Search Results (792)

With the development of underground space in urban areas, the demand for tunneling through existing railways is increasing. The adverse effects of cut-blasting during the construction of tunnels under crossing existing railways are investigated. Combined with the principle of blasting seismic wave superposition, LS-DYNA numerical simulation is used to analyze the seismic wave superposition law under different superposition methods. This study also investigates the vibration reduction effect of millisecond blasting for cut-blasting under the different classes of surrounding rocks. The results show that the vibration reduction forms of millisecond blasting can be divided into separation and interference of waveform. Based on the principle of superposition of blasting seismic waves, vibration reduction through wave interference is further divided. At the same time, a new vibration reduction mode is proposed. This vibration reduction mode can significantly improve construction efficiency while improving damping efficiency. The new vibration reduction mode can increase the vibration reduction to 80% while improving construction efficiency. Additionally, there is a significant difference in the damping effect of different classes of surrounding rock on the blasting seismic wave. Poor-quality surrounding rock enhances the attenuation of seismic wave velocity and peak stress in the surrounding rock. In the Zhongliangshan Tunnel, a tunnel cut-blasting construction at a depth of 42 m, the best vibration reduction plan of Class III is 3 ms millisecond blasting, in which the surface points achieve separation vibration reduction. The best vibration reduction plan of Class V is 1 ms millisecond blasting, in which the surface points achieve a new vibration reduction mode. During the tunnel blasting construction process, electronic detonators are used for millisecond blasting of the cut-blasting. This method can reduce the vibration effects generated by blasting. The stability of the existing railway is ultimately guaranteed. This can improve construction efficiency while ensuring construction safety. This study can provide significant guidance for the blasting construction of the tunnel through the railway. Full article

This paper proposes a lightweight gaze-tracking architecture named GhostBlock-Augmented Look to Coordinate Space (L2CS), which integrates GhostNet-based modules and depthwise separable convolution to achieve a better trade-off between model accuracy and computational efficiency. Conventional lightweight gaze-tracking models often suffer from degraded accuracy due to aggressive parameter reduction. To address this issue, we introduce GhostBlocks, a custom-designed convolutional unit that combines intrinsic feature generation with ghost feature recomposition through depthwise operations. Our method enhances the original L2CS architecture by replacing each ResNet block with GhostBlocks, thereby significantly reducing the number of parameters and floating-point operations. The experimental results on the Gaze360 dataset demonstrate that the proposed model reduces FLOPs from 16.527 × 10⁸ to 8.610 × 10⁸ and parameter count from 2.387 × 10⁵ to 1.224 × 10⁵ while maintaining comparable gaze estimation accuracy, with MAE increasing only slightly from 10.70° to 10.87°. This work highlights the potential of GhostNet-augmented designs for real-time gaze tracking on edge devices, providing a practical solution for deployment in resource-constrained environments. Full article

This paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on boron-based fuel under various flow rate conditions. Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. The proposed framework enables accurate classification and prediction of the dynamic evolution of flow structures based on learned representations from scattering images. Experimental results show that the feature vectors extracted by the SAE form clearly separable clusters in the latent space, leading to high classification accuracy under varying flow conditions. In the prediction task, the feature vectors predicted by the LSTM exhibit strong agreement with ground truth, with average mean square error, mean absolute error, and r-square values of 0.0027, 0.0398, and 0.9897, respectively. Furthermore, the reconstructed images offer a visual representation of the changing flow field, validating the model’s effectiveness in structure-level recovery. These results suggest that the proposed method provides reliable support for future real-time prediction of powder fuel mass flow rates based on optical sensing and imaging techniques. Full article

Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments. Full article

This paper presents a formulation for the dynamic analysis of dam–reservoir–foundation systems, employing a coupled finite element model that integrates displacements and reservoir pressures. An innovative coupled approach, without separating the solid and fluid equations, is proposed to directly solve the single non-symmetrical governing equation for the whole system with non-proportional damping. For the modal analysis, a state–space method is adopted to solve the coupled eigenproblem, and complex eigenvalues and eigenvectors are computed, corresponding to non-stationary vibration modes. For the seismic analysis, a time-stepping method is applied to the coupled dynamic equation, and the stress–transfer method is introduced to simulate the nonlinear behavior, innovatively combining a constitutive joint model and a concrete damage model with softening and two independent scalar damage variables (tension and compression). This formulation is implemented in the computer program DamDySSA5.0, developed by the authors. To validate the formulation, this paper provides the experimental and numerical results in the case of the Cahora Bassa dam, instrumented in 2010 with a continuous vibration monitoring system designed by the authors. The good comparison achieved between the monitoring data and the dam–reservoir–foundation model shows that the formulation is suitable for simulating the modal response (natural frequencies and mode shapes) for different reservoir water levels and the seismic response under low-intensity earthquakes, using accelerograms measured at the dam base as input. Additionally, the dam’s nonlinear seismic response is simulated under an artificial accelerogram of increasing intensity, showing the structural effects due to vertical joint movements (release of arch tensions near the crest) and the concrete damage evolution. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

This paper presents new fast algorithms for the fourth type discrete Hartley transform (DHT-IV) for input data sequences of lengths from 3 to 8. Fast algorithms for small-sized trigonometric transforms can be used as building blocks for synthesizing algorithms for large-sized transforms. Additionally, they can be utilized to process small data blocks in various digital signal processing applications, thereby reducing overall system latency and computational complexity. The existing polynomial algebraic approach and the approach based on decomposing the transform matrix into cyclic convolution submatrices involve rather complicated housekeeping and a large number of additions. To avoid the noted drawback, this paper uses a structural approach to synthesize new algorithms. The starting point for constructing fast algorithms was to represent DHT-IV as a matrix–vector product. The next step was to bring the block structure of the DHT-IV matrix to one of the matrix patterns following the structural approach. In this case, if the block structure of the DHT-IV matrix did not match one of the existing patterns, its rows and columns were reordered, and, if necessary, the signs of some entries were changed. If this did not help, the DHT-IV matrix was represented as the sum of two or more matrices, and then each matrix was analyzed separately, if necessary, subjecting the matrices obtained by decomposition to the above transformations. As a result, the factorizations of matrix components were obtained, which led to a reduction in the arithmetic complexity of the developed algorithms. To illustrate the space–time structures of computational processes described by the developed algorithms, their data flow graphs are presented, which, if necessary, can be directly mapped onto the VLSI structure. The obtained DHT-IV algorithms can reduce the number of multiplications by an average of 75% compared with the direct calculation of matrix–vector products. However, the number of additions has increased by an average of 4%. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

Orthogonal time–frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited number of works have focused on this issue. In this paper, we propose a novel AMC approach for OTFS systems. We build a dual-stream convolutional neural network (CNN) model to simultaneously capture multi-domain signal features, which substantially enhances recognition accuracy. Moreover, we propose a differentiated embedded pilot structure that incorporates information about distinct modulation schemes to further improve the separability of modulation types. The results of the extensive experiments carried out show that the proposed approach can achieve high classification accuracy even under low signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art baselines. Full article

As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity, while deep learning-based approaches offer superior representational capacity. However, challenges remain in achieving a balance between fine-detail adaptation and computational efficiency. Mamba, a state–space model (SSM)-based architecture, offers linear-time complexity and excels at capturing long-range dependencies in sequences. It has been adopted in remote sensing compression tasks to model long-distance dependencies between pixels. However, despite its effectiveness in global context aggregation, Mamba’s uniform bidirectional scanning is insufficient for capturing high-frequency structures such as edges and textures. Moreover, existing visual state–space (VSS) models built upon Mamba typically treat all channels equally and lack mechanisms to dynamically focus on semantically salient spatial regions. To address these issues, we present an innovative architecture for distant sensing image compression, called the Multi-scale Channel Global Mamba Network (MGMNet). MGMNet integrates a spatial–channel dynamic weighting mechanism into the Mamba architecture, enhancing global semantic modeling while selectively emphasizing informative features. It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. The Channel–Global Information Modeling (CGIM) module enhances conventional VSS by introducing a dual-path attention strategy that reweights spatial and channel information, improving the modeling of long-range dependencies and edge structures. We conducted extensive evaluations on three distinct remote sensing datasets to assess the MGMNet. The results of the investigations revealed that MGMNet outperforms the current SOTA models across various performance metrics. Full article

Schmidt entropy is used as a common denotation for all Hilbert space entropies that can be defined via the Schmidt decomposition theorem; they include quantum entanglement entropies and classical separability entropies. Exact results about the asymptotic growth in time of such entropies (in the form of Renyi entropies of any order $\ge 1$) are directly derived from the Schmidt decompositions. Such results include a proof that pure point spectra entail boundedness in time of all entropies of order larger than 1; and that slower than exponential transport forbids faster than logarithmic asymptotic growth. Applications to coupled Quantum Kicked Rotors and to Floquet systems are presented. Full article

This paper presents a novel video encoding and decoding method aimed at enhancing security and reducing storage requirements, particularly for CCTV systems. The technique merges two video streams of matching frame dimensions into a single stream, optimizing disk space usage without compromising video quality. The combined video is secured using an advanced encryption standard (AES)-based shift algorithm that rearranges pixel positions, preventing unauthorized access. During decoding, the AES shift is reversed, enabling precise reconstruction of the original videos. This approach provides a space-efficient and secure solution for managing multiple video feeds while ensuring accurate recovery of the original content. The experimental results demonstrate that the transmission time for the encoded video is consistently shorter compared to transmitting the video streams separately. This, in turn, leads to about $54\%$ reduction in energy consumption across diverse outdoor and indoor video datasets, highlighting significant improvements in both transmission efficiency and energy savings by our proposed scheme. Full article

This study proposes the PHD-YOLO model as a means to enhance the precision of passion fruit detection in intricate orchard settings. The model has been meticulously engineered to circumvent salient challenges, including branch and leaf occlusion, variances in illumination, and fruit overlap. This study introduces a pioneering partial convolution module (ParConv), which employs a channel grouping and independent processing strategy to mitigate computational complexity. The module under consideration has been demonstrated to enhance the efficacy of local feature extraction in dense fruit regions by integrating sub-group feature-independent convolution and channel concatenation mechanisms. Secondly, deep separable convolution (DWConv) is adopted to replace standard convolution. The proposed method involves decoupling spatial convolution and channel convolution, a strategy that enables the retention of multi-scale feature expression capabilities while achieving a substantial reduction in model computation. The integration of the HSV Attentional Fusion (HSVAF) module within the backbone network facilitates the fusion of HSV color space characteristics with an adaptive attention mechanism, thereby enhancing feature discriminability under dynamic lighting conditions. The experiment was conducted on a dataset of 1212 original images collected from a planting base in Yunnan, China, covering multiple periods and angles. The dataset was constructed using enhancement strategies, including rotation and noise injection, and contains 2910 samples. The experimental results demonstrate that the improved model achieves a detection accuracy of 95.4%, a recall rate of 85.0%, mAP@0.5 of 91.5%, and an F1 score of 90.0% on the test set, which are 0.7%, 3.5%, 1.3%, and 2. The model demonstrated a 4% increase in accuracy compared to the baseline model YOLOv11n, with a single-frame inference time of 0.6 milliseconds. The model exhibited significant robustness in scenarios with dense fruits, leaf occlusion, and backlighting, validating the synergistic enhancement of staged convolution optimization and hybrid attention mechanisms. This solution offers a means to automate the monitoring of orchards, achieving a balance between accuracy and real-time performance. Full article

Wake effects significantly reduce efficiency and increase structural loads in wind farms. Therefore, accurate and computationally efficient models are crucial for wind farm layout optimization and operational control. High-fidelity computational fluid dynamics (CFD) simulations, while accurate, are too slow for these tasks, whereas faster analytical models often lack dynamic fidelity and 3D detail, particularly under complex conditions. Existing data-driven surrogate models based on neural networks often struggle with the high dimensionality of the flow field and scalability to large wind farms. This paper proposes a novel data-driven surrogate modeling framework to bridge this gap, leveraging Neural Networks (NNs) trained on data from the high-fidelity SOWFA (simulator for wind farm applications) tool. A physics-inspired NN architecture featuring an autoencoder for spatial feature extraction and latent space dynamics for temporal evolution is introduced, motivated by the time–space decoupling structure observed in the Navier–Stokes equations. To address scalability for large wind farms, a “wind box” decomposition strategy is employed. This involves training separate NN models on smaller, canonical domains (with and without turbines) that can be stitched together to represent larger farm layouts, significantly reducing training data requirements compared to monolithic farm simulations. The development of a batch simulation interface for SOWFA to generate the required training data efficiently is detailed. Results demonstrate that the proposed surrogate model accurately predicts the 3D dynamic wake evolution for single-turbine and multi-turbine configurations. Specifically, average velocity errors (quantified as RMSE) are typically below 0.2 m/s (relative error < 2–5%) compared to SOWFA, while achieving computational accelerations of several orders of magnitude (simulation times reduced from hours to seconds). This work presents a promising pathway towards enabling advanced, model-based optimization and control of large wind farms. Full article

This paper introduces a single-shot ultrafast imaging technique termed wavelength and polarization time-encoded ultrafast raster imaging (WP-URI). By integrating raster imaging principles with wavelength- and polarization-based temporal encoding, the system uses a spatial raster mask and time–space mapping to aggregate multiple two-dimensional temporal raster images onto a single detector plane, thereby enabling the effective spatial separation and extraction of target information. Finally, the target dynamics are recovered using a reconstruction algorithm based on the Nyquist–Shannon sampling theorem. Numerical simulations demonstrate the single-shot acquisition of four dynamic frames at 25 trillion frames per second (Tfps) with an intrinsic spatial resolution of 50 line pairs per millimeter (lp/mm) and a wide field of view. The WP-URI technique achieves unparalleled spatio-temporal resolution and frame rates, offering significant potential for investigating ultrafast phenomena such as matter interactions, carrier dynamics in semiconductor devices, and femtosecond laser–matter processes. Full article