Search Results (109)

This paper proposes TWISS (TOPSIS + Wrapper Incremental Subset Selection), a novel hybrid feature selection framework designed for electromyographic (EMG) pattern recognition in upper-limb prosthetic control. TWISS integrates the multi-criteria decision-making method TOPSIS with a forward wrapper search strategy, enabling subject-specific feature optimization based on a ranking that combines filter metrics, including Chi-squared, ANOVA, and Mutual Information. Unlike conventional static feature sets, such as the Hudgins configuration (48 features: four per channel, 12 channels) or All Features (192 features: 16 per channel, 12 channels), TWISS dynamically adapts feature subsets to each subject, addressing inter-subject variability and classification robustness challenges in EMG systems. The proposed algorithm was evaluated on the publicly available Ninapro DB7 dataset, comprising both intact and transradial amputee participants, and implemented in an open-source, fully reproducible environment. Two Google Colab tools were developed to support diverse workflows: one for end-to-end feature extraction and selection, and another for selection on precomputed feature sets. Experimental results demonstrated that TWISS achieved a median F1-macro score of 0.6614 with Logistic Regression, outperforming the All Features set (0.6536) and significantly surpassing the Hudgins set (0.5626) while reducing feature dimensionality. TWISS offers a scalable and computationally efficient solution for feature selection in biomedical signal processing and beyond, promoting the development of personalized, low-cost prosthetic control systems and other resource-constrained applications. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

Dynamic environments pose significant challenges for visual SLAM, including feature ambiguity, weak textures, and map inconsistencies caused by moving objects. We present a robust SLAM framework integrating image enhancement, a mixed-precision quantized feature detection network, semantic-driven dynamic feature filtering, and NeRF-based static scene reconstruction. The system reliably extracts features under challenging conditions, removes dynamic points using instance segmentation combined with polar geometric constraints, and reconstructs static scenes with enhanced structural fidelity. Extensive experiments on TUM RGB-D, BONN RGB-D, and a custom dataset demonstrate notable improvements in the RMSE, mean, median, and standard deviation. Compared with ORB-SLAM3, our method achieves an average RMSE reduction of 93.4%, demonstrating substantial improvement, and relative to other state-of-the-art dynamic SLAM systems, it improves the average RMSE by 49.6% on TUM and 23.1% on BONN, highlighting its high accuracy, robustness, and adaptability in complex and highly dynamic environments. Full article

Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise reduction method based on 3D adaptive filtering. Our technique processes a stack of holograms, each with an uncorrelated speckle pattern, using an adapted 3D Frost filter. Unlike conventional filtering techniques, our approach exploits statistical adaptivity to enhance noise suppression while preserving fine image details in the reconstructed holograms. Both numerical simulations and optical experiments confirm that our 3D filtering technique significantly enhances reconstruction quality. Specifically, it reduces the normalized standard deviation by up to 40% and improves the structural similarity index by up to 60% compared to classical 2D, 3D median, BM3D, and BM4D filters. Optical experiments validate the method’s effectiveness in practical digital holography scenarios by local and global image quality estimation metrics. These results highlight adaptive 3D filtering as a promising approach for mitigating speckle noise while maintaining structural integrity in digital holography reconstructions. Full article

An unsupervised anomaly-detection framework is proposed and field validated for thrust-bearing monitoring in the impulse turbine of a shoreline oscillating water-column (OWC) wave energy converter (WEC) off Jeju Island, Korea. Operational monitoring is constrained by nonstationary sea states, scarce fault labels, and low-rate supervisory logging at 20 Hz. To address these conditions, a 24 h period of normal operation was median-filtered to suppress outliers, and six physically motivated time-domain features were computed from triaxial vibration at 10 s intervals: absolute mean; standard deviation (STD); root mean square (RMS); skewness; shape factor (SF); and crest factor (CF, peak divided by RMS). A feature-based autoencoder was trained to reconstruct the feature vectors, and reconstruction error was evaluated with an adaptive threshold derived from the moving mean and moving standard deviation to accommodate baseline drift. Performance was assessed on a 2 h test segment that includes a 40 min simulated fault window created by doubling the triaxial vibration amplitudes prior to preprocessing and feature extraction. The detector achieved accuracy of 0.99, precision of 1.00, recall of 0.98, and F1 score of 0.99, with no false positives and five false negatives. These results indicate dependable detection at low sampling rates with modest computational cost. The chosen feature set provides physical interpretability under the 20 Hz constraint, and denoising stabilizes indicators against marine transients, supporting applicability in operational settings. Limitations associated with simulated faults are acknowledged. Future work will incorporate long-term field observations with verified fault progressions, cross-site validation, and integration with digital-twin-enabled maintenance. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Traditional median filtering with a fixed window easily leads to edge blurring and adaptive median filtering requires manual presetting of the maximum window parameter and has insufficient retention of details when dealing with high-density salt-and-pepper noise. Aiming at these problems, this paper proposes a hybrid decision-making adaptive median filtering algorithm with dual-window detection in collaboration with particle swarm optimization (PSO). The algorithm quickly locates suspected noise points through a 3 × 3 small window and enhances noise identification accuracy by using a PSO dynamically optimized 5–35-pixel large window. Meanwhile, a hybrid decision-making mechanism based on local statistical properties was introduced to dynamically select median filtering, weighted average based on spatial distance, or pixel preservation strategy to balance noise suppression and detail preservation, and the PSO algorithm was used to automatically find the optimal parameters of the large window’s size to avoid the manual parameter-tuning process. Experiments were conducted on standard grayscale and color images and compared with four traditional methods and two more advanced methods. The experiments showed that the algorithm improved the peak signal-to-noise ratio (PSNR) value by 2–4 dB and the structural similarity index measure (SSIM) metric by 0.05–0.2 under high salt-and-pepper noise density compared with the traditional methods, which effectively improved the contradiction between noise suppression and detail retention in traditional filtering algorithms and provided a highly efficient and intelligent solution for image denoising in high-noise scenarios. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry. Full article

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks. Full article

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article

Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article

Random noise degrades the quality and reduces the interpretability of Ground Penetrating Radar (GPR) data. The Block Matching Three Dimension (BM3D) algorithm is effective at suppressing Gaussian noise, but ineffective at handling salt-and-pepper noise. On the other hand, the Improved Adaptive Median (IAM) filter is suitable for eliminating salt-and-pepper noise, but performs poorly against Gaussian noise. In this paper, we introduce and implement JBI, a joint denoising algorithm that integrates both BM3D and improved adaptive median filtering, exploiting the advantages of both algorithms to effectively remove both Gaussian and salt-and-pepper noise from GPR data. Applying the proposed joint filter to both synthetic and real field GPR data, infested with various proportions of different noise types, shows that the proposed joint denoising algorithm yields significantly better results than these two filters when used separately, and better than other commonly used denoising filters. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

In the evolving field of mobile robotics, enhancing localization robustness in dynamic environments remains a critical challenge, particularly for ROS 2-based systems where sensor fusion plays a pivotal role. This study evaluates an AI-driven approach to dynamically adjust covariance parameters for improved pose estimation in a differential-drive mobile robot. A regression model was integrated into the robot_localization package to adapt the Extended Kalman Filter (EKF) covariance in real time, with experiments conducted in a controlled indoor setting over runs comparing AI-enabled dynamic covariance prediction against a static covariance baseline across Static, Moderate, and Aggressive motion dynamics. The AI-enabled system achieved a Mean Absolute Error (MAE) of 0.0061 for pose estimation and reduced median yaw prediction errors to 0.0362 rad (static) and 0.0381 rad (moderate) with tighter interquartile ranges (0.0489 rad, 0.1069 rad) compared to the baseline (0.0222 rad, 0.1399 rad). Aggressive dynamics posed challenges, with errors up to 0.9491 rad due to data distribution bias and Random Forest model constraints. Enhanced dataset augmentation, LSTM modeling, and online learning are proposed to address these limitations. Datalogging enabled iterative re-training, supporting scalable state estimation with future focus on online learning. Full article