Search Results (147)

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

To investigate the impact of underground multiple stress disturbances on the long-term stability of cemented tailings backfill (CTB), this study conducted experiments under different disturbance levels (20–80% of static strength) and frequencies (1–4 times). By comprehensively utilizing mechanical testing, wave velocity monitoring, digital image correlation (DIC), and scanning electron microscopy (SEM), the “heterogeneous” evolution mechanism of macro-micro damage was revealed. The results indicate that disturbance level and frequency exert distinctly different driving effects on the deterioration of CTB, rather than a simple linear superposition. Specifically, low-frequency disturbance produces a compaction strengthening effect, microscopically promoting the generation of Ca(OH)₂ and ettringite (increased Ca/Si ratio). In contrast, the combination of high disturbance and high frequency induces free water extrusion and inhibits hydration, leading to an advanced damage threshold based on energy evolution and the accelerated coalescence of microcracks, which favors the formation of C-S-H gel (decreased Ca/Si ratio). Within this heterogeneous mechanism, the disturbance level acts as the dominant controlling factor. This study clarifies the nonlinear mechanical and chemical evolution paths under composite disturbances, providing theoretical support for the dynamic stability control of backfill in deep multi-step mining. Full article

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks. Full article

Capacitive electrocardiography (cECG) enables non-contact heart rate monitoring through clothing, but motion artifacts remain a critical limitation for practical applications. We present a novel motion artifact removal method using non-contact floating electrodes as noise references combined with multi-reference Normalized Least Mean Squares (NLMS) adaptive filtering. The floating electrodes, positioned without skin contact, couple primarily to ambient 50 Hz mains interference, which becomes amplitude-modulated during motion due to changes in electrode–body capacitance. Six reference signals are derived from this noise electrode: band-pass-filtered signal and its derivative (capturing baseline-type artifacts), envelope and its derivative (capturing amplitude modulation patterns), and envelope asymmetry and its derivative (capturing non-linear electrode response during motion). The NLMS algorithm adaptively combines these references to estimate and remove motion artifacts while preserving QRS morphology through low-pass filtering of the correction signal. A hysteresis-based motion detector with minimum duration constraints enables selective application of artifact removal only during motion periods, leaving rest-period ECG unmodified. We present this as a proof-of-concept validation of a novel reference-electrode architecture for motion artifact suppression in non-contact ECG. The method was validated on 7 subjects across 24 recording sessions using two electrode configurations in two environments with different electromagnetic interference levels. Controlled axial rotation motion was induced at three frequencies using a custom apparatus with IMU-based gamification for protocol adherence. Performance was evaluated using R-peak detection F1 score against gel surface-contact electrodes ground truth and RMS reduction in motion regions. Results demonstrate consistent improvement in R-peak detection accuracy during motion periods with substantial artifact energy reduction. The proposed method is designed to address motion artifacts regardless of their physical source, though the present validation focused on subject-induced motion. Full article

Underwater images often suffer from color cast, low contrast, scattering-induced haze, and texture degradation, which limit the performance of underwater visual perception systems. To address these problems, this study proposes FreqMambaGAN, a frequency-decoupled selective state-space cycle-adversarial network for underwater image enhancement. The proposed method is built upon a CycleGAN-style bidirectional translation framework and introduces a frequency-decoupled Mamba generator to separately model low-frequency color and illumination information and high-frequency texture and edge details. In addition, Efficient Mamba Blocks are embedded into the generator and discriminator to enhance long-range dependency modeling with linear computational complexity. Skip-attention connections are further adopted to preserve shallow spatial details during reconstruction. To improve training stability and imaging plausibility, a multi-stage training strategy is designed by combining supervised warm-up, unpaired cycle-adversarial learning, perceptual regularization, total variation smoothing, and a lightweight physics-inspired consistency constraint based on dark-channel and underwater image-formation priors. Experiments on public underwater image enhancement datasets demonstrate that FreqMambaGAN achieves competitive quantitative performance and visually improved enhancement results in terms of color correction, contrast restoration, haze suppression, and structural preservation. These results indicate that integrating frequency-domain decomposition with selective state-space modeling is effective for underwater image enhancement. Full article

The rapid evolution of deepfake generation techniques has made high-fidelity facial manipulation a critical threat to social credibility and personal privacy, demanding detection algorithms with strong cross-domain generalization. Existing methods suffer from two fundamental limitations: spatial-domain approaches cannot capture imperceptible forgery artifacts, while frequency-aware methods lack effective integration of spatial semantic and spectral features. To address these challenges, we propose FreqMamba, an end-to-end face forgery detection framework that adaptively aggregates spatial semantic features and frequency-domain artifacts via a gated late-fusion mechanism, and performs global sequence modeling using a bidirectional vision state space model (Vim). FreqMamba consists of three core components: a CNN branch for spatial semantic features, a hierarchical discrete wavelet transform (DWT) branch for fine-grained frequency artifacts, and a bidirectional Mamba backbone for global sequence modeling with linear complexity. The gated fusion mechanism adaptively combines multi-branch features, enhancing responses in forgery-rich regions while suppressing irrelevant noise. Trained exclusively on FaceForensics++ (c23), FreqMamba achieves strong cross-domain performance: on Celeb-DF v2, it attains 0.7767 AUC, surpassing a comparable-parameter CNN baseline (1.14 M parameters, 0.7262 AUC) by 5.05 percentage points; on the real-world WildDeepfake dataset, it achieves 0.6993 AUC, outperforming the lightweight CNN baseline (0.6272 AUC) by 7.21 points. Ablation studies confirm that DWT frequency priors and Mamba sequence modeling exhibit synergistic effects, and Grad-CAM visualizations validate the model’s focus on critical forgery regions. FreqMamba provides an effective approach for generalized face forgery detection in cross-domain scenarios. Full article

This paper applies physics-informed neural networks (PINNs) to laterally excited liquid sloshing in a two-dimensional rectangular tank, where near-resonant forcing (${\omega }_e/{\omega }_1=0.9$) produces a multi-frequency beating response with a period of approximately $10T_1$. Linearized potential flow theory governs the problem; the network learns the velocity potential $\phi (x,z,t)$ while the free-surface elevation $\eta$ is injected analytically. Two training obstacles specific to forced sloshing are analyzed. First, a zero-solution trap arises because the trivial solution $\widehat{\phi }=0$ satisfies all equations except the free-surface conditions, whose residuals are roughly ${10}^{-4}$ times smaller than the Laplace residual; characteristic-scale normalization combined with loss weighting (${\lambda }_D={\lambda }_K=100$) breaks this trap. Second, spectral bias prevents standard MLPs from resolving the three co-existing frequencies (${\omega }_1$, ${\omega }_e$, $\mathsf{\Delta }\omega$); a Fourier time embedding that augments the input from 3 to 9 dimensions overcomes this limitation. Two additional techniques further reduce errors: a hard-wall boundary condition enforced exactly via a $\cos (\pi x/B)$ spatial embedding, which eliminates wall collocation points; and a gradient-enhanced Laplace regularizer (${\parallel \nabla \left({\nabla }^2\widehat{\phi }\right)\parallel }^2$) that constrains velocity smoothness through third-order automatic differentiation. An ablation study shows that these four techniques progressively reduce the horizontal velocity error from ${\epsilon }_u=12.46\%$ to $0.84\%$. Results are validated against a viscous finite-difference benchmark. Over one beating cycle the errors are ${\epsilon }_{\eta }=0.15\%$, ${\epsilon }_u=0.84\%$, and ${\epsilon }_w=1.65\%$. A frequency parameter study across ${\omega }_e/{\omega }_1$ = 0.5–1.1 gives ${\epsilon }_{\eta }<0.25\%$ and ${\epsilon }_u<2.3\%$ for all near-resonance cases. For long-time simulation, a time-domain decomposition strategy with transfer learning partitions the domain into one-beat windows; extending to five beating cycles ($50T_1$) yields ${\epsilon }_u=3.43\%$ and ${\epsilon }_{\eta }=0.30\%$ with no monotonic error accumulation across windows. The methodology is then extended to a three-dimensional rectangular tank ($B\times W\times H$) with bi-directional lateral excitation. The 3-D formulation introduces the y-dimension into the Laplace equation (${\nabla }^2\phi ={\phi }_{xx}+{\phi }_{yy}+{\phi }_{zz}=0$), adds transverse wall boundary conditions ($\partial \phi /\partial y=0$) enforced exactly via a $\cos (\pi y/W)$ embedding, and extends the Fourier time embedding from 9 to 16 dimensions to accommodate six physical frequencies. The bi-directional excitation excites both $(m,0)$ and $(0,n)$ modal families, producing a genuinely three-dimensional beating response. Experimental results verify that the proposed methods can be well generalized to three-dimensional scenarios. Within a single beating cycle, the relative errors reach ${\epsilon }_{\eta }=0.24\%$, ${\epsilon }_u=1.31\%$, ${\epsilon }_v=1.78\%$ and ${\epsilon }_w=2.32\%$, with a total training time of 2499 s. By applying time domain decomposition to carry out two-cycle three-dimensional simulations, the model can steadily maintain satisfactory prediction precision across segmented time intervals, achieving overall errors of ${\epsilon }_{\eta }=0.30\%$ and ${\epsilon }_u=1.32\%$. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

Colorectal cancer is the third most commonly diagnosed cancer worldwide, and early detection of polyps via colonoscopy is essential for improving patient survival. However, automatic polyp segmentation faces three key challenges: balancing global context with local detail, delineating ambiguous boundaries under low contrast, and handling large variations in polyp size and morphology. To address these challenges, we propose WMA-Net, a Wavelet-Guided Mamba-Attention Network that uses wavelet-domain semantic–boundary separation as the organizing design principle. Rather than introducing a new individual operator, the contribution lies in how existing components—wavelet decomposition, Mamba state space modeling, multi-directional pixel difference convolution, and uncertainty-aware reverse attention—are combined and coordinated within one boundary-aware framework. The architecture integrates pixel difference convolution for multi-directional edge detection, frequency-selective cross-scale fusion with dual-stream wavelet-domain processing, Mamba-based multi-scale aggregation with linear complexity, and uncertainty-aware progressive boundary refinement. Extensive experiments on five public polyp benchmarks demonstrate state-of-the-art performance on four out of five datasets. On the seen datasets, WMA-Net achieves mean Dice scores of 94.4% on CVC-ClinicDB and 93.6% on Kvasir-SEG. On the unseen datasets, WMA-Net attains 91.7% on CVC-300, 82.3% on CVC-ColonDB, and 83.8% on ETIS-LaribPolypDB, demonstrating robust cross-dataset generalization. Comprehensive ablation studies validate the effectiveness and synergy of each proposed module. Full article

The free vibration of functionally graded (FG) beams under thermal environments is fundamental to understanding forced vibration, flutter, and thermal buckling in high-temperature structures. However, current research primarily focuses on theoretical modeling and numerical solutions, with limited mechanistic insights into temperature-dependent frequency variations and multi-factor effects. This study presents an analytical investigation coupled with experimental validation to characterize the vibration behavior of FG beams under thermal environments. First, governing equations for thermal vibration of FG beams are derived under uniform, linear, and nonlinear temperature fields based on the power-law assumption, the rule of mixtures, Timoshenko beam theory, and Hamilton’s principle. Subsequently, analytical expressions for natural frequencies and mode shapes are obtained using the state-space method. Then, experimental validation is performed to verify the model’s accuracy. Finally, the combined effects of temperature field, power-law index, slenderness ratio, and boundary conditions on the natural frequencies are systematically analyzed. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

Few-shot fault diagnosis of intelligent high-speed train components remains challenging because fault samples are scarce and highly imbalanced. To address this issue, this paper proposes MR-DDIM, a class-conditional diffusion-based data augmentation framework for generating high-fidelity fault vibration signals from limited labeled data. A WT-UNet denoising backbone is developed by combining one-dimensional wavelet convolution with Feature-Wise Linear Modulation (FiLM) to capture multiscale time–frequency structures and enable class-controllable generation. To improve training stability and spectral fidelity, log-σ regularization and a multi-resolution STFT consistency loss are introduced into the optimization process. In addition, this paper proposed the multi-resolution spectral correlation coefficient (MR-SCC) and class-intrinsic maximum mean discrepancy (cMMD) to evaluate generation quality from spectral and distributional perspectives. Experiments on the BJTU-RAO datasets show that the proposed method can generate fault samples with high spectral consistency and reasonable intra-class diversity, thereby improving the robustness of downstream few-shot fault diagnosis. The results indicate that MR-DDIM provides an effective data augmentation solution for intelligent fault diagnosis in high-speed railway systems. Full article

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of $70.16\%\pm 7.09\%$ with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by $+7.06$% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

High-power jamming may drive the radio-frequency (RF) front end of a satellite receiver into a nonlinear regime, thereby invalidating the linear superposition assumption underlying conventional excision and blanking methods. We formulate dual-receiver direct-sequence spread-spectrum (DSSS) anti-jamming as a nonlinear source-separation problem in complex baseband using stacked I/Q observations. We then propose a time-domain separator that jointly estimates the desired DSSS signal and the jammer on a designated reference receiver. The separator combines a multi-scale convolutional front end with a Transformer encoder and is pretrained on synthetic nonlinear mixtures that include multi-tone or burst jamming as well as typical satellite impairments, including Doppler/carrier-frequency offset (CFO), phase noise, multipath, and additive white Gaussian noise (AWGN). Robustness under high-jammer-to-signal-ratio (JSR) conditions is improved through high-JSR oversampling and JSR-aware loss reweighting. After Stage I supervised pretraining on labeled synthetic mixtures, an optional Stage II mixture-only adaptation step further refines the separator using nonlinear reconstruction consistency and lightweight communication-motivated priors. Across 1000 test mixtures with JSRs from −5 to 15 dB, SNRs from 15 to 25 dB, and cubic coefficients $a\in \left[\mathrm{0,0.5}\right]$, the proposed method improves the desired-signal scale-invariant signal-to-noise ratio (SI-SNR) from −4.79 dB for the mixture baseline to 13.32 dB after supervised pretraining and to 17.73 dB after mixture-only blind fine-tuning. Over the same test set, the failure rate (SI-SNR < 0 dB) decreases from 60.7% to 2.3%. Full article

This study presents a hybrid framework primarily designed to predict electrical energy consumption in tubular light pipe systems while also providing interpretability through wavelet-based analysis. Indoor and outdoor illuminance were continuously monitored at one-minute intervals between January and May in Istanbul, Turkey. Using the continuous wavelet transform (CWT) with predefined scale ranges, multi-scale features such as scale-wise energy, relative wavelet energy, and wavelet entropy were extracted to quantify illumination variability and stability. These features were combined with contextual parameters (e.g., month and weather) to predict electrical energy consumption and the energy-saving ratio under a threshold-based lighting control strategy. Among the evaluated models, Random Forest was selected as the primary model due to its balance between prediction accuracy and interpretability, achieving lower prediction errors compared to baseline models (RMSE = 7.84 for RF, 9.39 for Linear Regression, and 8.28 for ARIMA), although the observed improvements are influenced by the inherent variability in the dataset. Feature-importance and SHapley Additive exPlanations (SHAP) analyses revealed that low-frequency wavelet components and low Wavelet Entropy values were found to strongly influence the predictive behavior, indicating that stable illumination leads to reduced artificial lighting demand and higher energy savings. A Lyapunov-inspired stability interpretation suggests that the system exhibits stable behavior consistent with asymptotic convergence. Unlike existing studies, the proposed framework integrates wavelet entropy with interpretable machine learning to jointly model illumination dynamics and energy demand. This enables more reliable prediction of lighting energy demand under highly variable daylight conditions. Full article