeding the maximum score achieved by the standard model (0.59). These results showed that ERP-informed kernel initialization provides improvements in single-trial ERP detection under subject-independent evaluation. These findings demonstrate that integrating domain knowledge with deep learning architectures can improve single-trial ERP detection. The proposed approach provides a step towards practical wearable EEG and passive brain–computer interface applications, as well as towards real-time monitoring of cognitive processes. Full article

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article

Coal-based solid wastes, including coal gangue and fly ash, can be extensively utilised in cemented backfill materials. However, the slump, bleeding rate, and mechanical strength of these materials depend nonlinearly on the mixture composition, particle size, solids concentration, and curing conditions, complicating the multi-performance mixture design. This study developed an ensemble-learning framework for the target-specific performance prediction and empirical-uncertainty-aware inverse design of coal-based solid-waste cemented backfill materials. A literature-derived database containing 720 observations and 11 predictors was established. After the target-specific filtering of missing responses, 214 observations were available for the slump, 284 for the bleeding rate, and 711 for the uniaxial compressive strength (UCS). Support vector regression (SVR), Bagging-SVR, AdaBoost-SVR, and Stacking-SVR were evaluated using 20 repeated random 80:20 holdout partitions to assess the within-database predictive performance. Bagging-SVR achieved the lowest mean inner-cross-validation RMSE for all three responses. Its mean test R² values were 0.969, 0.871, and 0.965 for the slump, bleeding rate, and UCS, respectively, with corresponding RMSE values of 2.228 cm, 1.206 percentage points, and 1.575 MPa. SHAP analysis showed that the coal-gangue particle size and solids concentration received the largest model attributions for the slump and bleeding-rate predictions, whereas the cement content and curing time received the largest attributions for the UCS prediction. The selected Bagging-SVR models were subsequently coupled with multi-objective differential evolution incorporating empirical prediction bounds, component mass balance, and target-specific five-nearest-neighbour applicability-domain constraints. The selected compromise candidate had a solids concentration of 79.46% and coal-gangue, fly-ash, and cement dry-solid mass fractions of 63.29%, 24.95%, and 11.76%, respectively. Its predicted slump, bleeding rate, and 28 d UCS were 21.19 cm, 1.85%, and 6.36 MPa, respectively. The nominal empirical upper bound of the bleeding rate was 3.83%, and the lower bound of the UCS was 3.74 MPa, both satisfying their prescribed limits. However, the nominal slump interval of 15.70–26.65 cm was not fully contained within the prescribed range of 18–26 cm. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

The rapid deployment of low-Earth-orbit mega-constellations has increased the demand for reliable and scalable orbit-anomaly monitoring. Existing methods are vulnerable to heavy-tailed measurement errors, maneuver-induced propagation drift, and the anisotropic uncertainty of short observation arcs. This study proposes an uncertainty-aware Starlink monitoring framework that combines residual-domain wavelet shrinkage with a Huber-weighted adaptive two-body extended Kalman filter to suppress non-Gaussian contamination without obscuring abrupt state changes. A linear altitude correction and a quadratic phase-time correction are introduced into Simplified General Perturbations-4 propagation to compensate for maneuver-related forecast drift. A cross-time covariance model and a joint normalized innovation squared test are further constructed for uncertainty-aware short-arc maneuver sensing, while hierarchical evidence fusion supports anomaly detection and event interpretation. Across paired experiments, the filtering chain reduces position root-mean-square error from 752.4 ± 77.7 m to 175.9 ± 9.9 m, and compensated propagation reduces the 72 h prediction error from 128.7 km to 19.6 km. The detector achieves 93.4% accuracy with a 2.7% false-alarm rate and maintains empirical short-arc false-alarm probabilities near the nominal one percent level. These results demonstrate a consistent engineering link between catalog-scale screening and uncertainty-aware short-arc maneuver sensing. Full article

Multi-modal image matching (MIM) remains a challenging problem due to nonlinear radiometric variations and geometric distortions across heterogeneous sensors. This paper proposes a robust feature-based matching framework that reduces reliance on intensity information while enhancing structural representation. The filter with local normalization is applied to transform the input images into a common intermediate domain. A block-based strategy is then employed to enforce a uniform spatial distribution of keypoints using the ORB (Oriented FAST and Rotated BRIEF) detector. To further suppress intensity variations and improve discriminability, a novel Max-Index-based HOG (MIHOG) is developed. This descriptor integrates multi-scale feature representations and encodes dominant structural information through discrete max-index mapping. Finally, correspondences are established using a brute-force matching strategy. Extensive experiments are conducted on two multi-modal datasets covering eight diverse scenarios. The proposed method achieves an average NCM of 224.52, RMSE of 3.4788, and SR of 92%. MIHOG obtains the highest NCM on 4/8 test scenarios and improves the average NCM by 18.3% compared with the second-best method. Meanwhile, it maintains competitive computational efficiency, with an average running time of 10.20s. These results demonstrate that MIHOG can provide dense and reliable correspondences under complex cross-modal radiometric and geometric variations. Full article

Microelectromechanical systems (MEMS) have a widespread use in several applications such as signal filtering, time referencing, and sensing. This paper explores the nonlinear dynamic behavior of a MEMS resonator using a reduced-order modeling approach. The study focuses on how multi-modal formulation and detuning affect the system’s response under primary resonance. Using the method of multiple scales, amplitude–phase response equations are derived, and time-domain simulations are generated with the Runge–Kutta method. Two mode combinations are examined: the first mode combined with the second mode and the first mode with the third mode for multi-modal influence evaluation. Results indicate that the first mode provides the dominant behavior to MEMS response, while the second and third modes exhibit minimal participation despite the nonlinearities retained in the presented multi-modal model. Additionally, a detuning study reveals that the geometric and forcing nonlinear effects are stronger near resonance and diminish as the system moves away from it. The analysis suggests that the significant features of the response can be captured in the case of primary resonance using only the first mode, which offers an effective modeling approach. From a design perspective, finding that the first mode alone is sufficient means that the essential dynamic behavior of the MEMS resonator can be predicted and controlled by focusing on its first mode of vibration. In practical terms, this greatly allows engineers to optimize geometry, driving voltage, or control parameters to target the first mode natural frequency without accounting for higher modes, which reduces computational cost and design complexity. Full article