Search Results (110)

In marine seismic surveys, the indistinguishability of subsurface boundaries caused by the superimposition of the acoustic signals reflected from it, particularly at specific frequency ranges characterized by strong spectral interference, reduces the resolution of the seismic record. We processed sub-bottom profiler data, acquired using a Bubble Pulser (nominal central frequency: ~400 Hz; effective bandwidth extending to ~1 kHz), (i) by extracting continuous wavelet transform (CWT) coefficients at the dominant energy scale to form the envelope and (ii) by applying Hilbert-based instantaneous frequency analysis to characterize medium-dependent spectral shifts. Envelope accuracy was benchmarked against four conventional filters using the sum of squared error (SSE) relative to a cubic-spline reference. CWT yielded the lowest SSE, outperforming low-pass 1 kHz and band-pass 400–1000 Hz; band-pass 400–650 Hz and low-pass 650 Hz were the least effective. Instantaneous-frequency trends differentiated rock, sand, and mud layers. Thus, compared to fixed-band filters, the scale-adaptive CWT envelope replicates raw energy more faithfully, while frequency attributes improve sediment classification. Low-pass filtering at 1000 Hz provides a more accurate representation of energy distribution than does bandpass filtering, particularly in the 400–650 Hz range. The integrated workflow—a robust, parameter-light alternative for high-resolution stratigraphic interpretation—enhances offshore engineering safety. Full article

We present a comparative analysis of three hybrid methodologies for transforming 3D kidney tumor segmentations of volumetric NIfTI data into highly accurate network representations. Exploiting the KiTS23 dataset, we evaluate edge-preserving reconstruction pipelines integrating anisotropic diffusion, multiscale Gaussian filtering and KNN-based network optimisation. Model 1 uses Gaussian smoothing with Marching Cubes, while Model 2 uses spline interpolation and Perona-Malik filtering for improved resolution. Model 3 extends this structure with normal sensitive vertex smoothing to preserve critical anatomical interfaces. Quantitative metrics (Dice score, HD₉₅) demonstrated the advantage of Model 3, which achieved a 22% reduction in the Hausdorff distance error rate compared to conventional methods while maintaining segmentation accuracy (Dice > 0.92). The proposed unsupervised pipeline bridges the gap between clinical interpretability and computational accuracy, providing a robust infrastructure for further applications in surgical planning and deep learning-based classification. Full article

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

Accurate estimation of tree diameter at breast height (DBH) from LiDAR point clouds is essential for forest inventory, biomass assessment, and ecological monitoring. This paper presents a perimeter-based DBH estimation framework that achieves competitive accuracy against geometric fitting methods across three datasets. The proposed approach partitions the trunk cross-section into angular sectors and employs Gaussian Mixture Models (GMMs) to identify representative boundary points in each sector, weighted by radial proximity and statistical confidence. To handle occlusion and partial scans, missing sectors are reconstructed using symmetry-aware proxy generation. The final perimeter is modeled via either convex hull or B-spline interpolation, from which DBH is derived. Extensive experiments were conducted on two public TreeScope datasets and a custom mobile LiDAR dataset. Compared to the Density-Based Clustering Ring Extraction (DBCRE) baseline, our method reduced RMSE by 22.7% on UCM-0523M (from 2.60 to 2.01 cm), 34.3% on VAT-0723M (from 3.50 to 2.30 cm), and 29.6% on the Custom Dataset (from 2.16 to 1.52 cm). Ablation studies confirmed the individual and synergistic contributions of GMM clustering, radial consistency filtering, and proxy synthesis. Overall, the method provides a flexible alternative that reduces dependence on strict geometric assumptions, offering improved DBH estimation performance with moderate occlusion and incomplete, uneven boundary coverage. Full article

Noise in Pulsar Timing Array (PTA) data is commonly modeled as a mixture of white and red noise components. While the former is related to the receivers, and easily characterized by three parameters (EFAC, EQUAD and ECORR), the latter arises from a mix of hard to model sources and, potentially, a stochastic gravitational wave background (GWB). Since their frequency ranges overlap, GWB search methods must model the non-GWB red noise component in PTA data explicitly, typically as a set of mutually independent Gaussian stationary processes having power-law power spectral densities. However, in searches for continuous wave (CW) signals from resolvable sources, the red noise is simply a component that must be filtered out, either explicitly or implicitly (via the definition of the matched filtering inner product). Due to the technical difficulties associated with irregular sampling, CW searches have generally used implicit filtering with the same power law model as GWB searches. This creates the data analysis burden of fitting the power-law parameters, which increase in number with the size of the PTA and hamper the scaling up of CW searches to large PTAs. Here, we present an explicit filtering approach that overcomes the technical issues associated with irregular sampling. The method uses adaptive splines, where the spline knots are included in the fitted model. Besides illustrating its application on real data, the effectiveness of this approach is investigated on synthetic data that has the same red noise characteristics as the NANOGrav 15-year dataset and contains a single non-evolving CW signal. Full article

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

This study presents a high-precision warehouse material level monitoring method that integrates millimeter-wave radar with 3D surface reconstruction to address the limitations of LiDAR, which is highly susceptible to dust and haze interference in complex storage environments. The proposed method employs Chirp-Z Transform (CZT) super-resolution processing to enhance spectral resolution and measurement accuracy. To improve grain surface identification, an anomalous signal correction method based on angle–range feature fusion is introduced, mitigating errors caused by weak reflections and multipath effects. The point cloud data acquired by the radar undergo denoising, smoothing, and enhancement using statistical filtering, Moving Least Squares (MLS) smoothing, and bicubic spline interpolation to ensure data continuity and accuracy. A Poisson Surface Reconstruction algorithm is then applied to generate a continuous 3D model of the grain heap. The vector triple product method is used to estimate grain volume. Experimental results show a reconstruction volume error within 3%, demonstrating the method’s accuracy, robustness, and adaptability. The reconstructed surface accurately represents grain heap geometry, making this approach well suited for real-time warehouse monitoring and providing reliable support for material balance and intelligent storage management. Full article

Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the accuracy of results. To address these limitations, this study presents the development of an open-source computational tool to optimize SPR biosensor characterization, implemented using MATLAB App Designer (Version R2024b). The tool enables the importation of experimental data, application of different smoothing methods, and integration of traditional and hybrid approaches to enhance accuracy in determining the resonance angle. The proposed tool offers several innovations, such as integration of both traditional and hybrid (angle vs wavelength) analysis modes, implementation of four advanced curve smoothing techniques, including Gaussian filter, Savitzky–Golay, smoothing splines, and EWMA, as well as a user-friendly graphical interface supporting real-time data visualization, experimental data import, and result export. Unlike conventional approaches, the hybrid framework enables multidimensional optimization of SPR parameters, resulting in greater accuracy and robustness in detecting resonance conditions. Experimental validation demonstrated a marked reduction in spectral noise and improved consistency in resonance angle detection across conditions. The results confirm the effectiveness and practical relevance of the tool, contributing to the advancement of SPR biosensor analysis. Full article

This study introduces an innovative GNSS-based monitoring system designed to evaluate deformation in mining headframes, effectively addressing the limitations of traditional methods, such as inadequate real-time capabilities and complex data processing requirements. The research was conducted at the Liuzhuang Mine in Anhui Province, China, where a monitoring network was established, consisting of one reference station and eight GNSS stations strategically positioned on sheave platforms and structural supports. Over a period of 66 days, high-frequency 3D deformation data were collected and processed using advanced methodologies, including cubic spline interpolation, generalized extreme studentized deviate (GESD) outlier removal, and Gaussian filtering. Spatiotemporal analysis, employing the “base state with amendments” model, indicated that 90% of the deformations (ΔX, ΔY, ΔH) were confined within ±8 mm, with more significant fluctuations observed near the sheave wheels due to mechanical stress. Correlation analysis identified the distance to the sheave wheel as the primary factor influencing horizontal deformation, with Pearson correlation coefficients exceeding 0.67, while vertical settlement remained stable. Risk thresholds, derived from statistical fluctuations, demonstrated that 99.2% of the data fell within safe limits during validation. In comparison to traditional approaches, the GNSS system delivers enhanced precision, real-time functionality, and a decreased field workload. This study presents a scalable framework for assessing headframe safety and guides the optimization of sensor placement in analogous mining settings. It is proposed that future integration with multi-source sensors, such as inertial navigation systems, will further augment monitoring robustness. Full article

Human motion recognition is crucial for applications like navigation, health monitoring, and smart healthcare, especially in weak GNSS scenarios. Current methods face challenges such as limited sensor diversity and inadequate feature extraction. This study proposes a CNN–Transformer–Attention framework with multimodal enhancement to address these challenges. We first designed a lightweight wearable system integrating synchronized accelerometer, gyroscope, and magnetometer modules at wrist, chest, and foot positions, enabling multi-dimensional biomechanical data acquisition. A hybrid preprocessing pipeline combining cubic spline interpolation, adaptive Kalman filtering, and spectral analysis was developed to extract discriminative spatiotemporal-frequency features. The core architecture employs parallel CNN pathways for local sensor feature extraction and Transformer-based attention layers to model global temporal dependencies across body positions. Experimental validation on 12 motion patterns demonstrated 98.21% classification accuracy, outperforming single-sensor configurations by 0.43–7.98% and surpassing conventional models (BP-Network, CNN, LSTM, Transformer, KNN) through effective cross-modal fusion. The framework also exhibits improved generalization with 3.2–8.7% better accuracy in cross-subject scenarios, providing a robust solution for human activity recognition and accurate positioning in challenging environments such as autonomous navigation and smart cities. Full article

The well-known uncertainties in subsurface velocity field definition call for the integration of all the available data, including vintage seismic profiles, which, despite typically being in raster or paper format, often contain velocities derived from stacking and associated interval velocities. This study aims to build a velocity model for the time-to-depth conversion of an interpreted seismic reflection profile by using the interval velocity reported on a vintage, paper-format seismic profile and contribute to improving the subsurface geological model of the Sicily Channel, Central Mediterranean. Spline interpolation is used for velocity model building of the shallower part (3.5 sec TWT) of the seismic profile CS89-01, derived from the stacking velocities of 31 Common Depth Point (CDP) gathers. This was followed by the Gaussian convolution operator and a data exclusion filter to improve the accuracy of the velocity model. The time-to-depth-converted seismic reflection profile is a regional cross-section that covers almost the entire Sicily Channel, crossing part of the northern margin of the African Plate, from Tunisia to eastern Sicily. This study provides a new subsurface velocity field that can be applied, or taken into account, to most parts of the Sicily Channel when structural and stratigraphic interpretations are carried out at specific sites and where uncertainties in subsurface geological model exist (e.g., in the present study, the volcanic bodies in the Pantelleria Graben and Lampedusa High). Full article

Sampling and reconstruction are indispensable processes in signal processing, and appropriate foundations are crucial for spline reconstruction models. Generalized cardinal polishing splines (GCP-splines) are a class of high-precision explicit splines with pretty properties. We propose the theory of GCP-splines for signal reconstruction and differential signaling to improve signal reconstruction accuracy. First, the elementary theory of the GCP-splines signal processing is proposed, and it mainly includes Fourier transformation and Z-transformation of the GCP-splines. Then, a GCP-splines filter that can be used to reconstruct the output signal from the input discrete signal is proposed. Next, we propose differential signal reconstruction based on the GCP-splines and the sampled original signal values to obtain information on the signal change rate. Numerical experiments demonstrate that the signal reconstruction based on the GCP-splines yields lower approximation errors and better performance than the linear interpolation filter and cardinal B-spline interpolation filter. Full article

The high-precision motion state analysis of space targets has important scientific value and application potential in the fields of geodynamics, geodesy, space collision warning and avoidance, and the capture, recovery, and removal of space debris. With the increasing repetition rate of satellite laser ranging systems, the inversion and analysis of space target motion state based on high-precision and high-repetition-rate satellite laser ranging data has become a hot spot in current research. How to filter out noise and retain valid information from the high-repetition-rate, high-precision laser ranging data has become a challenge. The traditional polynomial fitting method has problems with low data accuracy and erroneous deletion of valid data when processing laser ranging data with complex patterns. To address this problem, this paper innovatively designs a new type of spline function and accordingly proposes a laser ranging data preprocessing method that can automatically adapt to the trend of data variation. The method is validated using various characteristic observation data provided by Changchun station (7237), and the results show that the novel spline method is superior to the traditional method in maintaining the integrity of data patterns and significantly improves the data accuracy. After two iterations of denoising, the RMS of the novel spline method is reduced to one-fourth of that before denoising and as low as one-eighteenth of that of the traditional method, and the accuracy is further improved with the increase of the number of iterations. This study provides a practical and reliable novel solution for the preprocessing of laser ranging data with complex patterns. Full article

Additive manufacturing technologies have allowed the production of complex geometries that are typically obtained by applying topology optimization techniques. The outcome of the optimization process is a tessellated geometry, which has reduced aesthetic quality and unwanted spikes and cusps. Filters can be applied to improve the surface quality, but volume shrinking and geometry modification can be noticed. The design practice suggests manually re-designing the object in Computer-Aided Design (CAD) software, imitating the shape suggested by topology optimization. However, this operation is tedious and a lot of time is wasted. This paper proposes a methodology to automate the conversion from topology optimization output to a CAD-compatible design for industrial components. Topology optimization usually produces a dense triangle mesh with a high topological genus for those objects. We present a method to automatically generate a collection of spline (tensor-product) patches joined watertight and test the approach on real-wise industrial components. The methodology is based on the use of quadrilateral patches which are built on the external surface of the components. Based on the tests carried out, promising results have been obtained. It constitutes a first step towards the automatic generation of shapes that can readily be imported and edited in a CAD system. Full article