Search Results (874)

To address the challenges of small-sample rock fine classification—such as overfitting caused by limited sample size and the increased complexity resulting from high inter-class similarity—this study proposes a Stacking ensemble method tailored for small-sample rock image classification. Using a dataset of seven rock categories provided by the BdRace platform, 38 features were extracted across three dimensions—color, texture, and grain size—through grayscale thresholding, HSV color space analysis, gray-level co-occurrence matrix computation, and morphological analysis. The interrelationships among features were evaluated using Spearman correlation analysis and hierarchical clustering, while a voting-based fusion strategy integrated Lasso regularization, gray correlation analysis, and variance filtering for feature dimensionality reduction. The Whale Optimization Algorithm (WOA) was employed to perform global optimization on the base learners, including Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NBM), and Support Vector Machine (SVM), with Logistic Regression serving as the meta-classifier to construct the final Stacking ensemble model. Experimental results demonstrate that the Stacking method achieves an average classification accuracy of 85.41%, with the highest accuracy for black coal identification (97.16%). Compared to the single models RF, KNN, NBM, and SVM, it improves accuracy by 7.27%, 8.64%, 6.79%, and 6.94%, respectively. Evidently, the Stacking model integrates the strengths of individual models, significantly enhancing recognition accuracy. This research not only improves rock identification accuracy and reduces exploration costs but also advances the intelligent transformation of geological exploration, demonstrating considerable engineering application value. Full article

The separation of oil-in-water emulsions from industrial wastewater remains a significant challenge, particularly under saline conditions. This study evaluated bacterial cellulose (BC) membranes from Komagataeibacter hansenii for filtering synthetic effluents with high oil content (ES1) and saline oil-in-water emulsions (ES2). FTIR confirmed the incorporation of lipophilic compounds into the BC matrix. Crystallinity decreased from 78.8% to 40% following ES1 filtration, while a new peak at 2θ ≈ 31.8° appeared in ES2, indicating salt deposition. TGA revealed increased mass loss in the oil-saturated membrane (BCO), whereas the saline-exposed membrane (BCOS) exhibited higher thermal stability. SEM showed fiber compaction and localized deposition of oil and salt, corroborated by EDS, which identified Na, Cl, Ca, and elevated oxygen levels. Mechanical testing indicated that oil acted as a plasticizer, increasing the elongation at break of BCO, while salt crystallization enhanced BCOS stiffness. The membranes removed up to 98% of organic load (BOD and COD), 69% of oils and greases, and reduced turbidity and apparent color by 92%. Partial salt retention (~23%) and a significant decrease in dissolved oxygen were also observed. These results demonstrate the potential of BC membranes as an effective and sustainable solution for the treatment of complex oily and saline wastewater. Full article

Environmental factors like light, photoperiod, temperature, and water are vital for crop growth, and even slight deviations from their optimal ranges can cause seedling stress and reduce yield. Therefore, this study aimed to quantify seedling stress symptoms using image features analysis under varying light, photoperiod, temperature, and water conditions. Seedlings were grown under controlled low, normal, and high environmental conditions. Light intensity at 50 µmol m⁻² s⁻¹ (low), 250 µmol m⁻² s⁻¹ (normal), and 450 µmol m⁻² s⁻¹ (high), photoperiod cycles, 8/16 h (day/night) (low), 10/14 h (day/night) (normal), and 16/8 h (day/night) (high) day/night, temperature at 20 °C (low), 25 °C (normal), and 30 °C (high), and water availability at 1 L per day (optimal), 1 L every two days (moderate stress), and 1 L every three days (severe stress) were applied for 15 days. Commercial low-cost RGB, thermal, and depth sensors were used to collect data every day. A total of 1080 RGB images, which were pre-processed with histogram equalization and filters (Median and Gaussian), were used for noise reduction to minimize illumination effects. Morphological, color, and texture features were then analyzed using ANOVA (p < 0.05) to assess treatment effects. The result shows that the maximum canopy area for tomato was 115,226 pixels, while lettuce’s maximum plant height was 9.28 cm. However, 450 µmol m⁻² s⁻¹ light intensity caused increased surface roughness, indicating stress-induced morphological alteration. The analysis of Combined Stress Index (CSI) values indicated that the highest stress levels were 50% for pepper, 55% for tomato, 62% for cucumber, 55% for watermelon, 50% for lettuce, and 50% for pak choi. The findings showed that image-based stress detection enables precise environmental control and improves early-stage crop management. Full article

Monitoring the condition of ropes aboard historic ships is crucial for both safety and preservation. This study introduces a portable, low-cost imaging device designed for deployment on the Italian training ship Amerigo Vespucci, enabling autonomous acquisition of high-quality images of onboard ropes. The device, built around a Raspberry Pi 3 and enclosed in a 3D-printed protective case, allows the crew to label the state of ropes using colored markers and capture standardized visual data. From 207 collected recordings, a curated and balanced dataset was created through frame extraction, blur filtering using Laplacian variance, and image preprocessing. This dataset was used to train and evaluate convolutional neural networks (CNNs) for binary classification of rope conditions. Both custom CNN architectures and pre-trained models (MobileNetV2 and EfficientNetB0) were tested. Results show that color images outperform grayscale in all cases, and that EfficientNetB0 achieved the best performance, with 97.74% accuracy and an F1-score of 0.9768. The study also compares model sizes and inference times, confirming the feasibility of real-time deployment on embedded hardware. These findings support the integration of deep learning techniques into field-deployable inspection tools for preventive maintenance in maritime environments. Full article

There has been an increasing demand for high-resolution image sensing technologies in recent years due to their diverse and advanced optical applications. With recent advances in nanofabrication technologies, this can be achieved through the realization of high-density pixels. However, the development of high-density and miniaturized pixels introduces challenges to the conventional color filters, which generally transmit and absorb different spectral components of light. A significant portion of the incident light is inherently lost using conventional color filters. Moreover, as the pixel size is shrunk, optical losses appear to be substantial. To address these fundamental limitations, a novel nanophotonic optical router is proposed in this work. Our router utilizes a single-layer, all-dielectric metasurface as a spectral router. The metasurface is designed through an inverse design approach that exploits adjoint sensitivity analysis. A novel figure of merit is developed and incorporated in the inverse design process, enabling the metasurface design to effectively sort and route the incoming light into four targeted channels, each corresponding to a distinct spectral component—red, green, blue, and near-infrared. We demonstrate that the proposed quad-spectral metasurface router, having a compact footprint of $2 \mathsf{\mu }\mathrm{m}\times 2 \mathsf{\mu }\mathrm{m}$, achieves an average optical efficiency of approximately 39% across the broad spectral range, i.e., 400–850 nm, with each spectral channel exceeding an efficiency of 25%. This surpasses the maximum efficiency attainable by the conventional four-channel color filters. Our proposed quad-spectral metasurface router offers a wide range of applications in low-light imaging, image fusion, computational photography, and computer vision. In addition, this work highlights the applicability of an adjoint-based inverse design approach to accelerate the development of compact, efficient, and high-performance nanophotonic devices for the next generation of imaging and sensing systems. Full article

Switchable microlasers with multicolor output and high spectral purity are of crucial importance for various photonic devices. However, switchable multicolor lasing usually operates in multimode, which largely restricts its practical applications due to the lack of an effective mode selection mechanism. Here, switchable single-mode lasing was successfully achieved in coupled microfiber cavities, in which each microfiber served as both WGM resonator and mode filter for another microfiber. The unique mode selection mechanism is demonstrated experimentally and theoretically in the coupled microfibers. Furthermore, the color of single-mode lasing is tunable at will via the doping of microfibers with different active materials. Our work might provide a platform for building switchable multicolor lasers and gaining further insights into photonic integration. Full article

This study aims to improve the accuracy and efficiency of apple defect detection under complex lighting conditions. A novel approach is proposed that integrates filtered imaging with color space reconstruction, utilizing YOLOv12 as the detection framework. “Red Fuji” apples were selected, and an imaging platform featuring adjustable illumination and RGB filters was established. Following pre-experimental optimization of imaging conditions, a dataset comprising 1600 images was constructed. Conversions to RGB, HSI, and LAB color spaces were performed, and YOLOv12 served as the baseline model for ablation experiments. Detection performance was assessed using Precision, Recall, mAP, and FPS metrics. Results indicate that the green filter under 4500 K illumination combined with RGB color space conversion yields optimal performance, achieving an mAP50–95 of 83.1% and a processing speed of 15.15 FPS. This study highlights the impact of filter–color space combinations on detection outcomes, offering an effective solution for apple defect identification and serving as a reference for industrial inspection applications. Full article

To increase information accuracy when using ultrawide-band (UWB) localization for robotic dogs, we introduce a switching method for a Student’s t-distributed extended Kalman filter (EKF) that achieves UWB localization under colored measurement noise (CMN). First, a distributed UWB localization framework under CMN is designed, which can reduce the impact of CMN caused by carrier jitter on positioning accuracy. Then, a Student’s t-distributed EKF under CMN with a switch factor is proposed, which effectively improves the adaptability of the algorithm through adaptive selection of colored factors. Finally, experimental validation demonstrates the efficacy and high performance of the proposed method for two practical scenarios. Full article

Trajectory prediction in the Internet of Vehicles (IoV) is crucial for enhancing road safety and traffic efficiency; however, existing methods often fail to address the challenges of colored noise in GPS data and long-term dependency modeling. To overcome these limitations, this paper proposes AttSCNs, a probabilistic hybrid framework integrating stochastic configuration networks (SCNs) with an attention-based encoder to model trajectories while quantifying prediction uncertainty. The model leverages SCNs’ stochastic neurons for adaptive noise filtering, attention mechanisms for dependency learning, and Bayesian hyperparameter optimization to infer robust configurations as a posterior distribution. Experimental results on real-world GPS datasets (10,000+ urban/highway trajectories) demonstrate that AttSCNs significantly outperform conventional approaches, reducing RMSE by 36.51% compared to traditional SCNs and lowering MAE by 97.8% compared to Kalman filter baselines. Moreover, compared to the LSTM model, AttSCNs achieve a 52.5% reduction in RMSE and a 68.5% reduction in MAE, with real-time inference speed. These advancements position AttSCNs as a robust, noise-resistant solution for IoV applications, offering superior performance in autonomous driving and smart city systems. Full article

Adaptive filtering algorithms often suffer from biased parameter estimation and performance degradation in the presence of colored input noise and impulsive observation noise, both of which are common in practical sensor and communication systems. Existing bias-compensated least mean square (LMS) algorithms generally assume white Gaussian input noise, thereby limiting their applicability in real-world scenarios. This paper introduces a robust convex combination bias-compensated LMS (CC-BC-LMS) algorithm designed to address both colored Gaussian input noise and impulsive observation noise. The proposed algorithm achieves bias compensation through robust estimation of the input noise autocorrelation matrix and employs a modified Huber function to mitigate the influence of impulsive noise. A convex combination of fast and slow adaptive filters enables variable step-size adaptation, effectively balancing rapid convergence and low steady-state error. Extensive simulation results demonstrate that the proposed CC-BC-LMS algorithm provides substantial improvements in normalized mean square deviation (NMSD), surpassing state-of-the-art bias-compensated and robust adaptive filtering techniques by $4.48$ dB to $11.4$ dB under various noise conditions. These results confirm the effectiveness of the proposed approach for reliable adaptive filtering in challenging noisy environments. Full article

Although GPUs are widely used in Computer-Generated Holography (CGH), their specific application to concrete problems such as occlusion or speckle filtering through temporal multiplexing is not yet standardized and has not been fully explored. This work aims to optimize the software architecture by taking the GPU architecture into account in a novel way for these particular tasks. We present an optimized algorithm for CGH computation that provides a joint solution to the problems of speckle noise and occlusion. The workflow includes the generation and illumination of a 3D scene, the calculation of the CGH including color, occlusion, and temporal speckle-noise filtering, followed by scene reconstruction through both simulation and experimental methods. The research focuses on implementing a temporal multiplexing technique that simultaneously performs speckle denoising and occlusion culling for point clouds, evaluating two types of occlusion that differ in whether the occlusion effect dominates over the depth effect in a scene stored in a CGH, while leveraging the parallel processing capabilities of GPUs to achieve a more immersive and high-quality visual experience. To this end, the total computational cost associated with generating color and occlusion CGHs is evaluated, quantifying the relative contribution of each factor. The results indicate that, under strict occlusion conditions, temporal multiplexing filtering does not significantly impact the overall computational cost of CGH calculation. Full article

Medical endoscopic video processing requires real-time execution of color component acquisition, color filter array (CFA) demosaicing, and high dynamic range (HDR) compression under low-light conditions, while adhering to strict thermal constraints within the surgical handpiece. Traditional hardware-aware neural architecture search (NAS) relies on fixed hardware design spaces, making it difficult to balance accuracy, power consumption, and real-time performance. A collaborative “power-accuracy” optimization method is proposed for hardware-aware NAS. Firstly, we proposed a novel hardware modeling framework by abstracting FPGA heterogeneous resources into unified cell units and establishing a power–temperature closed-loop model to ensure that the handpiece surface temperature does not exceed clinical thresholds. In this framework, we constrained the interstage latency balance in pipelines to avoid routing congestion and frequency degradation caused by deep pipelines. Then, we optimized the NAS strategy by using pipeline blocks and combined with a hardware efficiency reward function. Finally, color component acquisition, CFA demosaicing, dynamic range compression, dynamic precision quantization, and streaming architecture are integrated into our framework. Experiments demonstrate that the proposed method achieves 2.8 W power consumption at 47 °C on a Xilinx ZCU102 platform, with a 54% improvement in throughput (vs. hardware-aware NAS), providing an engineer-ready lightweight network for medical edge devices such as endoscopes. Full article

Graphite nodularity is a key indicator for evaluating the microstructure quality of ductile iron and plays a crucial role in ensuring product quality and enhancing manufacturing efficiency. Existing research often only focuses on a single type of feature and fails to utilize multi-source information in a coordinated manner. Single-feature methods are difficult to comprehensively capture microstructures, which limits the accuracy and robustness of the model. This study proposes a hybrid estimation model for the graphite nodularity of ductile cast iron based on multi-source feature extraction. A comprehensive feature engineering pipeline was established, incorporating geometric, color, and texture features extracted via Hue-Saturation-Value color space (HSV) histograms, gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and multi-scale Gabor filters. Dimensionality reduction was performed using Principal Component Analysis (PCA) to mitigate redundancy. An improved watershed algorithm combined with intelligent filtering was used for accurate particle segmentation. Several machine learning algorithms, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), are applied to estimate graphite nodularity based on geometric features (GFs) and feature extraction. Experimental results demonstrate that the CatBoost model trained on fused features achieves high estimation accuracy and stability for geometric parameters, with R-squared (R²) exceeding 0.98. Furthermore, introducing geometric features into the fusion set enhances model generalization and suppresses overfitting. This framework offers an efficient and robust approach for intelligent analysis of metallographic images and provides valuable support for automated quality assessment in casting production. Full article

Color accuracy is both important and elusive in cultural heritage imaging. An established method for improving color accuracy is dual-RGB imaging, where RGB images of an object are captured sequentially under two different conditions and then combined. As part of an initiative to increase accessibility to color-accurate imaging, the use of lighting filters with the dual-RGB method is investigated. Gel lighting filters are low-cost and can be directly integrated into an imaging workflow by placing them in front of the existing light sources. This research found that color accuracy can be increased by using lighting filters, but it can also be decreased by a poor selection of filter combinations. The identity of the best-performing filters is highly dependent on the light source and can be affected by the pixels selected to represent the color target. Current simulation approaches are insufficient to predict which filters will increase color accuracy. While lighting filters are a promising method for accessible multispectral imaging, their practical implementation is complex and requires further research and adjustments to the method. Full article

Unmanned aerial vehicles (UAVs) have become a tool for solving numerous practical tasks. UAV sensors provide images and videos for on-line or off-line data processing for object localization, classification, and tracking due to the use of trained convolutional neural networks (CNNs) and artificial intelligence. However, quality of images acquired by UAV-based sensors is not always perfect due to many factors. One of them could be noise arising because of several reasons. Its presence, especially if noise is intensive, can make significantly worse the performance characteristics of CNN-based techniques of object localization and classification. We analyze such degradation for a set of eleven modern CNNs for additive white Gaussian noise model and study when (for what noise intensity and for what CNN) the performance reduction becomes essential and, thus, special means to improve it become desired. Representatives of two most popular families, namely the block matching 3-dimensional (BM3D) filter and DRUNet denoiser, are employed to enhance images under condition of a priori known noise properties. It is shown that, due to preliminary denoising, the CNN performance characteristics can be significantly improved up to almost the same level as for the noise-free images without CNN retraining. Performance is analyzed using several criteria typical for image denoising, object localization and classification. Examples of object localization and classification are presented demonstrating possible object missing due to noise. Computational efficiency is also taken into account. Using a large set of test data, it is demonstrated that: (1) the best results are usually provided for SSD Mobilenet V2 and VGG16 networks; (2) the performance characteristics for cases of applying BM3D filter and DRUNet denoiser are similar but the use of DRUNet is preferable since it provides slightly better results. Full article