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Multiphysics simulation of Maxwell’s and Landau–Lifshitz–Gilbert equations are performed to solve electromagnetic fields by considering the dynamics of magnetization. These equations are discretized, and the time-domain responses are computed using a finite-difference time-domain scheme. This study focuses on the acceleration of multiphysics simulations in terms of the multiscale modeling of the interaction between electromagnetic fields and magnetization. A nanoscale magnetic film is conducted to develop a method for measuring the magnetic properties using the near field of the magnetic film.

Nonstationary characteristics and lack of time delays analysis remain two large obstacles to diagnosing fault root cause in industrial processes. However, ignoring these two issues, many traditional methods tend to introduce some spurious or indirect causal relationships that interfere with root cause judgments. In this article, multiscale partial symbolic transfer entropy (MPSTE) is proposed to handle the above-mentioned problems. On the one hand, a multivariate multiscale filter module of MPSTE is designed to estimate the information transfer on multiple time scales and detect time delays while considering the indirect causality of the multivariate industrial system. On the other hand, to capture the causal information from the nonstationary processes, MPSTE introduces a segmental symbolization procedure and maps time series into finite rank vectors, overcoming the limitation of stationarity. Moreover, two causal criteria for MPSTE are constructed to identify the direction and significance level of causality correspondingly. MPSTE can thus provide sufficient information for causal relationships and exclude spurious causality. Finally, a causal diagram construction strategy with time delay based on MPSTE is established to diagnose the root cause and trace the fault propagation path. The results of experimental verification in a coal mill rig prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Accurate modeling of the coupling between mechanical and magnetic behavior is a key challenge for designing many electromagnetic devices. The requirements for such modeling are notably the ability to consider multiaxial configurations, thermodynamic consistency to allow the calculation of losses, and the implementability into structural analysis tools. So far, the modeling approaches available in the literature do not usually combine these three features simultaneously. In this paper, for the first time, the influence of mechanical stress on the magnetic hysteretic behavior is modeled through the association of a reversible simplified multiscale approach and a macroscopic energy-based magnetic hysteresis model in a vector-play form. A phenomenological description of the dissipation parameters under mechanical stress is proposed. The non-monotonic effect of tensile stress on the magnetic permeability is modeled using a second-order development in the magneto-elastic energy. Material parameters for both reversible and irreversible behavior are identified from experimental characterization under mechanical stress performed on a DC04 electrical steel. The experimental tests include anhysteretic and hysteretic measurements. Modeling results of the anhysteretic magnetic permeability, the coercive field, and the remanent induction under several levels of peak magnetic field and uniaxial mechanical stress are satisfactorily compared with those obtained experimentally. The model is shown to reasonably predict the hysteresis losses under tensile and compressive stress, as well as the response of the material under a complex magnetic field waveform with harmonic content.

DNA nanostructures offer promising applications, particularly in the biomedical domain, as they can be used for targeted drug delivery, construction of nanorobots, or as a basis for molecular motors. One of the most prominent techniques for assembling these structures is DNA origami. Nowadays, desktop applications are used for the in silico design of such structures. However, as such structures are often spatially complex, their assembly and analysis are complicated. Since virtual reality (VR) was proven to be advantageous for such spatial-related tasks and there are no existing VR solutions focused on this domain, we propose Vivern, a VR application that allows domain experts to design and visually examine DNA origami nanostructures. Our approach presents different abstracted visual representations of the nanostructures, various color schemes, and an ability to place several DNA nanostructures and proteins in one environment, thus allowing for the detailed analysis of complex assemblies. We also present two novel examination tools, the Magic Scale Lens and the DNA Untwister, that allow the experts to visually embed different representations into local regions to preserve the context and support detailed investigation. To showcase the capabilities of our solution, prototypes of novel nanodevices conceptualized by our collaborating experts, such as DNA-protein hybrid structures and DNA origami superstructures, are presented. Finally, the results of two rounds of evaluations are summarized. They demonstrate the advantages of our solution, especially for scenarios where current desktop tools are very limited, while also presenting possible future research directions. [ABSTRACT FROM AUTHOR]

In the electronic nose (e-nose), a stable feature representation of the gas sensor’s response is a key step to realize subsequent odor identification algorithms. However, the noises in gas sensors hinder the acquisition of such features. In order to solve this problem, this article proposes a stable feature extraction algorithm which takes the impulse response of the e-nose system as the feature. The impulse response is estimated from a nonparametric model constrained by a multiscale wavelet kernel regularization matrix. The kernel regularization matrix equips the proposed feature extraction method with an ability in resistance to random noise. A numerical experiment proves that compared with single-scale kernel regularization, the use of multiscale wavelet kernel helps to achieve more stable and accurate impulse response estimation. Then, a field experiment is conducted to demonstrate the performance of the proposed features. This experiment aims to identify four different whiskies measured by a self-designed e-nose with four commercial gas sensors. Under the framework of transfer learning, the classification result based on the proposed features outperforms those using other considered features. The accuracy of whisky identification reaches 92.00%, showing a good potential of applying the proposed feature representations in the area of e-noses. [ABSTRACT FROM AUTHOR]

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.g., topology-preserving. Moreover, these learning-based approaches typically pose hyper-parameter learning as a black-box problem and require considerable computational and human effort to perform many training runs. To tackle the aforementioned problems, we propose a new learning-based framework to optimize a diffeomorphic model via multi-scale propagation. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Further, we propose a new bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have recently been introduced to Alzheimer's Disease (AD) diagnosis. Despite their encouraging prospects, most of the existing models only process AD-related brain atrophy on a single spatial scale, and have high computational complexity. Here, we propose a novel Attention-based 3D Multi-scale CNN model (AMSNet), which can better capture and integrate multiple spatial-scale features of AD, with a concise structure. For the binary classification between 384 AD patients and 389 Cognitively Normal (CN) controls using sMRI scannings, AMSNet achieves remarkable overall performance (91.3% accuracy, 88.3% sensitivity, and 94.2% specificity) with fewer parameters and lower computational load, generally surpassing seven comparative models. Furthermore, AMSNet generalizes well in other AD-related classification tasks, such as the three-way classification (AD-MCI-CN). Our results manifest the feasibility and efficiency of the proposed multi-scale spatial feature integration and attention mechanism used in AMSNet for AD classification, and provide potential biomarkers to explore the neuropathological causes of AD. [ABSTRACT FROM AUTHOR]

This work presents an innovative model for the derivation of permittivity evolution of polyethylene (PE)-based materials with aging. First, the derivation of the microscale contributions to the real permittivity of methylene unit [constitutive repetitive unit (CRU) of PE] and its oxidation products, that is, ketones and hydroperoxides, in the solid state is presented. Then, a chemical kinetic model is recalled predicting the concentration, under proper hypotheses, of the oxidized species created during polymer aging. The proposed model combines the concentrations of methylene unit and its oxidation products with the respective contribution to permittivity, providing the trend of permittivity of polymer with aging. Results depict good agreement with the experimental data, validating the model. [ABSTRACT FROM AUTHOR]

Time-series forecasting is a key component in the automation and optimization of intelligent applications. It is not a trivial task, as there are various short-term and/or long-term temporal dependencies. Multiscale modeling has been considered as a promising strategy to solve this problem. However, the existing multiscale models either apply an implicit way to model the temporal dependencies or ignore the interrelationships between multiscale subseries. In this article, we propose a multiscale interactive recurrent network (MiRNN) to jointly capture multiscale patterns. MiRNN employs a deep wavelet decomposition network to decompose the raw time series into multiscale subseries. MiRNN introduces three key strategies (truncation, initialization, and message passing) to model the inherent interrelationships between multiscale subseries, as well as a dual-stage attention mechanism to capture multiscale temporal dependencies. Experiments on four real-world datasets demonstrate that our model achieves promising performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video self-supervised learning is a challenging task, which requires significant expressive power from the model to leverage rich spatial-temporal knowledge and generate effective supervisory signals from large amounts of unlabeled videos. However, existing methods fail to increase the temporal diversity of unlabeled videos and ignore elaborately modeling multi-scale temporal dependencies in an explicit way. To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and propose a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy. Specifically, a Spatial-Temporal Knowledge Discovering (STKD) module is first introduced to extract motion-enhanced spatial-temporal representations from videos based on the frequency domain analysis of discrete cosine transform. To explicitly model multi-scale temporal dependencies of unlabeled videos, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i.e., the intra-/inter-snippet Temporal Contrastive Graphs (TCG). Then, specific contrastive learning modules are designed to maximize the agreement between nodes in different graph views. To generate supervisory signals for unlabeled videos, we introduce an Adaptive Snippet Order Prediction (ASOP) module which leverages the relational knowledge among video snippets to learn the global context representation and recalibrate the channel-wise features adaptively. Experimental results demonstrate the superiority of our TCGL over the state-of-the-art methods on large-scale action recognition and video retrieval benchmarks. The code is publicly available at https://github.com/YangLiu9208/TCGL. [ABSTRACT FROM AUTHOR]

Seismic background noise often damages the desired signals, thereby resulting in some artifacts in the seismic imaging that follows. Since about 2016, some supervised-deep-learning methods have shown impressive performance in seismic data denoising, but they usually only consider single-scale features and neglect the multiscale strategy. To further reinforce their denoising performance, a novel multiscale convolutional neural network (CNN) combined with a spatial attention mechanism, called multiscale spatial attention denoising network (MSSA-Net), is proposed to tell weak reflected signals apart from strong seismic background noise. Unlike conventional single-scale CNNs, this proposed MSSA-Net can achieve the extraction of multiscale features, which is beneficial for the suppression of strong noise and the recovery of weak reflected signals. Specifically, MSSA-Net contains a principal denoising network and two auxiliary networks. The former utilizes the widen convolution composed of multiple parallel convolution layers with different kernel sizes to capture the informative multiscale features; the latter two leverage upsampling and downsampling to extract local fine and global coarse features, respectively. Furthermore, a spatial attention block is adopted to fuse these multiscale features, thereby distinguishing weak reflected signals from strong seismic background noise. Multiple experiments of synthetic and real seismic records demonstrate the effectiveness of MSSA-Net. In addition, compared with two classical single-scale CNNs, MSSA-Net performs better in signal recovery, indicating the positive effect of the multiscale strategy. [ABSTRACT FROM AUTHOR]

A multifield and multiscale coupling model was developed to simulate the wear process of solid armature in an electromagnetic railgun. The finite element method was used to solve the electromagnetic-thermal coupled field to obtain the 3-D distribution of temperature and Lorentz force in the railgun. Taking the temperature and Lorentz force as the input, the transient mechanical response described by a material point model was calculated on a finer scale than the finite element model. By tracing the movement of the material particles that compose the armature, the evolution details of the armature contact interface were obtained and the whole wear process was shown. The results suggested that both the melting erosion and the mechanical wear played important roles in the armature wear issue. [ABSTRACT FROM AUTHOR]

Recently, convolutional sparse coding (CSC) has shown great success in many image processing tasks, such as image super-resolution and image separation. However, it performs poorly in image denoising task. In this letter, we provide a new insight for CSC denoising by revisiting the CSC from a multi-scale perspective. We propose a multi-scale CSC model for image denoising. By unrolling the multi-scale solution into a learnable network, we obtain an interpretable lightweight multi-scale network, namely MCSCNet. Experimental results show that the proposed MCSCNet significantly advances the denoising performance, with an average PSNR improvement of 0.32 dB over the state-of-the-art (SOTA) CSC based method. In addition, our MCSCNet is on par with many SOTA deep learning based methods, with less network parameters and lower FLOPs. The ablation study also validates the effectiveness of the multi-scale CSC mechanism. [ABSTRACT FROM AUTHOR]

The maintenance of subway tunnels is vital to ensure the safety of their daily operation. Issues experienced by shield subway tunnels, especially the water leakages, require rapid and accurate detection and diagnosis. Due to the large number of disturbances in the tunnels, conventional algorithms face limitations when extracting discriminative features. To solve this problem, we propose a novel and efficient deep learning model for extracting multiscale and discriminative features of water leakages based on mobile laser scanning (MLS) point cloud intensity images. A new residual network module (Res2Net) is integrated with a cascade structure to form a unified model to extract the multiscale features of water leakages. The model can fully consider geometric characteristics of water leakages and grade the residual connections in a single residual block. This expands the size of receptive field in each network layer and can better facilitate the extraction of geometric characteristics of water leakages. Finally, we verify the advantages of the proposed method via experiments on five water leakage datasets of tunnel intensity images converted from point clouds obtained by a self-developed MLS system and compare its performance with other methods. [ABSTRACT FROM AUTHOR]

This article presents a nonlinear multi-scale model order reduction (MOR) method based on a piecewise linearization technique. On the material scale, the eddy-current (EC) field in laminated cores is expressed through Legendre polynomials. The Cauer ladder network (CLN) method is applied to the homogenized nonlinear EC problem to generate the equivalent electric circuit. The accuracy and efficiency of the proposed method are verified by the mean of a test case with saturated stacked core and pulse width modulation (PWM) excitation. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) cross-modal text–image retrieval has attracted extensive attention for its advantages of flexible input and efficient query. However, traditional methods ignore the characteristics of multiscale and redundant targets in RS image, leading to the degradation of retrieval accuracy. To cope with the problem of multiscale scarcity and target redundancy in RS multimodal retrieval task, we come up with a novel asymmetric multimodal feature matching network (AMFMN). Our model adapts to multiscale feature inputs, favors multisource retrieval methods, and can dynamically filter redundant features. AMFMN employs the multiscale visual self-attention (MVSA) module to extract the salient features of RS image and utilizes visual features to guide the text representation. Furthermore, to alleviate the positive samples ambiguity caused by the strong intraclass similarity in RS image, we propose a triplet loss function with dynamic variable margin based on prior similarity of sample pairs. Finally, unlike the traditional RS image-text dataset with coarse text and higher intraclass similarity, we construct a fine-grained and more challenging Remote sensing Image-Text Match dataset (RSITMD), which supports RS image retrieval through keywords and sentence separately and jointly. Experiments on four RS text–image datasets demonstrate that the proposed model can achieve state-of-the-art performance in cross-modal RS text–image retrieval task. [ABSTRACT FROM AUTHOR]

Great advances have been made with the lattice Boltzmann (LB) method for complicated fluid phenomena and fundamental thermal processes over the past three decades. This paper presents a systematic overview of the LB method from 1990 to 2018, based on bibliometric analysis and the Science Citation Index Expanded (SCI-E) database. The results show that China took the leading position in this field, followed by the USA and UK. The Chinese Academy of Sciences had the most publications, while the Los Alamos National Laboratory was first as far as highest average citation per paper and h-index are concerned. Physical Review E was the most productive journal and “Mechanics” was the most frequently used subject category. Keyword analysis indicated that recent research has focused on the natural convection and heat transfer of nanofluid or multiphase flow in complex porous media. Hydrothermal treatment of nanofluid with shape factor on the conditions, such as variable magnetic fields, thermal radiation and slipping boundary, were the research hotspots. Further research perspectives mainly explore the multiscale models for coupling multiple transport phenomena, morphology optimization of porous parameters, new nanoparticles with shape factor, multicomponent LB method considering Knudsen diffusion effect, LB-based hybrid methods, radiation performance or boiling-heat transfer of nanofluid, and the active control of droplets, may continue to attract more attention. Moreover, some new applications, such as phase change of metal foam, erosion induced by nanaofluid, anode circulating, 3D modeling in thermal systems with vibration, and magnetohydrodynamics microfluid devices, could be of interest going forward.

Moore’s law has remarkably promoted the development of semiconductor technology over the last decades. The advanced packaging involving novel materials, structures, and processes has contributed significantly to achieving excellent performance. Among various forms of advanced packaging, wafer-level-fan-out packaging is superior to standard wafer-level packaging in that it allows more input/output (I/O) connections, leading to landmark development for high-end devices. Furthermore, fan-out wafer-level package (FOWLP) technology enables heterogeneous integration of chiplets fabricated with different manufacturing processes, which provides integrated, sophisticated, and multifunctional dies with higher performance, lower cost, and tinier size. In packaging materials, processes, and structures, a thorough understanding of the process mechanics is necessary to ensure accuracy, while multiphysical field and multiscale modeling are beneficial to clarifying the internal mechanism of warpage. Process mechanics was applied to FOWLP and the characteristics were systematically expounded. Focusing on the warpage analysis of the embedded silicon fan-out (FO) wafer-level package, we introduced the principle of process mechanics and explored the material–structure–process interaction. The typical processes (including: 1) cleaning and litho cavity; 2) die attach and assignment; 3) dry film and dielectric layer 1 and patterning; 4) physical vapor deposition (PVD) seed layer 1 and patterning copper plating and redistribution layer (RDL) 1; 5) dielectric layer 2 and patterning; 6) PVD seed layer 2 and patterning copper plating and RDL 2; and 7) passivation layer, drop ball back grinding, and dicing) of the embedded silicon FO packaging are analyzed in detail to explore the warping evolution of individual processes. In addition, a material equivalent model of representative volume element (RVE) and an extended theoretical model of a multilayer structure were proposed. The comprehensive comparison for the extended theoretical model, experimental measurement, and finite element model (FEM) showed that the theoretical model is effective and relatively accurate. Overall, both the theoretical model and FEM are feasible approaches for predicting the warpage during the manufacturing process. [ABSTRACT FROM AUTHOR]

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, instrument transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results. [ABSTRACT FROM AUTHOR]

A multibranch Rao–Wilton–Glisson (MB-RWG) basis function is proposed in this article, which is composed of one positive triangle with a larger edge length and several negative triangles with smaller edge lengths. All negative triangles are aggregated to replace the negative triangle in the traditional RWG basis function, with exactly the same function expression defined on the triangles. The number of negative triangles can be changed in different mesh structures. Similar to traditional RWG basis functions, the proposed MB-RWG basis functions guarantee normal current continuity across the common edges. No line charges exist and the total charge on one MB-RWG is zero. MB-RWG basis functions can be used very conveniently to connect one surface with a coarse mesh scheme to another with a fine mesh scheme and can be flexibly applied in the domain decomposition method (DDM). Numerical results validate the accuracy and demonstrate the versatility of the proposed basis function in modeling multiscale perfect electrically conducting (PEC) objects. [ABSTRACT FROM AUTHOR]

Objective: To investigate the importance of membrane charge oscillations and redistribution in multi-compartmental ultrasonic neuromodulation (UNMOD) intramembrane cavitation models. Methods: The Neuronal Intramembrane Cavitation Excitation (NICE) model and multiScale Optimized model of Neuronal Intramembrane Cavitation (SONIC) of UNMOD are compared for a nanoscale multi-compartmental and point neuron approximation of the bilayer sonophore and surrounding proteins. The temporal dynamics of charge oscillations and their effect on the resulting voltage oscillations are investigated by fourier series analysis. Results: Comparison of excitation thresholds and neuronal response between nanoscale multi-compartmental and point models, implemented in the SONIC and NICE framework, demonstrates that the explicit modeling of fast spatial charge redistribution is critical for an accurate multi-compartmental UNMOD-model. Furthermore, the importance of modeling partial protein coverage is quantified by the excitability thresholds. Subsequently, we establish by fourier analysis that these charge oscillations are slowly changing in time. Conclusion: Fast charge redistribution significantly alters neuronal excitability in a multi-compartmental nanoscale UNMOD-model. Also the mutual exclusivity between protein and sonophore coverage should be taken into account, when simulating the dependency of neuronal excitability on coverage fractions. Charge oscillations are periodic and their fourier components change on a slow timescale. Furthermore, the resulting voltage oscillations decrease in energy with overtone number, implying that an extension of the existing multiscale model (SONIC) to multi-compartmental neurons is possible by taking into account a limited number of fourier components. Significance: First steps are taken towards a morphologically realistic and computationally efficient UNMOD-model, improving our understanding of the underlying ultrasonic neuromodulation mechanisms. [ABSTRACT FROM AUTHOR]

Objective: Purposes of this work were i) to develop an in silico model of tumor response to radiotherapy, ii) to perform an exhaustive sensitivity analysis in order to iii) propose a simplified version and iv) to predict biochemical recurrence with both the comprehensive and the reduced model. Methods: A multiscale computational model of tumor response to radiotherapy was developed. It integrated the following radiobiological mechanisms: oxygenation, including hypoxic death; division of tumor cells; VEGF diffusion driving angiogenesis; division of healthy cells and oxygen-dependent response to irradiation, considering, cycle arrest and mitotic catastrophe. A thorough sensitivity analysis using the Morris screening method was performed on 21 prostate computational tissues. Tumor control probability (TCP) curves of the comprehensive model and 15 reduced versions were compared. Logistic regression was performed to predict biochemical recurrence after radiotherapy on 76 localized prostate cancer patients using an output of the comprehensive and the reduced models. Results: No significant difference was found between the TCP curves of the comprehensive and a simplified version which only considered oxygenation, division of tumor cells and their response to irradiation. Biochemical recurrence predictions using the comprehensive and the reduced models improved those made from pre-treatment imaging parameters (AUC = 0.81 $\pm$ 0.02 and 0.82 $\pm$ 0.02 vs. 0.75 $\pm$ 0.03, respectively). Conclusion: A reduced model of tumor response to radiotherapy able to predict biochemical recurrence in prostate cancer was obtained. Significance: This reduced model may be used in the future to optimize personalized fractionation schedules. [ABSTRACT FROM AUTHOR]

A subgridding unconditionally stable finite-element time-domain method based on local eigenvalue solution (SUSL-FETD) is proposed to solve multiscale modeling. In this method, subgridding elements are used to discrete a multiscale computational domain, and the explicit central-difference method is applied for temporal discretization. Compared with traditional elements, the subgridding elements have a more complex distribution of edges and nodes. Therefore, an efficient subgridding scheme is given to form the element matrix for each subgridding element. Such system matrices assembled by all element matrices are symmetric. The subgridding FETD (S-FETD) is then further developed into the subgridding unconditionally stable FETD (SUS-FETD) by filtering spatial unstable modes. It is time-consuming to obtain the unstable modes by global eigenvalue decomposition. In this article, the local eigenvalue decomposition is proposed to get the unstable modes, which further reduces the memory storage and computing time. Numerical examples certify that the proposed SUSL-FETD has high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The success of convolutional neural network (CNN) based single image dynamic scene blind deblurring (SIDSBD) methods mainly stems from the multi-scale/multi-patch model and the designs of the encoder-decoder architecture, and the residual block structure, which make different contributions to SIDSBD. In this paper, we further exploit the advantages of the multi-scale model, the encoder-decoder module, and the residual block structure, respectively, and propose a novel multi-scale channel attention network (MSCAN) for effective single image dynamic scene blind deblurring. Different from existing multi-scale models, in our proposed network, each scale consists of multiple levels, in which a novel spatial pyramid pooling channel attention (SPPCA) strategy is proposed to adaptively rescale the channel-wise features by using both the global and local feature statistics for more powerful network representation. Extensive experiments on both the synthetic benchmark datasets and the real blurred images show that our method can produce better deblurring results than the state-of-the-art SIDSBD methods in terms of both qualitative evaluation and quantitative metrics. [ABSTRACT FROM AUTHOR]

A simulation chain, composed of GEANT4 and a kinetic Monte Carlo (KMC) algorithm, dedicated to study the annealing process of defects produced after energetic particle irradiation is presented. Results will be used to compute dark current nonuniformity (DCNU). [ABSTRACT FROM AUTHOR]

Modeling time series (TS) is a research focus in cryospheric sciences because of the complexity and multiscale nature of events of interest. Highly nonuniform sampling of measurements from different sensors with different levels of accuracy, as is typical for measurements of ice sheet elevations, makes the problem even more challenging. In this article, we propose a spline-based approximation framework (ALPS—Approximation by Localized Penalized Splines) for modeling TS of land ice changes. The localized support of the B-spline basis functions enable robustness to nonuniform sampling, a considerable improvement over other global and piecewise local models. With features like discrete-coordinate-difference-based penalization and two-level outlier detection, ALPS further guarantees the stability and quality of approximations. ALPS incorporates rigorous model uncertainty estimates with all approximations. As demonstrated by examples, ALPS performs well for a variety of data sets, including TS of ice sheet thickness, elevation, velocity, and terminus locations. The robust estimation of TS and their derivatives facilitates new applications, such as the reconstruction of high-resolution elevation change records by fusing sparsely sampled TS of ice sheet thickness changes with modeled firn thickness changes, and the analysis of the relationship between different outlet glacier observations to gain new insights into processes and forcing. [ABSTRACT FROM AUTHOR]

In deep learning (DL)-based multifocus image fusion, effective multiscale feature learning is a key issue to promote fusion performance. In this article, we propose a novel DL model named multiscale feature interactive network (MSFIN), which can segment the source images into focused and defocused regions accurately by sufficient interaction of multiscale features from layers of different depths in the network for multifocus image fusion. Specifically, based on the popular encoder–decoder framework, two functional modules, namely, multiscale feature fusion (MSFF) and coordinate attention upsample (CAU), are designed for interactive multiscale feature learning. Moreover, the weighted binary cross-entropy (WBCE) loss and the multilevel supervision (MLS) strategy are introduced to train the network more effectively. Qualitative and quantitative comparisons with 19 representative multifocus image fusion methods demonstrate that the proposed method can achieve state-of-the-art performance. The code of our method is available at https://github.com/yuliu316316/MSFIN-Fusion. [ABSTRACT FROM AUTHOR]

Multiple ship tracking (MST) is an important task in marine surveillance and ship situational awareness systems. Considerable work has been conducted on multiple object tracking in recent years, but it has focused primarily on pedestrians and automobiles, leaving a gap in studies on MST due to the particularities of complex marine scenes, such as ship scale variations, the long-tailed distribution of ships, and long-term occlusions caused by ship movements. In this article, we present a robust deep affinity network (RoDAN) for MST. To overcome the above difficulties in MST, we start with the basic deep affinity network (DAN) and improve it in three aspects: scale, region, and motion. For the scale dimension, we integrate an atrous spatial pyramid pooling (ASPP) module to improve the modeling ability for multiscale ships. For the region dimension, we propose the joint global region modeling (JGRM) module, which further strengthens the modeling ability of DAN and exploit it to overcome the long-tailed distribution property of ships. For the motion dimension, we propose the motion-matching optimization (MMO) module to fine-tune the tracking results and make our tracker more robust, less reliant on the front-end detector, and ameliorate long-term occlusions. The experimental results demonstrate that our MST method outperforms the state-of-the-art methods. In particular, it reduces the number of ID switches (IDSs) and trajectory fragmentations (FMs), achieving holistically preferable performance. Meanwhile, our method achieves a comparable speed. [ABSTRACT FROM AUTHOR]

In this work, we comprehensively investigate the performance of CMOS inverters based on 2-D materials (2DMs), such as MoS2, WSe2, WS2, black phosphorus (BP), WSe2-MoS2, and benchmark against their silicon (Si) counterpart for sub-10-nm channel length. The performance evaluation of the 2DM-based CMOS inverters is done using an in-house developed multiscale modeling approach, which translates the atomistic device model into the professional circuit simulation using the Verilog-AMS interface. Among 2DM-based inverters, heterogeneous WSe2-MoS2 inverter configuration exhibits excellent switching characteristics for 5.6 nm and beyond channel length with a larger static noise margin, nanowatt-order power dissipation, and comparative speed to Si-based inverter. Despite lower noise margins and higher power dissipation, Si-based inverter, with lower gate capacitance, allows marginally higher speed than that of 2DM-based inverters. Furthermore, at 3-nm channel length, static and dynamic performance metrics of inverter degrade significantly due to more pronounced short-channel effects; however, MoS2-based inverter demonstrates a good functionality. The performance analysis and benchmarking show promise and opportunities with 2DM-based devices for future logic applications; however, optimizing the contact resistance, parasitic capacitances, and channel length are the key device design parameters in developing the high-performance CMOS inverter. [ABSTRACT FROM AUTHOR]

Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks. [ABSTRACT FROM AUTHOR]

This article develops a multi-scale model order reduction (MOR) method including both material and machine scale MOR. The material-scale MOR homogenizes the eddy-current (EC) field in laminated cores using the Legendre expansion of magnetic induction along the stacking direction within each steel sheet. The machine-scale MOR represents the EC field in electromagnetic devices with their equivalent Cauer ladder network (CLN). The former MOR is built into the latter by applying the CLN procedure to the homogenized EC field equation. [ABSTRACT FROM AUTHOR]

Integration-technology feature shrink increases computing-system susceptibility to single-event effects (SEE). While modeling SEE faults will be critical, an integrated processor’s scope makes physically correct modeling computationally intractable. Without useful models, presilicon evaluation of fault-tolerance approaches becomes impossible. To incorporate accurate transistor-level effects at a system scope, we present a multiscale simulation framework. Charge collection at the 1) device level determines 2) circuit-level transient duration and state-upset likelihood. Circuit effects, in turn, impact 3) register-transfer-level architecture-state corruption visible at 4) the system level. Thus, the physically accurate effects of SEEs in large-scale systems, executed on a high-performance computing (HPC) simulator, could be used to drive cross-layer radiation hardening by design. We demonstrate the capabilities of this model with two case studies. First, we determine a D flip-flop’s sensitivity at the transistor level on 14-nm FinFet technology, validating the model against published cross sections. Second, we track and estimate faults in a microprocessor without interlocked pipelined stages (MIPS) processor for Adams 90% worst case environment in an isotropic space environment. [ABSTRACT FROM AUTHOR]

In this article, an automatic load balancing method for the parallel subdomain-level discontinuous Galerkin time-domain (DGTD) method has been proposed based on the message passing interface (MPI) library. First, a diagram that exhibits the relationship between the computation load (CPU time) and the DoFs has been extracted from numerical experiments for hexahedral and tetrahedral meshes, respectively. Then, an automatic iteration load balancing scheme has been employed to optimize the positions of the interfaces in order to minimize the CPU time differences between subdomains with the help of the diagram obtained in the first step. This scheme comes with a one-click solution using a greedy algorithm after an initial division of the original model, which is the only step that requires an external operation. In addition, the whole load balancing scheme is only involved in the preprocessing part of the code without modifying the system matrices iteratively. Consequently, the load balance can be relatively easily and efficiently implemented, which can significantly expedite the simulation of the large/multiscale models. The effectiveness of this load balancing strategy has been demonstrated via numerical cases. [ABSTRACT FROM AUTHOR]

This review highlights the updated picture of charge transport in polymer dielectrics and offers suggestions for future research. It begins with the backgrounds of the various theoretical charge transport models and the underlying concepts of the physical quantities. The techniques for deriving the microscopic physical quantities, e.g., the Marcus parameters and the spectral density of bath, from first-principles methods are outlined for tutorial purposes. Our recent multi-scale simulation studies are presented to demonstrate how and why the macroscopic charge transfer behavior can be predicted from these microscopic quantities, without the use of phenomenological/ empirical models nor ad hoc parameters. Some new insights gained through the multiscale modeling technique are summarized, with a focus on the relation between the chemical structure of polymers and the carrier transport properties. Finally, what is further required for computer-aided (rational) dielectric polymer design is discussed. [ABSTRACT FROM AUTHOR]

Human parsing, especially in the wild, has attracted a lot of attention due to its great potential in many real-world applications. The Pyramid Spatial Parsing (PSP) module has shown superior performances in scene and human parsing tasks. However, the basic AvgPool operation in PSP equally aggregates spatial clues of a local region, and thus mixes up influences of different human parts presented in this region. It results in failures in capturing useful contexts relevant to parsing different parts. To address this problem, a suitable mechanism to collect spatial clues aligning with different human parts is proposed in this paper. We employ a Gather-Excite (GE) operation, a replacement of the AvgPool-Upsample operation in a pyramidical structure, to accurately reflect relevant human parts of various scales. The GE operation contains two steps: the gather operation that adaptively aggregates spatial clues to relevant human parts, and the excite operation that generates new feature maps with the gathered contextual information. This results in a novel Pyramidical Gather-Excite Context (PGEC) module to solve the multi-scale problem and parse person at various scales. The PGEC module is composed of multiple GE operations with different spatial extents and aggregates local and global spatial clues for better modeling multi-scale contextual information in parallel. Moreover, we integrate the PGEC module with fine-grained details, edge preserving module and deep supervision to formulate a novel PGEC Network (PGECNet) for human parsing. The proposed PGECNet has achieved state-of-the-art performance on four single-person human parsing datasets (i.e., LIP, PPSS, ATR and Fashion Clothing) and two multi-person human parsing datasets (i.e., PASCAL-Person-Part and CIHP). The experimental results show that the proposed PGEC is superior to the PSP and ASPP modules especially in single-human parsing task. The source code is publicly available at https://github.com/31sy/PGECNet. [ABSTRACT FROM AUTHOR]

The scene graph generated for an image structurally represents its object interactions and it substantially aids image scene understanding. To the best of our knowledge, most current works on scene graph generation chiefly focus on pairwise object regions for object and relation inference while ignoring the global visual context outside of these regions. Guided by the intuition that object/relation inference can benefit from the visual context within an image, this paper proposes a multi-scale context modeling method, which can jointly discover and integrate the complementary object-centric and region-centric context for scene graph inference. While both the object-centric and region-centric contexts are separately modeled by their individual modules, a bi-directional message propagation strategy is designed to mutually reinforce the context modeling. A context-fused inference is then proposed to integrate the multi-scale context to guide scene graph inference. Extensive experiments establish that this method can achieve competitive performance compared to the state-of-the-art methods on three benchmarks. Additional ablation studies further validate its effectiveness. Code has been made available at: https://github.com/ningxu1990/MSCM. [ABSTRACT FROM AUTHOR]

Due to the deteriorated conditions of illumination lack and uneven lighting, the performance of traditional background modeling methods is greatly limited for the surveillance of nighttime video. To make background modeling under nighttime scene performs as well as in daytime condition, we put forward a promising generation-based background modeling framework for foreground surveillance. With a pre-specified daytime reference image as background frame, the GAN based generation model, called N2DGAN, is trained to transfer each frame of nighttime video to a virtual daytime image with the same scene to the reference image except for the foreground part. Specifically, to balance the preservation of background scene and the foreground object(s) in generating the virtual daytime image, we presented a two-pathway generation model, in which the global and local sub-networks were well combined with spatial and temporal consistency constraints. For the sequence of generated virtual daytime images, a multi-scale Bayes model was further proposed to characterize pertinently the temporal variation of background. We manually labeled ground truth on the collected nightime video datasets for performance evaluation. The impressive results illustrated in both the main paper and supplementary show the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the joint kinematics, kinetics, and soft tissue mechanical responses is essential in the evaluation of musculoskeletal (MS) disorders. Since in vivo measurement of these quantities requires invasive methods, musculoskeletal finite element (MSFE) models are widely used for simulations. There are, however, limitations in the current approaches. Sequentially linked MSFE models benefit from complex MS and FE models; however, MS model’s outputs are independent of the FE model calculations. On the other hand, due to the computational burden, embedded (concurrent) MSFE models are limited to simple material models and cannot estimate detailed responses of the soft tissue. Thus, first we developed a MSFE model of the knee with a subject-specific MS model utilizing an embedded 12 degrees of freedom (DoFs) knee joint with elastic cartilages in which included both secondary kinematic and soft tissue deformations in the muscle force estimation (inverse dynamics). Then, a muscle-force-driven FE model with fibril-reinforced poroviscoelastic cartilages and fibril-reinforced poroelastic menisci was used in series to calculate detailed tissue mechanical responses (forward dynamics). Second, to demonstrate that our workflow improves the simulation results, outputs were compared to results from the same FE models which were driven by conventional MS models with a 1 DoF knee, with and without electromyography (EMG) assistance. The FE model driven by both the embedded and the EMG-assisted MS models estimated similar results and consistent with experiments from literature, compared to the results estimated by the FE model driven by the MS model with 1 DoF knee without EMG assistance. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness. [ABSTRACT FROM AUTHOR]

An improved memory-efficient subdomain level discontinuous Galerkin time domain (SL-DGTD) method, which utilizes the repetition of the geometries, has been proposed to model the periodic and aperiodic structures with square uniform lattice. Significant reduction of memory consumption has been achieved comparing to the conventional SL-DGTD approach. Based on the previous pioneering work of memory-efficient SL-DGTD method, two improvements have been implemented to extend the scope of application. First, multiple kinds of cells, which can be placed arbitrarily on a square lattice, are allowed after a generalized interface recognition scheme. Therefore, besides the large finite periodic arrays successfully solved by previous memory-efficient SL-DGTD method, the aperiodic and/or the quasi-periodic array can be simulated with much less memory as well. Second, certain part of a cell can be cut out to form a new cell. In other words, the cells can be embedded hierarchically, and this is crucial for an efficient modeling of multiscale structures. The effectiveness and efficiency of these improvements has been validated via modeling both proof-of-idea cases and patch antenna arrays with satisfactory reduction in memory consumption. [ABSTRACT FROM AUTHOR]

Given a video sequence, video frame interpolation aims to synthesize an in-between frame of two consecutive frames. In this paper, we propose a multi-scale position feature transform (MS-PFT) network for video frame interpolation where two parallel prediction networks and one optimization network are designed to predict the features of target frame and generate the final interpolation result, respectively. To increase the fidelity of the synthesised frames, we propose to apply a position feature transform (PFT) layer in the residual blocks of the prediction networks to estimate scaling factors which help evaluate different degrees of the importance of deep features around a target pixel. A PFT layer utilizes optical flow to extract and generate position features and then adjusts the learning process of our model. We further extend our model into a multi-scale structure in which each scale of the network shares the same parameters to maximise the efficiency of our network with model size unchanged. The experiments show that our method can handle the challenging scenarios like occlusion and large motion effectively and the proposed method outperforms those state-of-the-art approaches on different datasets. [ABSTRACT FROM AUTHOR]

A recursive domain decomposition approach based on a 2-D time-harmonic finite element (FE) model with AVI formulation is used to model a wireless power transfer (WPT) unit with litz wires. Similar techniques exist in the literature, but they have not yet been applied to WPT units. The approach produces a model that is significantly faster to update and resolve than a traditional FE model, and hence, it is suitable for performing parametric sweeps where the positioning of the coils is varied. Using the technique, it is possible to study losses emerging in individual strands even with an extremely high number of strands. The loss distribution between the litz wire strands is studied, varying the number of strands from 7 to 925. The results and speed are compared with a traditional FE AVI model. The method yields the results up to a 1% relative error compared with the traditional model with significantly faster simulation time. [ABSTRACT FROM AUTHOR]

The advent of blockchain technology can refine the concept of DTs by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in industrial sectors. A DT is an integrated multiphysics, multiscale, and probabilistic simulation, representation, and mirroring of a real-world physical component. The DTs help to visualize designs in 3D, perform tests and simulations virtually prior to creation of any physical component, and consequently play a vital role in sustaining and maintaining Industry 4.0. It is anticipated that DTs will become prevalent in the foreseeable future because they can be used for configuration, monitoring, diagnostics, and prognostics. This article envisages how blockchain can reshape and transform DTs to bring about secure manufacturing that guarantees traceability, compliance, authenticity, quality, and safety. We discuss several benefits of employing blockchain in DTs. We taxonomize the DTs literature based on key parameters (e.g., DTs levels, design phases, industrial use cases, key objectives, enabling technologies, and core applications). We provide insights into ongoing progress made towards DTs by presenting recent synergies and case studies. Finally, we discuss open challenges that serve as future research directions. [ABSTRACT FROM AUTHOR]

A decomposed discrete-time model of the dual active bridge (DAB) converter is proposed in this article, which has significantly simplified the conventional discrete-time model without affecting the calculation accuracy of the exponential matrix. Therefore, the decomposed discrete-time model is also applied to the design of the digital controller to compensate the digital control delay, which could deteriorate the stability of the system. Besides, the comprehensive and systematic analysis of all the possible unstable phenomena of the DAB converter is conducted to reveal their intrinsic mechanism. The corresponding multiscale oscillations and the sudden jump phenomena are also presented to exhibit their dynamic characteristics. In addition, the closed-form expressions of the state variables are obtained, which are utilized to explicitly express the analytical relationships among the state variables, system parameters, and the Floquet multipliers. Accordingly, the parameter spaces of the selected crucial system parameters are obtained, which can provide the guidance for the parameters design of the DAB converter. [ABSTRACT FROM AUTHOR]

A pixel-wise classification for high-resolution (HR) synthetic aperture radar (SAR) images is a challenging task, due to the limited availability of labeled SAR data, as well as the difficulty of exploring context information affected by coherent speckle. In this article, we propose a novel supervised classification method for HR SAR images, which combines a context-aware encoder network (CAEN) and a hybrid conditional random field (HCRF) model. First, a new CAEN architecture is developed based on the intrinsic property of HR SAR pixel-wise labeling. The proposed architecture follows an encoder–decoder structure, wherein the residual context encoder (RCE) block and the global context-aware (GCA) block are proposed in the encoder module to capture local to global semantic contexts. The multiscale skip connections and feature compression structures are designed in the decoder module to preserve precise object structures while improving computational efficiency. Then, a patch sampling strategy is adopted to ensure that the training and test data are completely separated. It can achieve a less-biased estimate of the test error of CAEN. In addition, the overlapped sampling and data augmentation techniques are used to solve the problem of limited labeled data. Finally, the HCRF model is constructed and combined with the previous CAEN to further enhance the spatial label consistency. Our HCRF integrates pixel-level and region-level potentials into a unified Bayesian framework, making two spatial supports come to a more accurate decision on pixel categories. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms. [ABSTRACT FROM AUTHOR]

Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this paper we present a novel data dependent multiscale model for hyperspectral unmixing accounting for spectral variability. The new method incorporates spatial contextual information to the abundances in extended linear mixing models by using a multiscale transform based on superpixels. The proposed method results in a fast algorithm that solves the abundance estimation problem only once in each scale during each iteration. Simulation results using synthetic and real images compare the performances, both in accuracy and execution time, of the proposed algorithm and other state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel residual attentive learning network architecture for predicting dynamic eye-fixation maps. The proposed model emphasizes two essential issues, i.e., effective spatiotemporal feature integration and multi-scale saliency learning. For the first problem, appearance and motion streams are tightly coupled via dense residual cross connections, which integrate appearance information with multi-layer, comprehensive motion features in a residual and dense way. Beyond traditional two-stream models learning appearance and motion features separately, such design allows early, multi-path information exchange between different domains, leading to a unified and powerful spatiotemporal learning architecture. For the second one, we propose a composite attention mechanism that learns multi-scale local attentions and global attention priors end-to-end. It is used for enhancing the fused spatiotemporal features via emphasizing important features in multi-scales. A lightweight convolutional Gated Recurrent Unit (convGRU), which is flexible for small training data situation, is used for long-term temporal characteristics modeling. Extensive experiments over four benchmark datasets clearly demonstrate the advantage of the proposed video saliency model over other competitors and the effectiveness of each component of our network. Our code and all the results will be available at https://github.com/ashleylqx/STRA-Net. [ABSTRACT FROM AUTHOR]

Objective: We propose a new bidimensional entropy measure and its multiscale form and evaluate their behavior using various synthetic and real images. The bidimensional multiscale measure finds application in helping clinicians for pseudoxanthoma elasticum (PXE) detection in dermoscopic images. Method: We developed bidimensional fuzzy entropy ($FuzEn_{2D}$) and its multiscale extension ($MSF_{2D}$) and then evaluated them on a set of synthetic images and texture datasets. Afterwards, we applied $ MSF_{2D}$ to dermoscopic PXE images and compared the results to those obtained by bidimensional multiscale sample entropy ($MSE_{2D}$). Results: The results for the synthetic images illustrate that $FuzEn_{2D}$ has the ability to quantify images irregularity. Moreover, $FuzEn_{2D}$ , compared with bidimensional sample entropy ($SampEn_{2D}$), leads to more stable results. The tests with the multiscale version show that $ MSF_{2D}$ is a proper image complexity measure. When applied to the dermoscopic PXE images, the paired t-test illustrates a significant statistical difference between $ MSF_{2D}$ of neck images with papules and normal skin images at a couple of scale factors. Conclusion: The results for the synthetic data illustrate that $FuzEn_{2D}$ is an image irregularity measure that overcomes $SampEn_{2D}$ in terms of reliability, especially for small-sized images, and stability of results. The results for the PXE dermoscopic images demonstrate the ability of $MSF_{2D}$ to recognize dermoscopic images of normal zones from PXE papules zones with a large effect size. Significance: This work introduces new image irregularity and complexity measures and shows the potential for $ MSF_{2D}$ to serve as a possible tool helping medical doctors in PXE diagnosis. [ABSTRACT FROM AUTHOR]