High Computational Efficiency Research Articles

Modeling of multi-scale 3-D conductive network and electrical conductivity of carbon fiber braided composites

The prediction of electrical conductivity and current conduction path of carbon fiber 3-D braided composites is challenging due to its complex microstructure. This paper presents a modeling approach for establishing the multi-scale 3-D conductive network to study the current conduction behavior of the braided composites. The real microstructures, including the random distribution and random contact of carbon fibers at the micro-scale, the spatial path of carbon fiber yarn, and the yarn contact at the meso-scale, have been considered in the model. The conductive network models are valid for the conductivity prediction of the yarn and the composites. The electric potential distribution and current conduction path inside the yarn and the composites have been obtained with the multi-scale 3-D conductive network. This work provides a modeling method with high computational efficiency and simple implementation to quickly predict and design the conductivity of 3-D braided composites.

Nonlinear subspace clustering by functional link neural networks

Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. We name our method as Functional Link Neural Network Subspace Clustering (FLNNSC). Furthermore, we propose a convex combination subspace clustering scheme that combines a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination method is named as Convex Combination Subspace Clustering (CCSC), which allows for a dynamic balance between linear and nonlinear representations. Extensive experiments conducted on four widely used datasets, including Extended Yale B, USPS, COIL20, and ORL, demonstrate that both FLNNSC and CCSC outperform several state-of-art subspace clustering methods in terms of clustering accuracy. Our affinity graph experiments reveal that FLNNSC exhibits clear block diagonal structures. We provide recommendations for hyperparameters in FLNNSC by performing a parameter sensitivity analysis, and empirically verify the convergence of FLNNSC. Additionally, we show that FLNNSC has a lower computational cost compared to two high-performing methods.

A Hybrid Approach of Substitution and Permutation techniques for Modern Image-Cryptosystem

Abstract This paper proposes a new image encryption algorithm based on the introduction of cellular automata with Galois field-based methods to further encourage a higher level of security. This begins the encryption process by creating an initial substitution box from the irreducible polynomials in the Galois field, followed by the final substitution box obtained through permutation operations. This lays
the foundation for a sound cryptographic process. In order to handle different cryptographic attack types, balance in the substitution box has been closely analyzed. Additionally, we leverage the chaotic nature of the logistic map to incorporate a random element, significantly strengthening the encryption scheme against diverse cryptanalytic attacks. Nonlinear dynamics, as introduced by cellular automata, further develop the encryption process and disperse the image’s data, making it more intricate for any potential attacker. The differential analysis validates the encryption scheme’s resistance to differential attacks, thereby confirming its potential for secure image transmission and storage. The proposed hybrid approach presents a comprehensive solution for image encryption, ensuring high security and computational efficiency. This contribution is an added advantage in the development of image cryptosystems,
addressing the modern challenges facing secure data within the digital landscape.

TIA-UNet: transformer-enhanced deep learning for adolescent idiopathic scoliosis spinal x-ray image segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual x-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy. This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models. By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD). TIA-UNet attained a Dice similarity coefficient of 90. 02%, a Mean Intersection over Union (MIoU) of 81. 96%, and a Hausdorff Distance (HD) of 4. 09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floating-point operations (FLOPs). As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning techniques in medical image analysis.

Terahertz time-domain spectral hierarchical thickness measurement based on integer genetic algorithm

ABSTRACT Terahertz time-domain spectroscopy is a widely used non-contact detection technology with significant potential in the field of non-destructive testing. In this study, the transfer function of the coating system is derived based on the Rouard model and the refractive index of the coating within the transfer function is modelled using the Debye model. The hierarchical thickness of the multilayer composite material is fitted by random optimisation algorithm. Considering the complexity of the objective function and the practical accuracy requirements in engineering problems, the integer genetic algorithm is adopted to find the optimal parameters. The comparison of the ordinary genetic algorithm and the integer genetic algorithm reveals that the integer genetic algorithm requires a significantly smaller population size and computation time than the ordinary genetic algorithm. Furthermore, the predicted thickness obtained by the integer genetic algorithm is more repeatable, has higher computational efficiency, and exhibits smaller errors. Our results indicate that combining the integer genetic algorithm with the Rouard model holds potential applications in thickness measurement of multilayer composite materials.

A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis

Efficient bearing fault diagnosis not only extends the operational lifespan of rolling bearings but also reduces unnecessary maintenance and resource waste. However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. To address these challenges, this paper proposes a Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation (MSCNN-SKD) for bearing fault diagnosis. The MSCNN-SKD employs a five-stage architecture. Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. In the final two stages, a self-distillation process enhances learning by allowing deep-layer features to guide shallow-layer learning, improving performance, especially in data-limited scenarios. Extensive experiments on multiple datasets validate the model’s high diagnostic accuracy, computational efficiency, and robustness, demonstrating its suitability for real-time industrial applications in resource-limited environments.

Reparameterization of the mW model to accurately predict the experimental phase diagram of methane hydrate.

Due to their high computational efficiency, the coarse-grained water models are of particular importance for practical molecular simulations of gas hydrates. In these models, the mW model is successfully used to study many thermodynamics and dynamics of methane hydrate. Yet, despite several decades of intense research, the mW model is still found to overestimate the melting temperature of methane hydrate. We here employ the minimum mean squared error estimation to revisit the key parameter of the mW model, which determines the strength of the tetrahedral angle of the water system. Relying on the free energy calculations, we first estimate the chemical potentials of water in the liquid phase for temperatures at which methane hydrate forms. We then turn to the mean squared error to describe the chemical potential deviation between the mW model and the TIP4P/ice model (the latter could reproduce the experimental phase diagram of methane hydrate). By minimizing the mean squared error, we finally have an optimized parameter for the mW model. In this part, we also discuss the pressure effect on such reparameterization procedure. Moreover, relying on the direct coexistence method, the melting temperature determined using the reparameterized mW model is found to be consistent with the experimental data. This strategy provides a means to improve the coarse-grained model to match the experimental observations for temperatures in the range of interest.

Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.

Nested Neural Networks: A Novel Approach to Flexible and Deep Learning Architectures

It’s easy to get lost in the complexities of sentences like, “Never will I have been in a place whose experiences will have been teaching me how amazing life really is till the beginning of every one of those marvelous whole new advenchallenges for more than 20 years of life by the end of my military service.” These kinds of nested and layered statements are not just the domain of advanced grammar—they have a parallel in cutting-edge technology as well. This paper introduces a novel neural network architecture, termed Nested Neural Networks (NNN), that incorporates nested layers within a more complex overarching structure. Much like the layered conditions in Future Perfect and Future Perfect Progressive tenses, NNNs utilize ‘nested’ conditions to bring flexibility and depth to data processing. This structure captures complex relationships, achieving high performance, computational efficiency, and generalization across tasks, much as understanding the nested clauses of a sentence can reveal deeper meanings. Our experiments on benchmark datasets demonstrate NNNs’ ability to handle intricate data patterns with enhanced memory efficiency and training adaptability, often outperforming traditional models. This fascinating intersection between language and machine learning showcases the powerful potential of structured complexity, whether in communication or computation.

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect

Surface temperature distribution is crucial for thermal property-based studies about irregular asteroids in our Solar System. While direct numerical simulations could model surface temperatures with high fidelity, they often take a significant amount of computational time, especially for problems for which temperature distributions are required to be repeatedly calculated. To this end, the deep operator neural network (DeepONet) proves a powerful tool due to its high computational efficiency and generalization ability. In this work, we apply DeepONet to the modeling of asteroid surface temperatures. Results show that the trained network is able to predict temperature with an accuracy of ~1% on average, while the computational cost is five orders of magnitude lower, enabling thermal property analysis in a multidimensional parameter space. As a preliminary application, we analyzed the orbital evolution of asteroids through direct N- body simulations embedded with an instantaneous Yarkovsky effect inferred by DeepONet-based thermophysical modeling. Taking asteroids (3200) Phaethon and (89433) 2001 WM41 as examples, we show the efficacy and efficiency of our AI-based approach.

Economically viable reshoring of supply chains under ripple effect

This paper presents stochastic mixed integer linear programming models for economically viable supply chain reshoring under the ripple effect, that is, for the integrated optimization of supply chain reshoring and supply chain viability. The problem objective is to select supply chain facilities for relocation and backup suppliers for recovery supplies to simultaneously minimize cost, maximize service and enhance viability. If the primary supplier is not selected for reshoring, the viability can alternatively be improved by recovery supplies from the backup supplier. The problem is formulated as a multi-portfolio stochastic optimization. The portfolio of suppliers/plants for reshoring is optimized along with an alternative recovery portfolio of backup supplies. This study indicates that reshoring decisions directly affect supply chain viability and have positive impact on both its average and worst-case performance. The more risk-aversive is viable reshoring (the confidence level greater than 0.90), the greater is also improvement of supply chain business-as-usual resilience. If increase of expected customer service is more important than cost reduction, supplier reshoring is preferable to backup sourcing. The findings also indicate that cost- and service-optimal reshoring decisions are getting closer when the government subsidy for reshoring significantly increases such that the reduced reshoring costs and domestic costs do not exceed the achieved reduction of lost sales and backup sourcing. Then both conflicting objectives can be simultaneously achieved, which is a highly desirable solution of the economically viable reshoring of supply chain. Numerical examples based on a real-world industrial supply chain prove practical usefulness of the proposed stochastic approach and its high computational efficiency.

An improved vector method for simultaneous analysis of removability and kinematics in block theory

Block theory is an important and commonly used method for addressing stability problems in rock engineering, and it is very meaningful to simplify its analysis procedure and improve its computational efficiency. In this paper, an improved vector method capable of simultaneously analyzing block removability and kinematics that works well for both convex and concave blocks is proposed. This improved approach is based on rigorously proven theorems. Compared to the original vector method in block theory, it is simpler and more efficient because it skips the removability analysis and simplifies some of the judgment conditions in kinematic analysis. With the results of kinematic analysis, the improved vector method can also determine the removability of the block. The validity of the improved vector method is verified by analyzing a series of individual convex or concave blocks with different removability and failure modes. Finally, the improved vector method is applied to the block progressive failure analysis of an engineering slope, which again validates the effectiveness of the improved vector method. The advantages of the improved vector method are highlighted through a comparison with existing methods, especially its high computational efficiency, ease of understanding and implementation.

Defect dynamics modeling of mesoscale plasticity

The collective motion of defects and their interaction are the basic building blocks for plastic deformation and corresponding mechanical behaviors of crystalline metals. Especially, dislocations among various defects are the “carrier” of plastic deformation in many crystalline materials, particularly ductile materials. To get a fundamental understanding of plastic deformation mechanisms, it calls for an integrated computational platform to simultaneously capture detailed defects characteristics across several length scales together with corresponding macroscopic mechanical response. In this paper, we present a three-dimensional mesoscale defect dynamics model to directly couple the three dimensional discrete dislocation dynamics model with continuum finite element method, aiming at capturing both size dependent plasticity at micron-, and submicron scale and constitutive behaviors at larger scales where such size-dependence disappear. Using non-singular dislocation theories, our model could accurately consider both short- and long-range elastic interactions between multiple dislocation segments with even higher computational efficiency than traditional dislocation dynamics simulations, together with the careful consideration of crystal/material rotation in the coupled framework. In addition, our model could directly model dislocation nucleation from stress concentrators such as a void, crack and indentor tip, which could allow us to investigate various defects’ motion and their mutual interactions, predicting macroscopic mechanical response of complex structures. The developed concurrently coupled model could also consider multiphysical phenomena by solving coupled governing equations in finite element framework, which could shed light on complex defect behaviors under various physical environments.

Low-rank quaternion tensor completion for color video inpainting via a novel factorization strategy

Recently, a quaternion tensor product named Qt-product was proposed, and then the singular value decomposition and the rank of a third-order quaternion tensor were given. From a more applicable perspective, we extend the Qt-product and propose a novel multiplication principle for third-order quaternion tensor named gQt-product. With the gQt-product, we introduce a brand-new singular value decomposition for third-order quaternion tensors named gQt-SVD and then define gQt-rank and multi-gQt-rank. We prove that the optimal low-rank approximation of a third-order quaternion tensor exists and some numerical experiments demonstrate the low-rankness of color videos. So, we apply the low-rank quaternion tensor completion to color video inpainting problems and present alternating least-square algorithms to solve the proposed low gQt-rank and multi-gQt-rank quaternion tensor completion models. The convergence analyses of the proposed algorithms are established and some numerical experiments on various color video datasets show the high recovery accuracy and computational efficiency of our methods.

A coupled peridynamics–smoothed particle hydrodynamics model for fluid–structure interaction with large deformation

Fluid–structure interaction (FSI) is ubiquitous in various engineering disciplines, and effectively managing FSI often appears to be the key for successful failure analysis and safety-oriented design. Smoothed particle hydrodynamics (SPH) serves as a potent nonlocal meshfree method for fluid dynamics modeling, while peridynamics (PD) demonstrates exceptional capability in addressing structural dynamics involving large deformations and discontinuities. Thus, leveraging their respective strengths in a combined approach holds significant promise for tackling FSI challenges. In this work, we propose a new peridynamics–smoothed particle hydrodynamics (PD-SPH) coupling model for addressing FSI. A stable and efficient coupling algorithm for data transfer between PD and SPH is put forward. In this coupling strategy, a PD particle directly participates in solving the SPH governing equations when it is identified to be within the support domain of an SPH particle. This can be done since the SPH quantities including the density, velocity, and pressure of a PD particle are naturally attainable within the framework of non-ordinary state-based peridynamics theory. Concurrently, in solving PD governing equations, reaction forces from SPH particles act as external forces for PD particles, determined straightforwardly through Newton's third law. As such, the proposed PD-SPH coupling strategy is straightforward to implement and offers high computational efficiency. Validation examples demonstrate that the proposed PD-SPH coupling model is computationally robust and adept at capturing physical phenomena in diverse FSI scenarios involving breaking free surfaces of fluid and large structural deformations of solid. Moreover, the proposed PD-SPH coupling model is flexible introducing no constraint conditions for applications and can accommodate different particle resolutions for PD and SPH domains. These features enable a broad application range of the proposed PD-SPH coupling model including simulations of explosion-induced soil fragmentation, rock fracture, and concrete dam failure, which will be conducted by authors in the near future.

A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention

Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized by high in-plane resolution but lower through-plane resolution, presents significant challenges. While applying 2D models to individual slices of a 2.5D image is feasible, it fails to capture the spatial relationships between slices. On the other hand, 3D models face challenges such as resolution inconsistencies in 2.5D images, along with computational complexity and susceptibility to overfitting when trained with limited data. In this context, 2.5D models, which capture inter-slice correlations using only 2D neural networks, emerge as a promising solution due to their reduced computational demand and simplicity in implementation. In this paper, we introduce CSA-Net, a flexible 2.5D segmentation model capable of processing 2.5D images with an arbitrary number of slices. CSA-Net features an innovative Cross-Slice Attention (CSA) module that effectively captures 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices. Moreover, CSA-Net utilizes the self-attention mechanism to learn correlations among pixels within the center slice. We evaluated CSA-Net on three 2.5D segmentation tasks: (1) multi-class brain MR image segmentation, (2) binary prostate MR image segmentation, and (3) multi-class prostate MR image segmentation. CSA-Net outperformed leading 2D, 2.5D, and 3D segmentation methods across all three tasks, achieving average Dice coefficients and HD95 values of 0.897 and 1.40 mm for the brain dataset, 0.921 and 1.06 mm for the prostate dataset, and 0.659 and 2.70 mm for the ProstateX dataset, demonstrating its efficacy and superiority. Our code is publicly available at: https://github.com/mirthAI/CSA-Net.

Dynamic Intelligent Analysis of Single-Column Pier Bridges Considering Vehicle–Bridge Interaction

With its advantages of low engineering cost, minimal space occupancy, and aesthetically pleasing shape, the single-column pier bridge is frequently utilized in urban viaducts and interchange ramps. Due to structural peculiarities, the bridge subjected to eccentric loading, flexural and torsional interaction, and centrifugal force may undergo tipping accidents. To better comprehend the overturning resistance of the bridge, the vibration governing equation of the bridge (idealized as an orthotropic thin plate with point supports in the domain model) is established considering vehicle–bridge interaction. An approximate dynamic analysis formula is subsequently derived via a hybrid method using the grey wolf optimizer. Compared with the outcomes of finite element analysis, the maximal deviation arising from this derived analytical formula is approximately [Formula: see text]% with the greater discrepancy being observed in scenarios featuring minimal support reactions. Compared with the finite element method, the proposed method in this study has higher computational efficiency with precision. This analytical solution is then utilized in the parametric study to scrutinize the influences of span, road surface roughness, and vehicle velocity on a single-column pier bridge under four vehicles. The analysis findings indicate that the proposed approach is reasonably precise, remarkably efficient, and efficacious.

CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, several previous studies have found that such models may be affected by miscalibration, an issue that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the C ontrastive L earning and A daptive KNN for P hotometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and $k$-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data, bypassing the intensive computation required for KNN. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, a deeper investigation on miscalibration for conventional deep learning is presented. We point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP.

Multicolor wavefront sensing using Talbot effect for high-order harmonic generation

High-order harmonic generation (HHG) using femtosecond lasers is an efficient and cost-effective method for producing attosecond and femtosecond light in the extreme ultraviolet and soft x-ray regions. This technique generates high-brightness harmonic beams with stable spectra, intensity, and wavefronts, making it a valuable tool for applications such as nanoimaging, material metrology, and studies of ultrafast dynamics. Accurate wavefront measurement of HHG beams is essential for analyzing complex incident fields, understanding atomic responses, and enhancing applications such as attosecond-driven ultrafast processes. However, quantifying the wavefronts of each harmonic component remains a challenge. Here, we present a multicolor wavefront sensing for HHG beams using the Talbot effect, validated theoretically and experimentally. Our method reconstructs individual high-order harmonic wavefronts with a spectral resolution of 4.8 eV, capturing multiple harmonic wavelengths in a single scan along the propagation direction, without the need for additional spectrometers or complex calibrations. This approach surpasses conventional Talbot effect wavefront sensing, which typically relies on quasimonochromatic waves or wavelength-averaging. Additionally, we introduce a high-accuracy centroid detection strategy that reduces tracking errors to the thousandths of a pixel while maintaining high computational efficiency, paving the way for broader applications in imaging and metrology. Published by the American Physical Society 2024