Search Results (2,955)

Sensing mutual information (SMI) is widely adopted as a performance metric for integrated sensing and communication (ISAC) to enhance both sensing and communication capabilities. However, conventional approaches derive SMI from amplitude and phase, whereas an explicit evaluation of range mutual information (RMI) remains absent. In this paper, we investigate a novel closed-form approximation of RMI for ISAC. We first derive an explicit expression for the posterior probability density function (PDF) of the target range, which is formulated as a function of the signal’s autocorrelation and cross-correlation. Furthermore, we show that under high signal-to-noise ratio (SNR), the estimated range PDF approximates a Gaussian distribution in the sensing-unconstrained scenario and a truncated Gaussian distribution in the sensing-constrained scenario. Finally, we derive closed-form approximations of the RMI in both scenarios under high SNR. In the sensing-unconstrained scenario, the RMI is proportional to the delay interval, root-mean-square bandwidth, and SNR. In the constrained scenario, we obtain a closed-form RMI approximation by introducing an entropy correction term that quantifies the impact of boundary constraints. Additionally, we employ a maximum likelihood estimation (MLE) method to assess range estimation performance. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed approximations. Full article

In this study, variational Bayesian inference (VBI) with Gaussian mixture models is applied to update models of nonlinear structures, and then, the calibrated model is employed to estimate the failure probability of structures using a subset simulation (SS) algorithm. To improve the computation efficiency of probabilistic nonlinear model updating, a Gaussian Process (GP) model is used to construct a surrogate likelihood function in Bayesian inference using an active learning algorithm, and then, Gaussian mixture models (GMMs) are employed to approximate the unknown posterior probabilistic density functions (PDFs) of model parameters. The optimized hyperparameters of GMMs can be obtained by maximizing the evidence lower bound (ELBO), and the stochastic gradient search method is used to solve this optimization problem. Based on the optimized hyperparameters, the posterior distributions of model parameters can be approximated using a combination of multiple Gaussian components. Subsequently, the SS algorithm is used to calculate the earthquake-induced failure probability of structures based on the calibrated nonlinear model. To verify the feasibility and effectiveness of the proposed method, a numerical simulation of a two-span bridge structure subjected to seismic excitations was developed. Moreover, the proposed strategy is further applied to estimate the failure probability of a scaled monolithic column structure subjected to bi-directional earthquake excitations. Both numerical and experimental results indicate that the proposed method is feasible and effective for probabilistic nonlinear model updates, and the updated model can significantly enhance the accuracy of structural failure probability predictions. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

This work investigated the influence of thickness-direction boundary conditions on the flow characteristics of granular material in a quasi-two-dimensional silo using the discrete element method (DEM). Two types of boundary conditions were considered in the thickness direction: wall conditions and periodic boundary conditions. The simulation results indicate that under wall conditions, velocity waves propagate upward, manifested by the formation of bubble-like sub-flow zones in the velocity field, and the particle motion in the upper bed region exhibits a clear stick–slip feature. In contrast, under periodic boundary conditions, particle motion displays a resonant mode. Further statistical analysis reveals that, despite the distinct macroscopic motion mode under the two boundary conditions, the probability distributions of particle vertical fluctuating velocities share similar characteristics: both exhibit fat-tailed and asymmetric features and deviate from Gaussian distribution. Additionally, under wall conditions, the horizontal distributions of particle vertical velocity conform to the kinematic model throughout the bed, whereas under periodic boundary conditions, the horizontal distributions in the upper bed region display plug flow characteristics. In summary, the results of this work demonstrate that thickness-direction boundary conditions play a crucial role in determining the flow characteristics of granular assembly in silos. Full article

In this paper, we propose a novel method which utilizes samples of measured product quality characteristics to efficiently estimate the probabilities of those quality characteristics being within the desired specifications and, consequently, the process yield. Specifically, when dealing with 1D Gaussian distributions, we formally prove that the proposed yield estimator asymptotically gives a lower Mean Squared Error compared to the best unbiased estimator. In order to enable maximization of yield, this novel estimator is incorporated into the framework of Bayesian Optimization which iteratively seeks controllable tool parameters under which the outgoing product yield is maximized. The newly proposed yield maximization method is demonstrated in an application involving high-fidelity simulations of a reactive ion etch chamber, a tool component commonly used in semiconductor manufacturing. The aim of these simulations was to rapidly and reliably determine tool parameters that maximize the probability of delivering desired plasma density characteristics under stochastic variations in chamber conditions. The novel yield estimation and optimization methods show superiority when the number of experimental observations is limited and the distributions of outgoing product characteristics can be approximated well by a Gaussian distribution. Full article

Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail dependencies among random variables. However, existing research has not thoroughly explored the multivariate dynamic coupling characteristics related to forecasting errors, nor has it sufficiently considered the complementary advantages among different Copula functions. To address this, we propose a method for forecasting photovoltaic power output probabilities days in advance, integrating multivariate dynamic Copula functions with reinforcement learning. First, to capture the time-varying structure of photovoltaic power-related variables, we introduce a sliding time window for segmented modeling of historical data, fitting marginal probability distributions for predicted irradiance, forecasting power, and forecasting error. Second, a joint probability distribution of dynamic Gaussian Copula and t-Copula is constructed based on historical samples within the time window, generating a probabilistic prediction interval for the target time. Finally, reinforcement learning is employed to adaptively combine the probability prediction intervals derived from both Copula types, yielding the final photovoltaic power probability forecast. Simulations using actual operational data from a photovoltaic power plant in Shanxi Province validate the effectiveness of the proposed method. Full article

Equitable access to sports facilities is essential for promoting residents’ well-being, yet existing studies mostly rely on large spatial analytical units, limiting the ability to identify intra-unit disparities in accessibility and equity. This study develops a building-scale framework for assessing sports facility accessibility from a spatial equity perspective, incorporating building volume-weighted population distribution and quantification of multi-type facility service capacity for precise demand and supply estimation. Taking the Yuexiu District, Guangzhou, as the study area, the study assesses the accessibility of residential buildings using the Gaussian Two-Step Floating Catchment Area (G2SFCA) method and evaluates spatial equity using the Lorenz curve and local Moran’s I. Results indicate a moderate level of equity in overall facility provision (Gini coefficient = 0.288), alongside substantial inter-type disparities, with Gini coefficients ranging from 0.330 to 0.800. Accessibility clusters exhibit pronounced scale variability, ranging from a few buildings to hundreds of buildings, with small clusters embedded within larger clusters of opposite accessibility. These fine-grained patterns are largely obscured in conventional aggregated-unit analyses, underscoring the necessity of building-scale assessment. Results provide a basis for precise allocation of both facility quantity and facility types, supporting efficient decision-making for urban planning and management. Full article

We propose a probabilistic shaping-assisted base precoding quantum noise stream cipher (PSABP QNSC) scheme to effectively alleviate the encryption penalty in QAM QNSC systems. In contrast to the uniformly distributed bases adopted in traditional QNSC, Gaussian distributed bases can provide shaping gain. We theoretically analyze the underlying gain mechanism of Gaussian distributed bases in the PSABP QNSC scheme. Experimental results of 160 km reveal that the encryption penalties of QPSK and 16QAM are reduced by 0.44 dB and 0.27 dB, in terms of OSNR. Moreover, the security is quantified through the number of masked signals as a primary key metric. To mitigate the impact of base precoding, we propose the effective bases and effective ciphertext symbol points to refine the security evaluation. Moreover, the security is estimated in terms of mutual information leakage, with $2.2\times {10}^{-4}$ bits of QPSK and $1.85\times {10}^{-4}$ bits of 16QAM. The results indicate that the PSABP QNSC scheme provides effective protection against eavesdropping. Full article

Accurate prediction of protein sidechain conformations is a fundamental challenge in structural biology, with diverse applications ranging from protein structure determination to computational drug design. The performance of backbone-dependent rotamer libraries is often limited by discrete binning artifacts and difficulties handling sparse conformational regions. In this work, we present a Bayesian framework for rotamer prediction that addresses these limitations through Dirichlet priors and spatial smoothing. Our approach models rotamer probabilities as continuous functions of backbone dihedral angles, using circular Gaussian convolution, to make the most of statistical strength from neighboring conformations while respecting the periodic nature of angular data. We constructed rotamer libraries through structural clustering of sidechain conformations rather than chi angle binning, ensuring that each rotamer represents a distinct three-dimensional geometry. We evaluated and compared our framework against the state-of-the-art libraries on two independent test sets. Our Dirichlet model achieved chi angle prediction accuracy of 59–60%. Notably, our method produced consistently lower angular errors, an approximate 13% reduction in mean deviation, suggesting that the continuous probability distributions better capture subtle conformational preferences. Further, we explored the incorporation of non-sequential context by including the identity of nearby non-neighboring residues as an example of extensibility of our framework. Full article

We propose Distribution-Preserving Latent Steganography via Conditional Optimal Transport (DPL-COT), a coverless image steganography framework for latent diffusion models. Unlike classical cover-modifying schemes, DPL-COT embeds a bitstream directly into the initialization noise latent ${\mathbf{z}}_T\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ without model retraining. Our primary objective is high recoverability and a low bit error rate (BER) under deterministic inversion, which is inherently imperfect due to numerical discretization and VAE nonlinearity. To maximize decoding stability, we restrict embedding to the natural tails of the latent prior by selecting the largest-magnitude coordinates, thereby increasing the sign decision margin against inversion drift. To preserve distributional stealth, per-bit target values are analytically derived from truncated Gaussians matching the marginal distribution of the selected coordinates. Conditional 1D optimal transport is applied independently for each bit class, mapping every coordinate to its target value while preserving rank order. We generate 5000 stego images using a pretrained diffusion model and demonstrate a favorable capacity–reliability trade-off (e.g., 4916 bits/image with 0.473% mean BER) and strong robustness to JPEG compression (sub-1% mean BER at $Q=60$). Compared with LDStega, a recent LDM-based scheme reporting 99.28% clean-channel accuracy, DPL-COT achieves 99.53% at a comparable operating point and sustains above-99% accuracy under all tested JPEG quality factors. Latent-space tests further confirm negligible cover–stego distribution shift (mean ${\mathrm{KS}}_2<0.003$, mean $W_1<0.003$), a property not formally addressed by prior methods. Full article

In light of the evolution of computer science and computational mechanics, an uncertainty analysis of engineering systems has become increasingly feasible. In this paper, the failure of shallow foundations in layered soil continua is examined. It is shown that Gaussian input distributions lead to approximately Gaussian output response distributions even in the presence of an extensive nonlinear relationship between them. Soil configurations that provide larger average values and higher output variability in terms of bearing capacity force are those in which cohesive, stronger soils such as clays exist in the upper layers. Configurations with sandy soils in the upper layers, in several cases, provide greater average values of maximum displacements, rotations, and output variation. In this paper, the probabilities of the Meyerhof spline onset point are also estimated. Therefore, the proposed framework can support shallow foundation design decisions. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

To address challenges in complex group decision-making (GDM)—specifically preference fuzziness, intricate subgroup segmentation, and non-cooperative behavior—this study proposes an adaptive consensus model based on probabilistic linguistic term sets (PLTSs). By integrating fuzzy C-means (FCM) clustering with a Gaussian mixture model (GMM), a fuzzy Gaussian mixture model (FGMM) is constructed to achieve soft segmentation of expert preference distributions. On this basis, an adaptive consensus feedback mechanism is developed, which dynamically integrates interactive and automated adjustment strategies via multi-level consensus thresholds, thereby balancing decision efficiency and quality. To identify and control non-cooperative behaviors, a cooperation index and a three-tier management strategy, which incorporates intra-group negotiation, weight penalties and an exit-delegation mechanism, were introduced. In the case of strategic decision-making of new energy vehicles (NEV), after four rounds of feedback iterations, the group consensus level increased from the initial 0.316 to 0.804, reaching the preset threshold and verifying the effectiveness of the consensus mechanism. Compared with the existing literature methods, the framework in this paper achieves more comprehensive integration and innovation in four aspects: preference expression, clustering mechanism, consensus feedback and behavior management. Full article

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a behavior-oriented analysis to identify recurring speed-cushion traversal strategies using driving simulator telemetry. A fixed-base simulator reproduced a real urban corridor, and trajectories were segmented in device-centered spatial windows capturing approach, traversal, and immediate recovery. Each segment was summarized by three indicators describing longitudinal and lateral control: mean speed, peak braking demand, and average lane position deviation. Features were standardized and clustered using k-means. The number of clusters was selected primarily through mean silhouette evaluation, while resampling-based checks and a Gaussian mixture modeling comparison were used as supportive evidence rather than competing decision rules. Three traversal profiles emerged: smooth cautious, reactive cautious, and unmoderated fast. The introduction of speed cushions shifted the distribution of segments towards cautious profiles, while driver-level concentration within a single profile was moderate. Overall, results indicate that speed cushions influence the whole vehicle control strategy, offering a quantitative basis for behavior-oriented evaluation of local traffic calming interventions in smart-city contexts. Full article

Infrared UAV small target detection is critical in areas such as military reconnaissance, disaster monitoring, and border patrol. However, it faces challenges due to the small size of targets, weak texture, and complex backgrounds in infrared images. Existing deep learning-based object detection models often learn spurious correlations between targets and their backgrounds. This leads to poor generalization and higher rates of false positives and missed detections in complex scenes. To overcome feature bias and improve performance, this paper proposes an enhanced detection framework based on causal reasoning. The framework builds on the advanced CSFPR-RTDETR detector. Guided by the principles of structural causal models, it explicitly separates causal and non-causal features in the feature space. Feature debiasing is achieved through a three-path approach. First, a causal data augmentation module is introduced. It applies frequency perturbations drawn from a Gaussian distribution to non-causal features. This strengthens the model’s robustness against mixed disturbances. Second, a counterfactual reasoning module is integrated into the backbone network. This module generates counterfactual samples to intervene in the feature distribution, helping the model identify and utilize causal features more effectively. Third, a causal attention mechanism module is added to the encoder. By distinguishing and weighting causal and non-causal features, it guides the model to focus on features that are essential for detecting targets. Experiments on the HIT-UAV public dataset show that the proposed framework improves mAP@50 by 5.6% and mAP@50:95 by 1.8%. Visualization analysis further confirms that the framework enhances feature discrimination and overall detection performance. Full article