Search Results (6,222)

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust, subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism. Together, these components capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring no data leakage across subjects in the training, validation, and test subsets. Learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections provide model interpretability. An ablation study quantifies the contribution of each architectural component. Performance analysis includes evaluation at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT achieved competitive performance among the contrasted models, reaching subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore support evidence that EEG-TACT provides a compact, stable, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings. They also motivate further validation on larger, multi-site, and medication-controlled datasets. Full article

Maritime communication systems are among the most important infrastructure of global maritime safety and security. They consist of very high frequency (VHF) radio, the Global Maritime Distress and Safety System (GMDSS), contemporary satellite nets, Automatic Identification System (AIS) networks, and the emerging VHF Data Exchange System (VDES). These systems are essential for distress signaling, navigational coordination, and vessel traffic management. As maritime operations are experiencing accelerated digitalisation, the safety and security dimensions of maritime communication systems have attracted substantial and growing scientific attention. This study presents a comprehensive literature review and bibliometric analysis of the safety and security of maritime communication systems. Guided by the PRISMA 2020 guidelines and Systematic Literature Review (SLR) methodology, a structured search was conducted across three major scientific databases: Scopus, Web of Science (WoS), and IEEE Xplore. Starting from a raw pool of 6648 records retrieved between 2000 and 2026, the dataset was reduced through successive filtering to a final body of 68 high-relevance publications. Bibliometric analysis reveals a significant upward publication trend from 2015 onwards, with a marked acceleration after 2019. Thematic analysis identifies seven principal research clusters: GMDSS modernisation, AIS safety and security, VDES and VHF next-generation systems, maritime cybersecurity, satellite communications, risk assessment frameworks, and emerging technologies, including artificial intelligence and autonomous vessel communications. The review identifies significant research gaps, including the absence of integrated cross-system risk frameworks, insufficient attention to human factors in cybersecurity, limited studies addressing emerging regulatory, legal governance components and a brief analysis of the maritime communications market. This study provides a structured foundation for future research and policy development in maritime communication security. Full article

This paper addresses the torque generated in a six-phase permanent-magnet synchronous machine (6PMSM) when it is reused as a three-phase integrated on-board battery charger in electric vehicles. The inequality stator-phase disposition produces unequal equivalent inductances among the windings, which unavoidably creates electromagnetic torque. A novel six-phase open-end winding topology is first introduced: during charging, both sides of every open-winding act as grid-side harmonic filters; under ideal balanced conditions, the two halves carry currents that are equal in magnitude and opposite in direction, so the counter-rotating fields cancel and no net torque is produced. However, this perfect condition is difficult to achieve in the real system in practice. More than a 5% difference (inequality) in stator winding inductance can be observed at different rotor positions. Consequently, a dedicated current-control strategy is developed in order to compensate the unequal inductance, force the winding currents back into balance, and thereby eliminate the undesired torque while providing additional harmonic attenuation. In the proposed charging mode, the system operates at 0.99 power factor with zero average torque and a total grid current harmonic distortion (THD) of 3.47%. Experimental results verify that the proposed topology and control algorithm successfully keep the 6PMSM torque-free even when the machine is operated as grid filter inductance. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of $53.39\mathrm{m}$, compared with $878.75\mathrm{m}$, $58.84\mathrm{m}$, and $56.49\mathrm{m}$ for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions. Full article

End-to-end motion control of 7-degree-of-freedom (DOF) redundant manipulators under sparse reward signals presents a fundamental challenge in deep reinforcement learning (DRL) for robotics: the vast configuration space and absence of dense gradient information combine to produce severe cold-start failures and high cross-seed training variance. This paper proposes Curriculum-SAC-HER, a novel fusion framework integrating Soft Actor–Critic (SAC), Hindsight Experience Replay (HER), and a performance-driven three-stage Automatic Curriculum Learning (ACL) scheduler, designed to resolve the cold-start exploration bottleneck within a training budget of 300,000 environment interaction steps. The core methodology progressively expands the spatial target distribution across three stages of increasing difficulty, conditioning each stage transition on an 80% rolling success threshold to guarantee kinematic prior consolidation before advancing. A rigorous evaluation across 15 independent training runs (five seeds per group, all retained without filtering) demonstrates that the proposed framework achieves a final mean success rate of 84.8% (std: 11.0%), substantially surpassing the SAC + HER ablation (70.3%, Mann–Whitney U test, p = 0.028) and the DDPG baseline (22.3%, p = 0.008), while compressing cross-seed variance by 67% relative to the ablation. Zero-shot robustness evaluations under simulated domain perturbations further reveal that the learned policy maintains above 92% success across extreme friction variations and sustains 71.8% success under a 1.5× payload increase, demonstrating that the ACL module fosters generalized kinematic representations rather than over-fitting to specific contact mechanics. Full article

To address the challenge of hand intrusion detection in frozen tuna cutting operations where operators wear thermal-insulating gloves, this study proposes a hand detection method based on dual-domain background modeling with absolute accuracy constraints. To tackle issues arising from low-resolution infrared arrays, such as defective pixels, random noise, and complex low-temperature backgrounds, a data preprocessing pipeline integrating defective pixel correction, exponential moving average (EMA), and median filtering is developed. A dual-domain background suppression (DDBS) strategy, combining spatial-domain and temporal-domain models with sensor absolute accuracy constraints, is employed to extract hand foregrounds under complex thermal conditions. Temperature thresholding, connected-component analysis, and hole-filling are further applied to effectively separate hands from frozen tuna. An experimental platform incorporating a Raspberry Pi 4B and an MLX90640 sensor was constructed, and a dataset comprising 1173 infrared frames was collected for validation purposes. Experimental results demonstrate that the proposed method achieves an accuracy of 94.12%, precision of 91.69%, recall of 97.55%, and F1-score of 94.53% for hand intrusion detection, with an average processing time of approximately 1.84 ms per frame. This provides a cost-effective, real-time solution for hand safety monitoring in frozen food processing operations. Full article

The design of intravascular ultrasound (IVUS) catheters involves inherently coupled acoustic, hemodynamic, and structural requirements. Existing design strategies, which often rely on empirical geometric refinement or single-physics optimization, are limited in their ability to simultaneously ensure acoustic transmission efficiency, flow compatibility, and mechanical reliability. A multiphysics topology optimization method for the integrated design of IVUS catheters under acoustic–fluid–structure interactions is proposed in this paper. A density-based design variable is introduced to characterize the material distribution within the design domain, and consistent interpolation schemes are employed to relate this variable to the effective acoustic properties in the Helmholtz equation, the Brinkman penalization coefficient in the incompressible Navier–Stokes equations, and the elastic stiffness tensor in the structural equilibrium equation. The optimization problem is formulated as a normalized multi-objective minimization of acoustic transmission loss, flow resistance, and structural compliance, subject to constraints on material volume, received acoustic energy, wall shear stress, and structural displacement. Density filtering and smooth Heaviside projection are incorporated to regularize the design field and promote well-defined material boundaries. An adjoint sensitivity formulation is further developed to enable efficient gradient evaluation for the coupled system. Compared with the initial design, the average acoustic transmission efficiency has increased by $59.01\%$, the shear stress has decreased by $53.87\%$, and the stiffness matching rate has reached $98.27\%$. The objective function converged after 35 iterations, demonstrating the numerical stability of the proposed acoustic–fluid–structure topology optimization framework. Full article

Super high-rise buildings are increasingly equipped with structural monitoring systems to track deformation responses during construction and operation, thereby supporting structural condition assessment and engineering management. To address key monitoring challenges, including GNSS multipath interference, insufficient vertical accuracy, accelerometer integration drift, and high-frequency noise, this study proposes a GNSS/accelerometer fusion monitoring method based on whale optimization algorithm–optimized variational mode decomposition (WOA-VMD) and adaptive robust Kalman filtering (ARKF). Continuous three-hour GNSS and accelerometer observations collected from a super high-rise building under construction are used for fusion validation. The results show that WOA-VMD effectively separates noise from deformation-related signals and outperforms conventional EMD and standard VMD in denoising performance. Compared with the raw observations, the fused east, north, and vertical displacement RMSEs are reduced by 68.84%, 75.97%, and 60.22%, respectively; the SNRs increase to 22.03 dB, 21.38 dB, and 16.74 dB, respectively; the STDs decrease by 72.58%, 75.62%, and 68.39%, respectively; and the PSDs increase to 9.47 dB, 9.02 dB, and 8.31 dB, respectively. The proposed framework exhibits sub-centimeter-level displacement monitoring performance in the horizontal directions and significantly enhances the monitoring capability of the vertical component. The field validation results demonstrate the feasibility and effectiveness of the proposed framework for short-term deformation monitoring of super high-rise buildings under practical monitoring conditions and indicate its potential for structural health monitoring applications. Full article

Computed tomography angiography (CTA)-based segmentation of the superficial temporal arteries (STAs) and facial vessels (FVs) is important for neurosurgical and reconstructive planning. Nevertheless, segmentation of STAs and FVs remains challenging because of their small caliber, tortuous courses, and proximity to high-intensity bony structures. This study aims to develop a deep learning framework for accurate automated segmentation of these craniofacial vessels. A single-input 3D nnU-Net v2 model was trained using raw CTA volumes, while a Fusion-based Vesselness Map (FVM) was constructed from multiscale vessel-enhancement filters to emphasize small vascular structures and suppress irrelevant regions such as the skull and skin. Instead of being used as an additional input channel, the FVM was incorporated into the loss function as a spatial prior to guide the network toward vessel boundaries and distal branches. In 72 clinical cases, the FVM-guided model improved segmentation accuracy compared with a baseline model trained with Dice Focal Loss, particularly in boundary delineation. For the STAs, the Average Symmetric Surface Distance decreased from 6.543 mm to 2.941 mm. Qualitative evaluation further showed reduced segmentation noise and fewer false positives near bone and distal branches. These findings suggest that integrating classical vessel enhancement into deep learning supervision can improve morphologically consistent craniofacial vessel segmentation and support preoperative surgical planning. Full article

In satellite-denied environments such as urban canyons, tunnels, and underground parking facilities, achieving high-precision autonomous positioning for vehicles remains a critical challenge. Although high-precision inertial measurement units (IMUs) can provide accurate dead reckoning, their deployment is limited by cost, size, and power consumption, making low-cost, microelectromechanical systems IMUs (MIMUs) an attractive alternative solution. However, the single MIMU suffers from substantial measurement noise and bias instability, leading to rapid error divergence that cannot sustain long-term autonomous navigation. To address the above issues, this paper proposes an autonomous positioning system based on a wheel-mounted MIMU array (Wheel-AINS). The system adopts a differential layout in which multiple low-cost MIMU chips are installed at the center of each of the left and right rear wheels, forming redundant sensor arrays. By differentially fusing symmetrically mounted chips, common-mode noise and zero bias are effectively canceled while the wheel rotation provides natural rotational modulation. The fused gyroscope outputs and known wheel radius are then used to estimate the vehicle forward speed, replacing traditional odometers. The estimated wheel speed and vehicle kinematic constraints are then integrated within a Kalman filter framework to suppress the error divergence of the inertial navigation system. A dedicated embedded hardware prototype with multi-chip synchronous acquisition and wireless transmission was developed. Three groups of urban road tests with total distances of 0.85 km, 2.14 km, and 2.49 km were conducted. The results indicate that the average position drift rate of the Wheel-AINS is 0.50%, and the average heading RMSE is 12.2°. The closure error of the 2.49 km trajectory is 10.43 m, reduced by approximately 80% compared with a single MIMU. The ablation experiment reveals that the MIMU array fusion module is the primary source of accuracy improvement, reducing the position RMSE from 155.0 m to 10.1 m, while the dual-wheel distance constraint further optimizes the position RMSE to 8.2 m, but increases the heading RMSE from 13.3° to 13.6°. This demonstrates that the proposed method can substantially improve autonomous positioning accuracy while maintaining a notably low system cost, providing a viable technical pathway for long-endurance vehicle navigation in satellite-denied environments. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article