Spatial Operators Research Articles

Вычислительные диэлектрические метаповерхности в фотонных топологических устройствах обработки многомерных сигналов

A single-layer computational dielectric broadband metasurface (MS) with silicon nanodiscs is proposed to implement spatial differentiation and contour recognition operations. The variety of physical mechanisms of dielectric MSs for performing similar mathematical operations is considered. The necessity of such computational nanostructures for improving the numerous methods of topological texture-fractal processing of signals and fields in modern radio physics and radio electronics is revealed.

Accuracy of an implicit scheme for the finite element method with a penalty for a nonlocal parabolic obstacle problem

In order to solve a parabolic variational inequality with a nonlocal spatial operator and a one-sided constraint on the solution, a numerical method based on the penalty method, finite elements, and the implicit Euler scheme is proposed and studied. Optimal estimates for the accuracy of the approximate solution in the energy norm are obtained.

Micromorphology and thematic micro-mapping reveal differences in the soil structuring traits of three arbuscular mycorrhizal fungi

Arbuscular mycorrhizal (AM) fungi contribute to soil structure, but little is known about the effect of individual fungal species on soil aggregation. In this study, the influence of 3 AM fungi species on soil aggregation in a Vitric Andosol was determined using physical, micromorphological, and imaging analyses. We used a pipe of polyvinyl chloride (PVC) with a six-way connector, which was filled with soil plus AM fungal inoculum (Funneliformis mosseae, Rhizophagus intraradices, Gigaspora gigantea or non-inoculated -control-). Then lateral pipe connectors (experimental units) were covered with mesh systems (0.5, 0.25, and 0.034 mm), and PVC tubes filled with sterile soil were connected laterally using a clamp. The greenhouse experiment consisted of four treatments each with 32 experimental units. Four experimental units of each treatment were separated and collected at different times during the year: three were used to determine water-stable aggregates (disturbed soils), and one was preserved (undisturbed soil) to elaborate soil thin sections. Thematic micro-maps were constructed with image mosaics from a whole soil thin section, and micromorphological analyses were conducted using spatial operators. Our results showed that AM fungi affect soil aggregation forming micro-aggregates and macro-aggregates of different sizes. The most significant effects were observed with F. mosseae > R. intraradices >Gi. gigantea > control. Aggregation hierarchy was observed in micromorphological analysis, where F. mosseae and R. intraradices start binding organo-mineral particles and microaggregates to form macroaggregates, modifying soil structure from intergrain (apedal= without peds) to crumb aggregates (pedal= with peds). Gigaspora gigantea only promoted macroaggregation, by associating with pumice particles. The two AM fungi from Glomeraceae possess similar morphology compared to that isolate belonging to Gigasporaceae, which explain in part, their differential contribution traits on soil aggregation, as highlighted by using together physical and micromorphological analyses of soil thin sections based on high-resolution image mosaics.

Tunable optical spatial differential operation via photonic spin Hall effect in a Weyl semimetal.

Optical differential operation is the basic principle of optical image edge detection, which has the advantages of high efficiency, simple structure and markerless compared with the traditional digital image processing methods. In this paper, we propose an optical differential operation with high contrast based on the photonic spin Hall effect in a Weyl semimetal, which enables to switch between one- and two-dimensional edge detection. Due to the unique optical and electrical properties of the Weyl semimetal, a transport model for the differential operation is established, which is closely related to the beam shifts. By tuning the incidence conditions, we effectively manipulate the in-plane and transverse shifts to switch differential operations between one and two dimensions. The contrast of the differential operation is further regulated by changing the physical parameters of the Weyl semimetal, and can be improved by two orders of magnitude compared to the conventional differentiator. This study provides new possibilities in edge detection and image processing owing to the advantages of switchable dimension and high contrast.

Operational and Environmental Assessment for Night Markets under the Informal Economy Impacts: Insights from the Case Study of Selangor, Malaysia

As an expression of the informal economy, the night market is not only a place that could attract locals to buy goods but also a place where tourists can experience the local characteristics. While the night market brought economic vitality to the city's economy, it also brought some issues. A comprehensive literature review of night markets revealed that existing studies paid less attention to outdoor night markets' spatial operations and waste management. Grounded in the connection between sustainability and night markets, the present study reflected on existing research findings by adopting different periods of fieldwork to conduct a case study of the night market in Jalan 17/1A, Selangor, Malaysia. The study focused on three aspects of night markets: spatial operations, waste, and waste recycling. It found that night market operations brought localized regional economic benefits while causing more severe pollution issues, significantly affecting the region's environmental health and sustainable development. The findings revealed that local government regulation of night market businesses in Malaysia is weak (the issuance of business licenses is informed of the operational guidelines), and there is also no relevant monitoring and penalties by the local government to intervene in the misbehavior of night market consumers. Based on the results of the field survey and combined with the concept of sustainable development, the present study recommended suggestions for the future spatial operation and waste management of night markets. Innovative and novel perspectives of the study combined qualitative observations and data analysis to comprehensively examine how spatial considerations and waste management strategies intersect in the unique context of night markets—aiming to fill the gaps in the sustainability of night market operations and to protect the ecology of the surrounding territorial environment, contributing to the discourse on sustainable development.

Base on temporal convolution and spatial convolution transformer for fluid prediction through well logging data

Fluid prediction is important in exploration work, helping to determine the location of exploration targets and the reserve potential of the estimated area. Machine learning methods can better adapt to different data distributions and nonlinear relationships through model training, resulting in better learning of these complex relationships. We started by using the convolution operation to process the log data, which includes temporal convolution and spatial convolution. Temporal convolution is specifically designed to capture time series relationships in time series data. In well log data, time information is often critical for understanding fluid changes and other important details. Temporal convolution learns trends and cyclical changes in the data. The spatial convolution operation makes the model more sensitive to the local features in the logging data through the design of the local receptive field and improves the sensitivity to fluid changes. Spatial convolution helps capture spatial correlations at different depths or locations. This can help the model understand the change of fluid in the vertical direction and identify the spatial relationship between different fluids. Then, we use the transformer module to predict the fluid. The transformer module uses a self-attention mechanism that allows the model to focus on information with different weights at different locations in the sequence. In the well log data, this helps the model to better capture the formation characteristics at different depths or time points and improves the modeling ability of time series information. The fully connected structure in the transformer module enables each position to interact directly with other locations in the sequence. By applying it to the data of Tarim Oilfield, the experimental results show that the convolutional transformer model proposed in this paper has better results than other machine learning models. This study provides a new idea in the field of logging fluid prediction.

An Adaptive Control Method and Learning Strategy for Ultrasound-Guided Puncture Robot

The development of a new generation of minimally invasive surgery is mainly reflected in robot-assisted diagnosis and treatment methods and their clinical applications. It is a clinical concern for robot-assisted surgery to use a multi-joint robotic arm performing human ultrasound scanning or ultrasound-guided percutaneous puncture. Among them, the motion control of the robotic arm, and the guiding and contact scanning processes of the ultrasonic (US-) probe determine the diagnosis effect, as well as the accuracy and safety of puncture surgery. To address these challenges, this study developed an intelligent robot-assisted system integrating autonomous US inspection and needle positioning, which has relation to several intelligent algorithms such as adaptive flexible control of the robot arm, autonomous US-scanning, and real-time attitude adjustment of the puncture needle. To improve the cooperativity of the spatial operation of the robot end-effector, we propose an adaptive flexible control algorithm that allows the operator to control the robot arm flexibly with low damping. To achieve the stability and uniformity of contact detection and imaging, we introduced a self-scanning method of US-probe based on reinforcement learning and built a software model of variable stiffness based on MuJoco to verify the constant force and velocity required by the end mechanism. We conducted a fixed trajectory scanning experiment at a scanning speed of 0.06 m/s. The force curve generally converges towards the desired contact force of 10 N, with minor oscillations around this value. For surgical process monitoring, we adopted the puncture needle detection algorithm based on Unet++ to acquire the position and attitude information of the puncture needle in real time. In short, we proposed and verified an adaptive control method and learning strategy by using an UR robotic arm equipped with a US-probe and puncture needle, and we improved the intelligence of the US-guided puncture robot.

Generation of 351 nm UV Q-switched laser beam with controllable spatial coherence

Ultraviolet laser with controllable spatial coherence is essential for a wide range of applications. In this study, we developed a pulsed, partially spatial coherence laser source operating in the ultraviolet (UV) range through extra-cavity frequency tripling of a solid-state Nd:glass degenerate laser. The oscillating modes of the laser can be flexibly controlled by size of the spatial filter inside the laser cavity, thus modulating the spatial coherence of the UV laser. The beam pattern and focusing, speckle suppression and imaging, coherence property of the partially coherent UV laser is investigated in detail. The third-harmonic generation (THG) supports many more transverse modes than the fundamental generation and the second-harmonic generation (SHG). Compared with high coherent UV laser, low spatial coherent UV laser has better speckle suppression effect and smoother focused spot intensity distribution. The high brightness, good monochromaticity and low spatial coherence make the UV degenerate laser an attractive illumination source for inertial confinement fusion, lithography, laser processing and others.

Formalizing and evaluating requirements of perception systems for automated vehicles using spatio-temporal perception logic

Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation methods appear to be inadequate in assessing the performance of a perception subsystem when used within an AV. In this paper, we present a logic—referred to as Spatio-Temporal Perception Logic (STPL)—which utilizes both spatial and temporal modalities. STPL enables reasoning over perception data using spatial and temporal operators. One major advantage of STPL is that it facilitates basic sanity checks on the functional performance of the perception system, even without ground truth data in some cases. We identify a fragment of STPL which is efficiently monitorable offline in polynomial time. Finally, we present a range of specifications for AV perception systems to highlight the types of requirements that can be expressed and analyzed through offline monitoring with STPL.

Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network

ABSTRACT Land use/cover change is a major cause of ecological degradation. Reliable LUCC data are essential for evaluating habitat quality. The current method of surface cover classification based on the convolutional neural networks (CNNs) is usually a local spatial operation using a regular convolutional kernel, which ignores the correlation between adjacent image elements. This paper proposes a combination network with two branches, branch 1 uses the K-nearest neighbor clustering algorithm to construct superpixels and then uses the data transformation module to construct a graph attention network (GAT); branch 2 constructs the CNN using attention and residual modules to obtain the spatial and higher-order semantic information of the images. Finally, the features are fused using weighted fusion, and a classification map with less point noise and greater consistency with the real surface coverage is obtained. The classification results of this network are better than those of the other competitive methods. In addition, the urbanization of Sanya has resulted in significant habitat degradation. A good fit ( R 2 in 2020 = 0.639) between habitat quality (HQ) and natural and socioeconomic factors was observed in Sanya. Natural factors are more relevant to HQ than socioeconomic factors and vary spatially.

MetaSwin: a unified meta vision transformer model for medical image segmentation.

Transformers have demonstrated significant promise for computer vision tasks. Particularly noteworthy is SwinUNETR, a model that employs vision transformers, which has made remarkable advancements in improving the process of segmenting medical images. Nevertheless, the efficacy of training process of SwinUNETR has been constrained by an extended training duration, a limitation primarily attributable to the integration of the attention mechanism within the architecture. In this article, to address this limitation, we introduce a novel framework, called the MetaSwin model. Drawing inspiration from the MetaFormer concept that uses other token mix operations, we propose a transformative modification by substituting attention-based components within SwinUNETR with a straightforward yet impactful spatial pooling operation. Additionally, we incorporate of Squeeze-and-Excitation (SE) blocks after each MetaSwin block of the encoder and into the decoder, which aims at segmentation performance. We evaluate our proposed MetaSwin model on two distinct medical datasets, namely BraTS 2023 and MICCAI 2015 BTCV, and conduct a comprehensive comparison with the two baselines, i.e., SwinUNETR and SwinUNETR+SE models. Our results emphasize the effectiveness of MetaSwin, showcasing its competitive edge against the baselines, utilizing a simple pooling operation and efficient SE blocks. MetaSwin's consistent and superior performance on the BTCV dataset, in comparison to SwinUNETR, is particularly significant. For instance, with a model size of 24, MetaSwin outperforms SwinUNETR's 76.58% Dice score using fewer parameters (15,407,384 vs 15,703,304) and a substantially reduced training time (300 vs 467 mins), achieving an improved Dice score of 79.12%. This research highlights the essential contribution of a simplified transformer framework, incorporating basic elements such as pooling and SE blocks, thus emphasizing their potential to guide the progression of medical segmentation models, without relying on complex attention-based mechanisms.

Wave Propagation in High-Contrast Media: Periodic and Beyond

Abstract This work is concerned with the classical wave equation with a high-contrast coefficient in the spatial derivative operator. We first treat the periodic case, where we derive a new limit in the one-dimensional case. The behavior is illustrated numerically and contrasted to the higher-dimensional case. For general unstructured high-contrast coefficients, we present the Localized Orthogonal Decomposition and show a priori error estimates in suitably weighted norms. Numerical experiments illustrate the convergence rates in various settings.

LES of turbulent partially-premixed flames using reaction–diffusion manifold-reduced chemistry with a consistent gradient estimate determined “on the fly”

This paper devises and applies a method for determining consistent, system-adapted reaction–diffusion manifolds (REDIM) for turbulent partially-premixed combustion. The method is an extension of a previous technique which was limited to laminar flames. It adapts a REDIM to the scalar gradients present in a given reacting flow by an iterative procedure, in which gradients from REDIM-reduced simulations are used to create a new and improved REDIM. An application of the method is demonstrated using large eddy simulations (LES) of a turbulent partially-premixed methane/air flame as the “gradient estimate generating” flame simulation. Compared to the previous laminar flame studies, the turbulent flame features less sharp gradient-scalar correlations because of the strong scattering of data. Methods for capturing the scatter in scalar-gradient correlations are applied, allowing us to assess the influence of the scatter on the resulting REDIM. Also, the fact that turbulent flame simulations often involve spatial filtering operations which tend to reduce the magnitude of the gradients is taken into account by comparing the REDIM output to quasi-direct numerical simulation (qDNS) data. It is found that the gradients devised from the filtered simulations are by a factor of 2–3 below those of the more fully resolved qDNS. Nevertheless, the REDIM devised from the LES predicts the states of the qDNS with good accuracy. Overall, observations show that the method can produce realistic reduced models also for systems with complex interactions of chemical reaction, molecular transport and flow, like partially-premixed turbulent flames.

Differentiation of Optical Signals Using an Integrated Metal–Dielectric–Metal Structure

The authors consider the optical properties of integrated metal–dielectric–metal structures embedded in a dielectric slab waveguide and their application to the problems of first- and second-order optical differentiation of the profile of the incident optical beam in reflection. Numerical simulation results show the investigated structures allow the spatial differentiation operation to be performed with high quality. The presented results could find application in new systems for optical information processing and analog optical computing based on integrated nanophotonic structures.

Complete Region of Interest for Unconstrained Palmprint Recognition.

Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks to crop the palmprint region of interest (ROI); and 2) large intra-class diversities of unconstrained palmprint images hinder the learning of intra-class-invariant palmprint features. In this paper, we propose to directly extract the complete palmprint region as the ROI (CROI) using the detection-style CenterNet without requiring the detection of any landmarks, and large intra-class diversities may occur. To address this, we further propose a palmprint feature alignment and learning hybrid network (PalmALNet) for unconstrained palmprint recognition. Specifically, we first exploit and align the multi-scale shallow representation of unconstrained palmprint images via deformable convolution and alignment-aware supervision, such that the pixel gaps of the intra-class palmprint CROIs can be minimized in shallow feature space. Then, we develop multiple triple-attention learning modules by integrating spatial, channel, and self-attention operations into convolution to adaptively learn and highlight the latent identity-invariant palmprint information, enhancing the overall discriminative power of the palmprint features. Extensive experimental results on four challenging palmprint databases demonstrate the promising effectiveness of both the proposed PalmALNet and CROI for unconstrained palmprint recognition.

Hybrid deep learning model for heart disease detection on 12-lead electrocardiograms

This research possesses the producing and taking the assessment of a new deep learning structure, namely, Deep Convolutional BiLSTM Hybrid Network, specialized for arrhythmia recognition before ECG records. Through PhysioNet Challenge 2021 dataset, which has collected different styles of the ECG recordings from nine different arrhythmia types, this study is trying to respond to the critical need that non-attendant and accurate arrhythmia detection is required in the medical field. By utilizing both convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) units that allow our model to carry out spatial and temporal operations, which is essential to the native dynamics existing ECG signal, we are able to get in view a more fine-grained analysis surpassing other traditional approaches. The suggested algorithm which has achieved accuracy, precision, recall, and F-score of 98.5%, 98.2%, 98.2%, and 98.4% percent respectively has proven to be a novel and more accurate solution compared to the existing state-of-the-art model when similar kinds of tasks are performed. These results not only reveal the effectiveness of CNNs and BiLSTM toginger in understanding complicated biomedical signs but also shed a light on the prospect of deep learning in raising the standards of dairgocis and health care practices. The research outcome of arrhythmia diagnosis by arranging new standard carpatients would bring a lot of changes to the existing medical informatics that provide a scalable solution that would be implemented to every healthcare institution globally for timely and accurate diagnosis.

Learning Unified Anchor Graph for Joint Clustering of Hyperspectral and LiDAR Data.

The joint clustering of multimodal remote sensing (RS) data poses a critical and challenging task in Earth observation. Although recent advances in multiview subspace clustering have shown remarkable success, existing methods become computationally prohibitive when dealing with large-scale RS datasets. Moreover, they neglect intrinsic nonlinear and spatial interdependencies among heterogeneous RS data and lack generalization ability for out-of-sample data, thereby restricting their applicability. This article introduces a novel unified framework called anchor-based multiview kernel subspace clustering with spatial regularization (AMKSC). It learns a scalable anchor graph in the kernel space, leveraging contributions from each modality instead of seeking a consensus full graph in the feature space. To ensure spatial consistency, we incorporate a spatial smoothing operation into the formulation. The method is efficiently solved using an alternating optimization strategy, and we provide theoretical evidence of its scalability with linear computational complexity. Furthermore, an out-of-sample extension of AMKSC based on multiview collaborative representation-based classification is introduced, enabling the handling of larger datasets and unseen instances. Extensive experiments on three real heterogeneous RS datasets confirm the superiority of our proposed approach over state-of-the-art methods in terms of clustering performance and time efficiency. The source code is available at https://github.com/AngryCai/AMKSC.

A General Spatial-Frequency Learning Framework for Multimodal Image Fusion.

multimodal image fusion involves tasks like pan-sharpening and depth super-resolution. Both tasks aim to generate high-resolution target images by fusing the complementary information from the texture-rich guidance and low-resolution target counterparts. They are inborn with reconstructing high-frequency information. Despite their inherent frequency domain connection, most existing methods only operate solely in the spatial domain and rarely explore the solutions in the frequency domain. This study addresses this limitation by proposing solutions in both the spatial and frequency domains. We devise a Spatial-Frequency Information Integration Network, abbreviated as SFINet for this purpose. The SFINet includes a core module tailored for image fusion. This module consists of three key components: a spatial-domain information branch, a frequency-domain information branch, and a dual-domain interaction. The spatial-domain information branch employs the spatial convolution-equipped invertible neural operators to integrate local information from different modalities in the spatial domain. Meanwhile, the frequency-domain information branch adopts a modality-aware deep Fourier transformation to capture the image-wide receptive field for exploring global contextual information. In addition, the dual-domain interaction facilitates information flow and the learning of complementary representations. We further present an improved version of SFINet, SFINet++, that enhances the representation of spatial information by replacing the basic convolution unit in the original spatial domain branch with the information-lossless invertible neural operator. We conduct extensive experiments to validate the effectiveness of the proposed networks and demonstrate their outstanding performance against state-of-the-art methods in two representative multimodal image fusion tasks: pan-sharpening and depth super-resolution. The source code is publicly available at https://github.com/manman1995/Awaresome-pansharpening.

Application of high-order SF-SFDTD scheme to solving a time-dependent SchrFDTD scheme , Wu

The finite-difference time-domain 2, 2 (FDTD (2, 2)) method with second-order numerical accuracy in time and space has been extensively employed in the field of quantum mechanics to solve the Schrödinger equation. Nevertheless, the presence of the Courant-Friedrichs-Lewy (CFL) condition imposes limitations on the grid size in the computational space, thereby constraining the admissible range of time steps. Accordingly, the efficiency of the FDTD(2, 2) method significantly decreases. In addition, the second-order numerical accuracy of the FDTD(2, 2) method both in time domain and in space domain often results in significant error accumulation during calculations, thereby undermining the fidelity of the simulation results. To surmount the constraints imposed by the CFL stability conditions and enhance the accuracy of computations, a novel approach termed SF-SFDTD(3, 4) method has been proposed, with 3 and 4 referring to the accuracy in space and time, respectively. This method combines spatial filtering (SF) with the high-order symplectic finite-difference time-domain (SFDTD) method. Its primary objective is to solve the time-dependent Schrödinger equation while ensuring time stability and scalability. The SF-SFDTD(3, 4) method obviates the need for further deriving the iterative formula employed in the conventional SFDTD(3, 4) method. Therefore, the method under consideration exhibits a remarkable degree of compatibility with its traditional counterpart. It is merely necessary to include a spatial filtering operation during each numerical iteration to eliminate spatial high-frequency components arising from the utilization of time step sizes that fail to satisfy the CFL stability condition, thereby ensuring the stability of the numerical scheme. Moreover, when the time step value satisfies the CFL stability condition, the amplitude of the high-frequency component approaches zero, thereby exerting a minimal influence on the accuracy of the computational results. The adoption of time steps that do not meet the CFL stability conditions leads to an amplification in the amplitude of the high-frequency component. However, this finding solely affects the stability of the computational results, and the elimination of these unstable high-frequency components scarcely affect the accuracy of the computational results. The SF-SFDTD(3, 4) retains the simplicity and efficacy inherent in the traditional SFDTD(3, 4) methods, while enhancing computational efficiency. Additionally, the numerical stability and dispersion error of the SF-SFDTD(3, 4) method are analyzed theoretically . Finally, the validity and efficacy of the proposed method are corroborated through numerical illustrations.

4D High-Resolution Imagery of Point Clouds for Automotive mmWave Radar

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.