One of the central challenges in affective computing is achieving reliable emotion recognition for natural and effective human–computer interaction. In this study, we introduce KTU-MEDAFE (Karadeniz Technical University Multimodal Emotion Dataset using Audio, Facial Images, and EEG), a newly developed multimodal dataset containing synchronized EEG signals, speech recordings, and facial videos collected from 40 participants under controlled emotional elicitation conditions. The dataset includes Turkish emotional speech and two recording sessions conducted on separate days, providing a language-specific resource that supports both participant-dependent baseline evaluation and future session-separated analysis. Although KTU-MEDAFE comprises three modalities, the present study focuses on EEG and speech integration. EEG and speech recordings meeting signal quality criteria were transformed into image representations using the Angle–Amplitude Graph (AAG) method and classified using transfer learning with ResNet-50 and GoogLeNet architectures. To exploit complementary information across modalities, multiple decision-level fusion strategies were evaluated. Experimental findings show that multimodal fusion provides higher average classification performance than unimodal EEG and speech models across the evaluated binary emotion pairs, with performance varying according to subject, fusion strategy, and model architecture. Overall, the results support the potential benefit of combining EEG and speech for multimodal emotion recognition while highlighting substantial subject-dependent variability in classification performance.
Vehicular ad hoc networks (VANETs) have become a vital component of intelligent transport systems, with their security concerns increasingly drawing attention. To safeguard user privacy and ensure data authenticity and integrity, researchers have devised numerous certificateless conditional privacy-preserving authentication (CLCPPA) schemes. However, existing schemes generally suffer from insufficient security or high computational and communication overhead. Moreover, most implicitly assume the existence of a secure channel between vehicles and trusted entities during pseudonym generation and transmission, making it difficult to meet the real-time demands and practical deployment requirements of VANETs. To address these issues, this paper constructs a certificateless aggregated conditional privacy-preserving authentication (CL-ACPPA) scheme under elliptic curve cryptography that does not require bilinear operations. Formal security analysis demonstrates that, under the Random Oracle Model and the elliptic curve discrete logarithm problem assumption, the proposed scheme resists adaptive chosen-message attacks from adversaries with varying capabilities. Performance analysis and experimental results demonstrate that, compared with existing schemes, the proposed scheme achieves higher security while maintaining low communication and computational overhead.
Designing Low Earth Orbit (LEO) constellations for applications like Positioning, Navigation, and Timing (PNT) is a challenging multi-objective optimization challenge. Conventional metaheuristics often suffer from premature convergence due to their reliance on static adaptive rules, limiting their effectiveness in complex search space. To address this limitation, we proposed a hybrid framework where a Double Deep Q-learning Network (DDQN) agent learns a policy to adaptively control the key parameters of a Particle Swarm Optimization (PSO) algorithm. The proposed framework formulates Walker constellation optimization as an sequential parameter control problem. Based on constellation performance feedback, the DDQN controller jointly selects the inertia weight and acceleration coefficients of PSO, guiding the PSO to more effectively balance exploitation and exploration. In a regional design case for China, our algorithm demonstrated superior performance. Compared to a 120 satellites benchmark constellation, the optimized constellation achieved a 27% reduction in Geometric Dilution of Precision (GDOP), a 27.6% enhancement in navigation accuracy, and a 5% increase in coverage multiplicity. This work establishes a robust methodology for the automated and intelligent design of LEO systems, validating the potential of deep reinforcement learning methods for complex aerospace optimization problems.
This paper proposes a bidirectional LC resonance DC–DC converter for wide-range applications. By introducing an auxiliary Boost bridge arm on the HVS of the conventional structure and multiplexing the resonant inductor, the proposed converter extends the voltage gain range. Utilizing a combined PWM and PFM control strategy, the converter operates in multiple modes: two gain modes (medium and high) during battery discharge, and three gain modes (low, medium, and high) during battery charging. This multi-mode mechanism effectively extends the voltage gain range, narrows the switching frequency variation, and simplifies magnetic component design. Furthermore, ZCS operation is confirmed under the tested representative operating conditions, significantly reducing switching losses. Finally, an experimental prototype with a rated power of 750 W was developed to verify the performance for 40–120 V battery charging and discharging requirements. The experimental results demonstrate the effectiveness and validity of the proposed topology and control strategy for high-efficiency, wide-range power conversion applications.
As an emerging and powerful technology, unmanned aerial vehicles (UAVs) have tremendous potential applications in real-time monitoring, instant communication, data transmission, and more, providing ground terminal users with more efficient services and support. In this paper, we analyze the optimal networking scheme for user association with UAVs based on matching algorithms, which offer higher throughput and satisfaction. Particularly, to minimize algorithm complexity and enhance the efficiency of user devices, we propose a novel approach for stable UAV–user device pairing. This framework combines the concepts of density-based clustering algorithms and utility-driven matching algorithms. Firstly, we address the issue of large-scale scenarios with numerous and unevenly distributed user devices by proposing a clustering algorithm. This clustering algorithm divides the geographical area into multiple grids and clusters based on local density and relative distance within each grid. Next, we introduce a hierarchical matching game, where user clusters and UAVs are the players in the game. Each player ranks the other based on their individual utility functions, constructing preference lists of UAVs for users and vice versa. The network resource balancing and efficiency maximization are achieved through the matching process of bilateral selection. Simulation results demonstrate that this method exhibits low average required transmit power per user and the highest throughput among the five compared schemes under hotspot user distributions.
Composite overwrapped pressure vessels are increasingly used for hydrogen storage because of their lightweight construction. Ensuring their structural integrity therefore becomes an important requirement for safe operation. Ultrasonic guided waves are well suited for this task because they are highly sensitive to structural changes in thin-walled pressure vessels. In this work, we developed a machine learning framework based on interpretable Best Daubechies Wavelet features and k-Nearest Neighbors novelty detection. The framework identifies a persistent transition in the UGW response during overpressurization that is indicative of a permanent structural change and occurs prior to burst failure. It was validated using measurements acquired from a real-world pressure vessel. For the a priori selected sensor pair 11–12, located in the highly stressed cylindrical section, the method achieved a balanced accuracy of 98.28% and a true negative rate of 100%. In addition, the proposed methodology identified the pressure level at which the persistent structural transition first became detectable and showed that this transition remained detectable after the vessel had returned to its normal operating pressure.
With the rapid development of low earth orbit (LEO) constellations, LEO signals of opportunity (LEO-SOP) navigation has attracted extensive attention for positioning services in complex-denied environments. However, LEO-SOP suffers from limited coverage multiplicity and time-varying observation noise, leading to degraded positioning accuracy and availability. To address this issue, this paper applies an innovation-based adaptive unscented Kalman filtering (AUKF) scheme to LEO/inertial navigation system (INS) tightly coupled integration. By constructing the innovation sequence, the impact mechanism of noise uncertainty on filtering performance is analyzed, and an online noise covariance estimation strategy is designed to achieve dynamic adaptive compensation for both process and measurement noise. Simulation results demonstrate that the proposed method effectively suppresses the destabilizing effects of LEO observation noise and significantly improves the robustness and estimation accuracy of the filter in complex environments.
Particle swarm optimization (PSO) is a widely used method for solving single-objective optimization problems. However, PSO suffers from diversity loss and premature convergence, leading to suboptimal performance in complex problems. To address these limitations, this paper introduces a novel self-adaptive multi-learning strategy PSO (SMLS-PSO) designed for real-parameter optimization tasks. SMLS-PSO integrates four distinct learning strategy-based PSO variants into a behavior pool and employs a new self-adaptive strategy selection mechanism. This mechanism dynamically chooses the most suitable learning strategy to update the velocities and positions of particles based on fitness information and payoff at different evolutionary stages. To enhance the performance of SMLS-PSO, three key improvements are incorporated: (1) a stagnation counter parameter to minimize wasted fitness evaluations; (2) a boundary symmetry mapping method to manage out-of-range searches; and (3) a Quasi-Newton local search operator to boost local exploitation capabilities. SMLS-PSO is first compared with four component PSO variants on 13 basic benchmark functions. Subsequently, SMLS-PSO is compared with eight state-of-the-art PSO variants on both 30D and 50D CEC2017 test suite problems. Experimental results indicate that SMLS-PSO statistically and significantly outperforms the compared algorithms on the majority of the test problems, showcasing its superior optimization capabilities. Finally, SMLS-PSO was applied to the optimization problem of UWB anchors layout, and it achieved a higher locatable space coverage rate and a better average HDOP value compared to the conventional layout scheme and other PSO variant optimization schemes.
Pilgrim safety during Hajj is challenged by dense crowds, sustained physical exertion, and demanding environmental conditions that can increase tiredness and compromise well-being. Although wearable sensing enables objective monitoring, the reviewed literature provides limited evidence on how synthetic augmentation affects predictive performance and cross-participant generalization when wearable data are scarce. To address this gap, this study presents an integrated framework that combines correlation- and domain-informed feature screening with statistically constrained synthetic data generation and a complementary assessment of predictive performance and synthetic data fidelity. Using the Hajj 1445 (June 2024) Apple Watch dataset comprising 120 records from three participants, the study compares classical resampling, univariate normal generation, multivariate normal (MVN) sampling, and covariance-aware generation using Cholesky decomposition and Mahalanobis distance filtering under a consistent classifier evaluation protocol. The highest observed record-level test accuracy is 93.33%, achieved by a Decision Tree using MVN sampling with moderate clipping (±1.4). Leave-one-participant-out evaluation of the same configuration yields a mean accuracy of 68.65% (SD = 19.72%), indicating lower cross-participant generalization. The regularized MVN–Cholesky–Mahalanobis configuration attains a composite synthetic-data quality score of 79.27%. These results show that statistically constrained synthetic augmentation can support tiredness prediction when real data are limited, while the participant-grouped results highlight the need for validation using larger and more diverse Hajj cohorts.
In line-of-sight sensing and surveillance systems, angle-of-arrival (AOA) localization accuracy depends strongly on sensor geometry. The spatially distributed sensors considered here may be electromagnetic, acoustic, optical, or other directional sensing devices that provide bearing measurements with known nominal accuracy. Existing frame-theoretic sensor-augmentation studies usually optimize the added-sensor bearings for a fixed number of sensors, whereas practical network design often requires determining the minimum number of sensors needed to satisfy a prescribed accuracy constraint. Poorly balanced bearing geometry can make the Fisher information matrix (FIM) ill-conditioned and increase localization uncertainty. This paper addresses this problem using the A-optimal Cramér–Rao lower bound trace as the accuracy measure, because it represents the sum of the lower bounds on the coordinate estimation-error variances. The existing Fisher information matrix is described by its total information, strong information direction, and eigenvalue gap. Each additional sensor is represented as a weighted vector in a double-angle plane, which converts the bearing-design problem into a planar vector-balancing problem. This representation separates the effects of total information and directional imbalance on localization performance. A polygon condition is used to determine the minimum achievable imbalance and A-optimal cost for fixed sensor weights. For equal-weight sensors, a direct rule is derived for finding the minimum required number of additional sensors, together with explicit bearing constructions for weak-direction compensation and tight-frame completion. The analysis is also extended to unequal sensor weights. Numerical optimization and Monte Carlo localization experiments confirm the analytical results and show that the proposed method improves information balance, FIM conditioning, empirical localization accuracy, and tail-error performance compared with the reference configurations. Random-network and target-position mismatch experiments further evaluate the design beyond the nominal network geometry.
Anticipatory postural adjustments (APA) generate initial center of mass motion during gait initiation and are associated with gait performance. While APA amplitude and duration have been linked to gait speed under single-task conditions, it remains unclear how these relationships are altered when motor control is constrained by cognitive demands. This study aimed to examine the association between APA characteristics prior to gait initiation and gait speed under dual-task conditions with externally paced cognitive load. Thirty-one healthy young adults performed fast walking under single- and dual-task conditions. Gait speed and APA parameters, and first-step range of motion were assessed using inertial measurement units. To examine associations between APA parameters and gait speed, multiple regression analyses were conducted separately for each condition. Longer APA duration was associated with decreased gait speed under both conditions. Greater anteroposterior amplitude of APA was associated with increased gait speed only under the dual-task condition, whereas no such association was observed under the single-task condition. No significant association was observed between cognitive performance and gait speed. These findings indicate that the association between APA characteristics and gait speed differs depending on task demands, suggesting that cognitive-motor constraints may modify the role of anticipatory control in gait performance.
Bearings are core components of rotating machinery, and the development of precise and efficient fault diagnosis technologies is of paramount importance for realizing early warning and accurate localization of faults. This paper briefly analyzes bearing fault mechanisms and provides a comprehensive review and critical commentary on the research progress of bearing fault diagnosis methods, outlining future development trends. Specifically, this study begins by introducing common bearing fault types and reviewing the advancements in fault signal acquisition techniques. Subsequently, it categorizes fault diagnosis methods based on vibration signals and critically evaluates their respective research methodologies. Furthermore, focusing on non-contact acoustic signal diagnosis, the paper summarizes mainstream technical pathways for acoustic signal denoising and highlights innovative applications of deep-learning models tailored to acoustic characteristics. Finally, addressing the urgent demands for industrial deployment, future research directions are projected from three perspectives: the deep integration of physics-driven and data-driven multi-modal fusion, interpretable diagnosis assisted by Large Language Models (LLMs), and lightweight engineering deployment. This work aims to provide a reference for constructing an all-scenario intelligent monitoring system.
Sleep respiratory monitoring plays a crucial role in the early diagnosis and health management of sleep-related respiratory disorders. However, conventional monitoring devices often suffer from some critical issues, such as poor wearing comfort and insufficient capability for long-term continuous monitoring, hindering their applications in non-invasive and prolonged sleep monitoring. In recent years, hydrogel-based mechanical sensors have attracted increasing attention for sleep respiratory monitoring owing to their outstanding flexibility, biocompatibility, mechanical compatibility with biological tissues, and excellent sensing performance, demonstrating great potential for practical applications. A comprehensive overview of recent advances in hydrogel-based mechanical sensors for sleep-related respiratory monitoring is provided in this review. The sensing mechanisms, materials, fabrication strategies, and representative applications are systematically summarized, with particular emphasis on the key factors governing sensor performance and their translation toward practical use. Furthermore, current challenges and corresponding optimization strategies are discussed. This review aims to provide valuable insights into the rational design and optimization of high-performance hydrogel-based mechanical sensors, and important strategies for the further development of advanced technologies for sleep-related respiratory monitoring.
Wearable sensors enable continuous, non-invasive monitoring of physiological and near-body environmental parameters, offering an occupant-centric alternative for thermal comfort assessment and building control. This systematic review analyses 117 studies (2008–2026) using body-worn sensors, tracing them along a four-stage pipeline: sensing, signal integration, comfort modeling, and building actuation. This reveals a previously unquantified bottleneck: while all 117 studies perform sensing and 83 (71%) integrate physiological and environmental signals, only 52 (44%) build predictive models and just five (4%) reach the building-actuation stage, of which only three implement closed-loop, wearable-informed control. Skin temperature (77 studies, 66%) and heart rate (69, 59%) are the most monitored signals, predominantly at the wrist (70, 60%); multimodal configurations are associated with higher accuracy than single-domain approaches. Machine learning models reach median classification accuracies near 90%, though validation strategy matters: leave-one-subject-out reaches 85% versus 90% for within-subject k-fold. Five structural limitations are identified: small, homogeneous samples (median 16 participants), laboratory-dominated designs (73 studies, 62%), inconsistent validation, limited open data, and unresolved multi-occupant aggregation. The field has learned to measure the occupant but not yet to act on the measurement. Closing this gap requires open benchmark datasets, standardized validation including leave-one-subject-out testing and PMV benchmarking, transfer learning, multi-occupant integration, and inclusive recruitment of vulnerable populations.
This paper presents a likelihood-consistent joint target-message receiver for a strictly constant-envelope continuous phase modulation (CPM) waveform in pulsed phased-array joint radar–communication (JRC). Station A performs monostatic detection with the transmitted message, while Station B performs bistatic detection and communication with an available, degraded, or absent target-independent reference path. The target-scattered message is included only under the target-present hypothesis. A log-domain 16-state recursion evaluates the exact finite-alphabet CPM message marginal over the complete 46+2-symbol, 92-bit frame and is verified against direct enumeration on a six-bit unit frame to floating-point precision. At a 10 dB reference-path SNR, marginalization increases detection probability relative to single-path max-log reconstruction by 4.67 percentage points (95% paired interval 1.33–8.00) and 8.33 percentage points (4.33–12.33) at target-path SNRs of 10 and 14 dB, respectively; the corresponding joint-success differences are zero. Across 2048 ordered error events, the directed conditional distance has a Spearman correlation of −0.970 with empirical pairwise error probability. The CPM parameterization guarantees a constant active-pulse envelope at every array element, whereas constrained common-phase selection provides only a secondary adjustment of message-dependent ambiguity sidelobes. System-level experiments in a coastal setting use independent 92-bit messages and matched transmission resources for the proposed CPM waveform and RRC-QPSK. Both waveforms recover all evaluated direct-path payloads, showing that the proposed constant-envelope waveform preserves communication reliability at the tested operating point. The same records demonstrate geometry-consistent monostatic and bistatic target recovery, while finite-scatterer tests define the boundary of the single-scattering-center model.
Strong and optically conspicuous frontal boundaries are common in the river-dominated Louisiana–Texas Shelf (LTS). Variable wind stress forcing along cross-shelf density gradients results in convergences/divergences that may lead to rapid vertical water mass displacements. In cases where near-bottom shelf waters are displaced to the surface, the associated optical anomaly is often distinct in satellite ocean color data. A satellite-observed optical feature consistent with near-bottom-water ventilation is examined over the LTS with multiple satellite-based ocean color radiometers (OLCI, VIIRS), as well as data from the Advanced Baseline Imager (ABI) on the geostationary GOES-R platform. In the aftermath of an atmospheric cold front passage and sustained northerly winds, the combined satellite analysis reveals a rapidly westward moving optical front delineated by a sharp visible-band reflectivity gradient. Ocean model simulations reproduce a qualitatively similar displacement of surface density fields that is consistent with advection by the along-front current with a potentially modulating influence from the diurnal heating. This study serves as an example of the kind of analyses that may be possible from future geostationary ocean color missions.
The Global Navigation Satellite System (GNSS) has been widely adopted in navigation applications due to its high accuracy and convenience. However, in urban canyon environments, severe signal blockage caused by buildings and trees introduces substantial non-line-of-sight errors and multipath effects, leading to degraded and highly fluctuating positioning performance. To address this issue, this paper proposes a residual-guided hybrid stochastic modeling framework. The method operates in two stages: first, a pseudo-range correction estimation network takes GNSS parameters containing residuals as input to estimate pseudo-range correction; second, these estimated corrections together with elevation angle and carrier-to-noise ratio are fed into a hybrid stochastic model parameter estimation network to determine model parameters. This design adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios. Meanwhile, the explicit modeling of relationships among pseudo-range correction, elevation angle, and carrier-to-noise ratio renders the stochastic model parameters interpretable. Experiments on public urban GNSS datasets demonstrate that the proposed method achieves competitive positioning performance against both conventional and learning-based baselines. It delivers notable accuracy improvements in light urban canyon environments, particularly on the KLT2 sequence, while maintaining robust and competitive performance in the more challenging TST and Mong Kok scenarios. These results validate the effectiveness of jointly estimating pseudo-range corrections and adaptive observation weights for enhancing positioning accuracy and robustness across diverse urban environments.
Remote photoplethysmography (rPPG) estimates heart rate (HR) from facial color variations. In continuous wavelet transform (CWT) scalograms, HR is represented by position along the frequency axis, whereas global average pooling (GAP) does not retain this coordinate explicitly. We investigated whether a structured frequency-localizing readout improves HR estimation and analyzed the remaining errors. Leave-one-subject-out evaluations were conducted on three datasets (PURE, UBFC-rPPG, and BH-rPPG) using a fixed chrominance-based signal-extraction and CWT pipeline. The proposed model combined a frequency-preserving convolutional neural network backbone with a soft-argmax readout over HR coordinates. Using the same backbone and training protocol, soft-argmax reduced segment-pooled mean absolute error (MAE) relative to the matched GAP readout from 8.09 to 2.98 beats per minute on PURE and from 5.14 to 3.82 beats per minute on UBFC-rPPG, with significant subject-level improvements on both datasets. Residual analyses indicated that major errors could originate from color projection, non-cardiac spectral components, motion, and HR-range mismatch. On BH-rPPG, the proposed model achieved the lowest MAE under low illumination, while the evaluated CWT-based estimators showed smaller illumination-induced degradation than the evaluated fast Fourier transform (FFT)-based estimators. These findings support structured soft-argmax readout design and highlight the need to detect or mitigate degraded input signals.
Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhanced Multi-Stage Reinforcement Learning Strategy is proposed. First, an uncertainty quantification model under multi-source interference is established to characterize time-varying noise and accurately quantify the ranging errors of heterogeneous links. Subsequently, using the resulting ranging variance, an adaptive weight allocation mechanism based on Minimum Variance Unbiased Estimation is constructed to dynamically adjust link weights, achieving the robust fusion of multi-modal observation data. Finally, a Weighted Least Squares objective function is formulated, and the GP-AC strategy is developed. By utilizing Gaussian Process Regression and local Geometric Dilution of Precision, a multi-stage reward mechanism is constructed to circumvent topological traps and accurately estimate the single-epoch three-dimensional coordinates of static or quasi-static underwater sensor nodes. Simulation results demonstrate that system robustness is improved by 91.9%, average accuracy is enhanced by 54.9%, and the measured average localization time is 7.45 s.
Continuous monitoring of permafrost dynamics remains challenging in remote high-mountain environments due to logistical constraints, harsh climatic conditions, and the limited availability of spatially distributed observations. In addition to direct temperature observations in boreholes, Autonomous Electrical Resistivity Tomography (A-ERT) offers significant potential for long-term monitoring by providing high temporal resolution observations of subsurface electrical properties, which are highly sensitive to freeze/thaw processes. This study presents the field validation of a low-power A-ERT system designed for long-term autonomous operation in extreme environments. The system was deployed at a high-altitude permafrost site near the Kumtor gold mine in the Central Tien Shan, Kyrgyzstan, representing the first application of continuous A-ERT monitoring in the Central Asian mountain ranges. The system operated continuously under harsh environmental conditions with air temperatures as low as −30 °C. Data quality remained consistently high throughout the monitoring period, with less than 1% of measurements removed during filtering, and inversion results with root-mean-square errors generally ranging between 3% and 4%. Time-lapse resistivity observations revealed strong seasonal freeze–thaw dynamics within the active layer and continued seasonal resistivity variations within the underlying permafrost despite permanently frozen conditions. Analysis of depth-dependent resistivity–temperature relationships revealed increasingly pronounced hysteresis behavior below the active layer, indicating that subsurface electrical properties were not controlled solely by temperature. This behavior likely reflects variations in unfrozen water content and pore connectivity within the fine-grained permafrost, where liquid water can persist at sub-zero temperatures. The results demonstrate the capability of the A-ERT system for reliable long-term autonomous monitoring in remote permafrost environments and investigation of coupled thermal and hydrological processes in permafrost systems.
