Search Results (13)

Marine oil spills pose a persistent threat to marine ecosystems and coastal economies, and their rapid and unpredictable spread requires timely and reliable monitoring. In X-band marine radar images, oil slicks usually appear as low-contrast dark targets embedded in heterogeneous sea clutter, making accurate segmentation particularly challenging. To address this problem, this study proposes a training-free two-stage oil slick detection framework that combines an improved Slick Boundary Ratio (SBR) feature with an improved Whale Optimization Algorithm (WOA). First, the improved SBR feature is used to extract the oil slick region of interest (ROI). Then, the improved WOA is employed to determine the global threshold for oil slick segmentation. Experimental results show that the proposed method achieves accurate and spatially coherent oil slick segmentation in complex radar backgrounds, with an Accuracy of 99.36%, a Precision of 85.73%, a Recall of 84.42%, an F1-score of 85.07%, and an Intersection over Union (IoU) of 74.01%. These results indicate that the proposed framework can effectively suppress false positives while maintaining strong detection sensitivity, thereby improving segmentation robustness in low-contrast marine radar scenes. Owing to its training-free design, the proposed method shows potential for shipborne and coastal oil spill monitoring applications. Full article

Reliable traffic sign detection is essential for the safety of autonomous driving systems. However, manually annotating large-scale datasets for this task is resource-intensive, making semi-supervised learning (SSL) a vital alternative. Despite their potential, current SSL methods often struggle with unreliable pseudo-label filtering and limited detection accuracy. To address these limitations, we propose a novel framework integrating a Dual Confidence Fusion (DC-Fusion) module and a Structured Block-Regularized Neck (SBR-Neck). The former improves pseudo-label reliability by combining classification and localization confidence scores, while the latter optimizes feature representation through multi-scale fusion and block-wise regularization. To preserve high-frequency spatial details, SBR-Neck incorporates Spatial-Context-Aware Upsampling (SCA-Upsampling), which utilizes multi-granularity feature decomposition. Experimental results on a proprietary traffic sign dataset demonstrate that our method achieves ${\mathrm{mAP}}_{50}$ scores of 10.4%, 17.8%, 23.7%, and 32.1% using 1%, 2%, 5%, and 10% labeled data, respectively. These results surpass the “Efficient Teacher” baseline by margins ranging from 3.07% to 11%, confirming the framework’s ability to provide robust detection in complex traffic scenarios. Full article

Winery wastewater contains recalcitrant pollutants, such as phenolic compounds, which can hinder biological treatment processes. While monitoring these systems is essential to prevent treatment failure, quantifying recalcitrant compounds through conventional methods can be time-consuming and costly due to complex analytical procedures and chemical disposal. In this study, machine learning (ML) was used to predict polyphenol concentration after the biological treatment of winery wastewater using a sequencing batch reactor (SBR). ML models, including ElasticNet (ENet), Multi-Layer Perceptron Regressor (MLPR), and Support Vector Regressor (SVR), were developed and tested using a small, high-dimensional dataset and leave-one-out cross-validation (LOOCV). Feature selection and hyperparameter tuning were applied to improve model performance. After optimization, the SVR model achieved the best performance, with MAE, MAPE, and R² of 0.88 mg/L, 9.3%, and 0.75, respectively. Full article

River ecosystems play a crucial role in the global water cycle and regional ecological security, yet they face severe challenges under the dual pressures of human activities and climate change. To systematically assess the spatiotemporal characteristics and driving mechanisms of river ecological impacts, this study proposes a modular and transferable method, which is Quantitative Analysis of Spatiotemporal Variations in Ecological Water-Supplementation Benefits of Rivers Based on Remote Sensing (QASViewSBR). Taking the Yongding River (Beijing section) from 2016 to 2023 as a case study, this research integrates multi-source remote sensing and ground monitoring data to extract river water bodies using an improved Normalized Difference Water Index and Vertical–Horizontal polarization characteristics. The Seasonal and Trend decomposition using Loess (STL) method was employed for time-series trend decomposition, Pearson correlation analysis was applied to identify driving factors of area changes, and the Pelt algorithm was used to quantify the response range of riparian vegetation to changes of river water levels. An integrated analytical framework of “dynamic monitoring—time series analysis—driving factor identification—spatial heterogeneity assessment” was established, enabling standardized end-to-end analysis from data acquisition to evaluation. The results indicate that the river water area in the basin increased significantly after 2019, with enhanced seasonal fluctuations. Under the ecological water supplementation policy, the “human-initiated, natural-response” mechanism was clearly observed, and the ecological responses along both riverbanks exhibited significant spatial heterogeneity due to variations in surface features and topography. QASViewSBR exhibits good universality and transferability, providing methodological support for ecological restoration and management in different river basins. Full article

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (R_p²) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (R_p² of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture. 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

The study addresses the critical issue of accurately predicting ammonia nitrogen (NH₃-N) concentration in a sequencing batch reactor (SBR) system, achieving reduced consumption through automatic control technology. NH₃-N concentration serves as a key indicator of treatment efficiency and environmental impact; however, its complex dynamics and the scarcity of measurements pose significant challenges for accurate prediction. To tackle this problem, an innovative Transformer-long short-term memory (Transformer-LSTM) network model was proposed, which effectively integrates the strengths of both Transformer and LSTM architectures. The Transformer component excels at capturing long-range dependencies, while the LSTM component is adept at modeling sequential patterns. The innovation of the proposed methodology resides in the incorporation of dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP) as input variables, along with their respective rate of change and cumulative value. This strategic selection of input features enhances the traditional utilization of water quality indicators and offers a more comprehensive dataset for prediction, ultimately improving model accuracy and reliability. Experimental validation on NH₃-N datasets from the SBR system reveals that the proposed model significantly outperforms existing advanced methods in terms of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Furthermore, by integrating real-time sensor data with the Transformer-LSTM network and automatic control, substantial improvements in water treatment processes were achieved, resulting in a 26.9% reduction in energy or time consumption compared with traditional fixed processing cycles. This methodology provides an accurate and reliable tool for predicting NH₃-N concentrations, contributing significantly to the sustainability of water treatment and ensuring compliance with emission standards. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes. Full article

The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. Full article

The characterising sound required for the Acoustic Scene Classification (ASC) system is contained in the ambient signal. However, in practice, this is often distorted by e.g., foreground speech of the speakers in the surroundings. Previously, based on the iVector framework, we proposed different strategies to improve the classification accuracy when foreground speech is present. In this paper, we extend these methods to deep-learning (DL)-based ASC systems, for improving foreground speech robustness. ResNet models are proposed as the baseline, in combination with multi-condition training at different signal-to-background ratios (SBRs). For further robustness, we first investigate the noise-floor-based Mel-FilterBank Energies (NF-MFBE) as the input feature of the ResNet model. Next, speech presence information is incorporated within the ASC framework obtained from a speech enhancement (SE) system. As the speech presence information is time-frequency specific, it allows the network to learn to distinguish better between background signal regions and foreground speech. While the proposed modifications improve the performance of ASC systems when foreground speech is dominant, in scenarios with low-level or absent foreground speech, performance is slightly worse. Therefore, as a last consideration, ensemble methods are introduced, to integrate classification scores from different models in a weighted manner. The experimental study systematically validates the contribution of each proposed modification and, for the final system, it is shown that with the proposed input features and meta-learner, the classification accuracy is improved in all tested SBRs. Especially for SBRs of 20 dB, absolute improvements of up to 9% can be obtained. Full article

Scenario identification plays an important role in assisting unmanned aerial vehicle (UAV) cognitive communications. Based on the scenario-dependent channel characteristics, a support vector machine (SVM)-based air-to-ground (A2G) scenario identification model is proposed. In the proposed model, the height of the UAV is also used as a feature to improve the identification accuracy. On the basis, an improved scenario identification method is developed including dataset acquisition, identification model training, and height-integrated model feedback. The shooting and bouncing ray/image (SBR/IM) method is used to obtain the datasets of channel characteristics, i.e., root-mean-square delay spread (RMS-DS), K factor, and angle spread (AS) under five typical scenarios: over-sea, suburban, urban, dense urban and high-rise urban. SBR/IM is a symmetry-based ray tracing (RT) simulation method. After the identification model is trained, a height-integrated feedback scheme is used to increase the identification performance. The simulation results show that the identification accuracy of improved method is about 14% higher than the method without height feature, which reaches nearly 100% under the over-sea and suburban and over 80% in urban, dense urban, and high-rise urban. Full article

As one of the core components of MEMS (i.e., micro-electro-mechanical systems), thin-film piezoelectric-on-silicon (TPoS) resonators experienced a blooming development in the past decades due to unique features such as a remarkable capability of integration for attractive applications of system-on-chip integrated timing references. However, the parasitic capacitive feedthrough poses a great challenge to electrical detection of resonance in a microscale silicon-based mechanical resonator. Herein, a fully-differential configuration of a TPoS MEMS resonator based on a novel structural design of dual interdigital electrodes is proposed to eliminate the negative effect of feedthrough. The fundamental principle of feedthrough suppression was comprehensively investigated by using FEA (i.e., finite-element analysis) modeling and electrical measurements of fabricated devices. It was shown that with the help of fully-differential configuration, the key parameter of SBR (i.e., signal-to-background ratio) was significantly enhanced by greatly suppressing the in-phase signal. The S-parameter measurement results further verified the effectiveness of this novel feedthrough suppression strategy, and the insertion loss and SBR of proposed TPoS resonators were improved to 4.27 dB and 42.47 dB, respectively. Full article

Synergistic effects of different fillers are widely utilized in polymer technology. The combination of various types of fillers is used to improve various properties of polymer composites. In this paper, a synergistic effect of flame retardants was tested to improve the performance of ceramizable composites. The composites were based of styrene-butadiene rubber (SBR) used as polymer matrix. Three different types of flame retardants were tested for synergistic effect: Mica (phlogopite) high aspect-ratio platelets, along with low softening point temperature glass frit (featuring ceramization effect), and melamine cyanurate, a commonly used flame retardant promoting carbonaceous char. In order to characterize the properties of the composites, combustibility, thermal stability, viscoelastic properties, micromorphology, and mechanical properties were tested before and after ceramization. The results obtained show that the synergistic effect of ceramization promoting fillers and melamine cyanurate was especially visible with respect to the flame retardant properties resulting in a significant improvement of fire resistance of the composites. Full article