Search Results (590)

We investigate the structure of correlation singularities for the Laguerre–Gauss beam of order $n=0$ and $m=2$ in the transverse plane during the propagation of the beam in the beam-wander model. We explicitly derive analytical expressions for the cross-spectral density of the corresponding beam order and the analytic expressions representing the singular behavior. We also verify that the singular points disappear at certain z values and reappear at other z values as shown in the previous numerical study. We investigate the dependence of the absolute value of the complex degree of coherence $\mu$ on the parameter $\delta$ of the beam-wander model during the propagation of the Laguerre–Gauss beam in the corresponding order. The complex degree of coherence depends not only on $\delta$ but also on the relative positions of two transverse observation points ${\mathit{\rho }}_1$ and ${\mathit{\rho }}_2$, as well as on the propagation variable z for the fixed values of the beam waist and the wavelength of the Laguerre–Gauss beam. Experiments on $\mu$ can demonstrate the range of the applicability of the beam-wander model in the turbulent atmosphere. Finally, we examine the orbital angular momentum flux density of the beam and confirm that the general behaviors of the previous studies also hold for $m=2$. Full article

In the context of bearing anomaly detection, challenges such as imbalanced sample distribution and complex operational conditions present significant difficulties for data-driven deep learning models. These issues often result in overfitting and high false positive rates in complex real-world scenarios. This paper proposes a strategy that leverages multimodal fusion and Self-Adversarial Training (SAT) to construct and train a deep learning model. First, the one-dimensional bearing vibration time-series data are converted into Gramian Angular Difference Field (GADF) images, and multimodal feature fusion is performed with the original time-series data to capture richer spatiotemporal correlation features. Second, a composite data augmentation strategy combining time-domain and image-domain transformations is employed to effectively expand the anomaly samples, mitigating data scarcity and class imbalance. Finally, the SAT mechanism is introduced, where adversarial samples are generated within the fused feature space to compel the model to learn more generalized and robust feature representations, thereby significantly enhancing its performance in realistic and noisy environments. Experimental results demonstrate that the proposed method outperforms traditional baseline models across key metrics such as accuracy, precision, recall, and F1-score in abnormal bearing anomaly detection. It exhibits exceptional robustness against rail-specific interferences, offering a specialized solution strictly tailored for the unique, high-noise operational environments of intelligent railway maintenance. Full article

Impairments in balance control are common across various clinical conditions and with aging, necessitating reliable methods for assessment. This study introduces a novel, low-cost posturographic system based on an unstable spring-supported platform that calculates center of pressure (COP) displacement using angular measurements in two horizontal axes. A heterogeneous sample of 105 participants underwent repeated trials on both the novel system and a traditional firm platform under eyes-open and eyes-closed conditions. COP velocity was recorded and analyzed for reliability using intraclass correlation coefficients (ICCs). The results showed significantly higher COP velocity on the unstable platform when visual input was removed, indicating greater reliance on visual control under unstable conditions. The novel system demonstrated comparable reliability to traditional platforms, with ICC values exceeding 0.90 when mean values from three trials were used. No learning effect was observed on the unstable platform, unlike the firm one. These findings suggest that the new system is a valid alternative for balance assessment, which is particularly effective in differentiating individuals with varying balance capabilities under eyes-closed conditions. Its affordability and methodological soundness make it suitable for clinical use and broader screening applications aimed at fall prevention. Full article

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

To obtain radar images of a group of small space objects or to resolve individual elements of complex space objects in near-Earth orbit, a radar system must have high spatial resolution. High range resolution is achieved by using complex probing signals with a wide spectrum bandwidth. Achieving high angular resolution for small or complex space objects is based on the inverse synthetic aperture antenna effect. Among the various classes of complex signals, only two have found practical application in Inverse Synthetic Aperture Radar (ISAR) systems so far: the Linear Frequency-Modulated signal (chirp) and the Stepped-Frequency signal. Over the coherent integration interval of the echo signals, which corresponds to the ISAR aperture synthesis time, the combined correlation characteristics of the signal ensemble are analyzed. A high level of integral correlation noise in the ensemble of probing signals degrades the quality of the radar image. Therefore, a probing signal with a Zero Autocorrelation Zone (ZACZ) is highly relevant for ISAR applications. In this work, through simulation, radar images of a complex space object were obtained using both chirp and ZACZ probing signals. A comparative analysis of the correlation characteristics of the echo signals and the resulting radar images of the complex space object was performed. Full article

Accurate evaluation of demand response (DR) potential at the individual user level is critical for the effective implementation and optimization of demand response programs. However, existing data-driven methods often suffer from insufficient feature representation, limited characterization of load profile dynamics, and ineffective fusion of heterogeneous features, leading to suboptimal evaluation performance. To address these challenges, this paper proposes a novel demand response potential evaluation method based on multivariate heterogeneous features and a Stacking-based ensemble mechanism. First, multidimensional indicator features are extracted from historical electricity consumption data and external factors (e.g., weather, time-of-use pricing), capturing load shape, variability, and correlation characteristics. Second, to enrich the information space and preserve temporal dynamics, typical daily load profiles are transformed into two-dimensional image features using the Gramian Angular Difference Field (GADF), the Markov Transition Field (MTF), and an Improved Recurrence Plot (IRP), which are then fused into a single RGB image. Third, a differentiated modeling strategy is adopted: scalar indicator features are processed by classical machine learning models (Support Vector Machine, Random Forest, XGBoost), while image features are fed into a deep convolutional neural network (SE-ResNet-20). Finally, a Stacking ensemble learning framework is employed to intelligently integrate the outputs of base learners, with a Decision Tree as the meta-learner, thereby enhancing overall evaluation accuracy and robustness. Experimental results on a real-world dataset demonstrate that the proposed method achieves superior performance compared to individual models and conventional fusion approaches, effectively leveraging both structured indicators and unstructured image representations for high-precision demand response potential evaluation. Full article

The growing density of Earth-orbiting objects demands improved Space Situational Awareness (SSA) to mitigate collision risks and sustain space operations. This study demonstrates a dual-purpose star tracker (ST) for SSA using data from the Resident Space Object Near-space Astrometric Reconnaissance (RSONAR) stratospheric balloon campaign under the 2022 Canadian Space Agency–Centre National d’Études Spatiales (CSA–CNES) STRATOS program. The low-cost optical payload—a wide-field monochromatic imager flown at 36 km altitude—acquired imagery subsequently used for post-processed attitude determination and Resident Space Object (RSO) detection. During stabilized pointing, over 27,000 images yielded sub-pixel astrometry and stable image quality (mean full-width-Half-maximum ≈ 388 arcsec). Photometric calibration to the Tycho-2 catalog achieved 0.37 mag root mean square (RMS) scatter, confirming radiometric uniformity. Apparent angular velocities of $7\times {10}^2$ to $8\times {10}^3$ arcsec ${\mathrm{s}}^{-1}$ corresponded to sunlit low-Earth-orbit (LEO) objects observed at 25°–35° phase angles. Covariance-weighted Mahalanobis correlation with two-line elements (TLEs) achieved sub-arcminute positional agreement. The Proximity Filtering and Tracking (PFT) algorithm identified 22,036 total RSO and 387 total streaks via image stacking. Results confirm that commercial off-the-shelf STs can serve as dual-use SSA payloads, and that stratospheric ballooning offers a viable alternative for optical SSA research. Full article

Background: Accurate assessment of foot morphology is essential in sports medicine, orthopaedics, and footwear design. Manual examination remains common but may lack accuracy and reproducibility. Alternative techniques, such as pedobarography and handheld 3D scanning, may offer more objective and reliable data, given that their reliability and agreement with established methods are confirmed. Methods: Twenty-six healthy adults (age 25.8 ± 4.7 years; BMI 24.1 ± 2.0 kg/m²) were investigated. Foot dimensions were assessed via manual examination, pedobarography, and handheld 3D scanning, each performed in random order by two independent investigators on two separate occasions. Relative and absolute intra-rater reliability were analysed using intraclass correlation coefficients (ICC), the change in the mean of repeated measurements (bias), limits of agreement (LoA), and the typical error (TE). Inter-method agreement was evaluated using Lin’s concordance correlation coefficients (CCC), mean bias, and LoA to assess interchangeability as well as systematic bias. Results: Good-to-excellent relative and absolute intra-rater reliability was found for the distance-related foot dimensions across all methods, except for heel width assessed via pedobarography (small bias but wide LoA and high TE). Relative and absolute reliability of the angular parameters assessed via pedobarography and 3D scanning ranged from poor to excellent. Inter-method agreement between manual examination, pedobarography, and 3D scanning appeared low when considering all three agreement indices (i.e., CCC, mean bias, and LoA). The largest discrepancies were observed for heel width and arch-related measures. Conclusions: All three methods seem reliable for assessing distance-related foot dimensions. However, limited agreement among the three methodological approaches indicates that they cannot be used interchangeably without standardisation. Full article

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

Background/Objectives: Single plane wave imaging (SPWI) offers ultrafast acquisition rates suitable for real-time ultrasound imaging applications; however, its image quality is compromised by beamforming artifacts such as sidelobe and grating lobe interferences. Methods: In this paper, we introduce an unsupervised beamforming framework based on adaptive deep coherence loss (DCL-A), which employs linear (${\alpha }_{\mathrm{linear}}$) or nonlinear weighting (${\alpha }_{\mathrm{nonlinear}}$) within the coherence loss function to enhance the artifact suppression and improve overall image quality. During training, the adaptive weight (α) is determined by the angular distance between the input and target PW frames, assigning lower α values for smaller distances and higher α values for larger distances. Therefore, this adaptability enables the method to surpass conventional DCL (no weighting) by emphasizing the different spatial correlation characteristics of mainlobe and sidelobe signals. To assess the performance of the proposed method, we trained and validated the network using publicly available datasets, including simulation, phantom and in vivo images. Results: In the simulation and phantom studies, the DCL-A with ${\alpha }_{\mathrm{nonlinear}}$ outperformed the comparison methods (i.e., conventional DCL and DCL-A with ${\alpha }_{\mathrm{linear}}$) in terms of peak range sidelobe level (PRSLL), achieving 7 dB and 14 dB greater sidelobe suppression, respectively, while maintaining a comparable full width at half maximum (FWHM). In the in vivo study, it achieved the highest contrast resolution among the comparison methods, yielding 2% and 3% improvements in generalized contrast-to-noise ratio (gCNR), respectively. Conclusions: These results demonstrate that the proposed deep learning-based beamforming framework can significantly enhance SPWI image quality without compromising frame rate, indicating promising potential for high-speed, high-resolution clinical applications such as cardiac assessment and real-time interventional guidance. Full article

Monitoring land use and land cover (LULC) transformations is essential for understanding socio-ecological dynamics. This study assesses structural shifts in Romania’s landscapes between 1990 and 2018 by integrating algorithmic complexity, fractal analysis, and Grey-Level Co-occurrence Matrix (GLCM) texture analysis. Multi-year maps were used to compute Kolmogorov complexity, fractal measures, and 15 GLCM metrics. The measures were compiled into a unified matrix, and temporal trajectories were explored with principal component analysis and k-means clustering to identify inflection points. Informational complexity and Higuchi 2D decline over time, while homogeneity and angular second moment rise, indicating greater local uniformity. A structural transition around 2006 separates an early heterogeneous regime from a more ordered state; 2012 appears as a turning point when several indices reach extreme values. Strong correlations between fractal and texture measures imply that geometric and radiometric complexity co-evolve, whereas large-scale fractal dimensions remain nearly stable. The multi-index approach provides a replicable framework for identifying critical transitions in LULC. It can support landscape monitoring, and future work should integrate finer temporal data and socio-economic drivers. Full article

Offshore wind turbines are subjected to environmental loads such as wind and ocean waves throughout their entire service lives. Saturated sandy soils experience liquefaction under cyclic shear stresses induced by earthquakes or strong wave actions, which can result in the tilting, settlement, or even overturning of structures. This study investigates the effect of fine content (FC) on the liquefaction resistance (CRR) of saturated sandy soils with different density states. Sandy soils with varying FC values are examined under three scenarios: (1) constant relative density; (2) constant void ratio; and (3) constant skeleton void ratio. A series of undrained cyclic triaxial tests are conducted on sandy soils with different FC and density states (D_r, e, and e_sk). The results indicate that an increase in FC leads to a decrease in CRR at constant D_r or e, whereas CRR at constant e_sk increases with increasing FC. No clear correlation is observed between D_r, e, or e_sk and CRR for saturated sandy soils with varying FC. Since e_sk does not account for the effect of fine particles on the contact state of skeleton particles, the equivalent skeleton void ratio (e_sk^*) is introduced to describe the particle contact state of sandy soils with different fine contents (FCs), considering the degree of fine particle participation. In addition, the test data reveal that the CRR of sandy soils with different FC and density states decreases with increasing e_sk^*, and a power relationship between the reduction in CRR and the increase in e_sk^* is established. This finding indicates that e_sk^*, which considers the proportion of fines contributing to the load-sustaining framework, serves as a reliable index for evaluating the CRR of various sandy soils. We find that grain shape plays a significant role in influencing CRR, and the overall CRR of sandy soils increases as the grain shape changes from spherical to angular, compared to the published test results for other sandy soils. Full article

Based on the extended Huygens–Fresnel principle and mode expansion theory, we derive the expression for the Coherence-Orbital Angular Momentum (COAM) matrix of twisted Gaussian Schell-model (TGSM) beams propagating through non-Kolmogorov turbulence. Using numerical simulations, we compare the evolution characteristics of the COAM matrix in free space and under non-Kolmogorov turbulence conditions. The study analyzes the variation patterns in the absolute values, real parts, and imaginary parts of the COAM matrix elements under different topological charges, and provides a detailed investigation of the influence of various beam parameters and turbulence parameters on these elements. The results show that by selecting appropriate parameters, the negative impact of turbulence on the correlation between orbital angular momentum (OAM) modes can be effectively mitigated. This work provides theoretical support for parameter selection and optimization in atmospheric optical communication systems. Full article

In PAC spectroscopy, hyperfine interactions of a radioactive probe nucleus with its surroundings are measured, providing information about the local atomic structure and dynamics at the probe site. In the so-called fast reorientation time regime for fluctuating nuclear quadrupole interactions (NQIs), the PAC signal is an exponentially decaying function, with decay constant λ depending on both the hyperfine interaction and dynamics. For a molecular system in solution, dynamics may originate from Brownian molecular tumbling (rotational diffusion) with rotational correlation time τ_c and from local dynamics at the probe site, occurring at a characteristic time scale τ_loc. The τ_c and the τ_loc cannot be discriminated in a single PAC spectrum; however, assuming that they scale differently with viscosity and temperature, a series of experiments in which these parameters are varied may allow for discrimination of τ_c and the τ_loc. Three models are presented for the effect of dynamics on the PAC signal: (1) the Stokes–Einstein–Debye model with linear scaling of λ with viscosity ξ; (2) a more general model presenting a power law scaling of λ with (ξ/ξ₀)ⁿ; and (3) a model that includes rotational and local dynamics leading to an expression for λ that scales with ξ/(ξ + c), where c is a constant that depends on temperature, molecular volume, and τ_loc. These models may serve as different approaches to analyze PAC data and their dependence on temperature and solvent viscosity in the fast reorientation time regime, and they can be applied to design experiments for optimal discrimination of global rotational diffusion and local dynamics at the probe site. Full article

Background: Seated shot put is a core Paralympic event in which lower-limb-impaired athletes generate throwing power primarily through the trunk and upper limbs. The configuration of the throwing frame may influence performance stability and biomechanics. This study aimed to evaluate the effect of two seated orientations on throwing performance, kinematics, and within-subject reliability in a Paralympic F55 athlete using markerless motion capture. Methods: A para-athlete F55-class (age: 37 years; body mass: 93 kg; height: 180 cm; training experience: 20 years) performed 20 throws (10 per seat position: perpendicular and 54.5° rotated). Kinematic data were recorded with an eight-camera, 250 Hz markerless motion capture system. Variables included throw distance, trial time, release angle, wrist acceleration and velocity, and torso angular velocities. Reliability was assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEM), coefficient of variation (CV%), Bland–Altman analysis, and ROC curve discrimination. Results: Throw distance did not differ significantly between positions (p = 0.1086), but trial duration was significantly shorter in the rotated position (p = 0.0114). Most kinematic variables showed poor-to-moderate reliability (ICC = −0.51 to 0.40). Bland–Altman and ROC analyses indicated stable performance measures but higher variability in torso motion, with torso rotation (AUC = 0.72) showing the strongest discriminative ability. Conclusions: Seated orientation minimally affected performance but influenced trunk kinematics and reliability, emphasizing the need for individualized biomechanical assessment in Paralympic shot put training. Full article