Multi-scale Resolution Research Articles

Sigmoid Functions, Multiscale Resolution of Singularities, and $hp$-Mesh Refinement

Sigmoid Functions, Multiscale Resolution of Singularities, and $hp$-Mesh Refinement

MPCCN: A Symmetry-Based Multi-Scale Position-Aware Cyclic Convolutional Network for Retinal Vessel Segmentation

In medical image analysis, precise retinal vessel segmentation is crucial for diagnosing and managing ocular diseases as the retinal vascular network reflects numerous health indicators. Despite decades of development, challenges such as intricate textures, vascular ruptures, and undetected areas persist, particularly in accurately segmenting small vessels and addressing low contrast in imaging. This study introduces a novel segmentation approach called MPCCN that combines position-aware cyclic convolution (PCC) with multi-scale resolution input to tackle these challenges. By integrating standard convolution with PCC, MPCCN effectively captures both global and local features. A multi-scale input module enhances feature extraction, while a weighted-shared residual and guided attention module minimizes background noise and emphasizes vascular structures. Our approach achieves sensitivity values of 98.87%, 99.17%, and 98.88%; specificity values of 98.93%, 97.25%, and 99.20%; accuracy scores of 97.38%, 97.85%, and 97.75%; and AUC values of 98.90%, 99.15%, and 99.05% on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. In addition, it records F1 scores of 90.93%, 91.00%, and 90.55%. Experimental results demonstrate that our method outperforms existing techniques, especially in detecting small vessels.

A New Insight on the Upwelling along the Atlantic Iberian Coasts and Warm Water Outflow in the Gulf of Cadiz from Multiscale Ultrahigh Resolution Sea Surface Temperature Imagery

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction.

ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching.

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data. We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes. The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

Unrecoverable lattice rotation governs structural degradation of single-crystalline cathodes.

Transitioning from polycrystalline to single-crystalline nickel-rich cathodes has garnered considerable attention in both academia and industry, driven by advantages of high tap density and enhanced mechanical properties. However, cathodes with high nickel content (>70%) suffer from substantial capacity degradation, which poses a challenge to their commercial viability. Leveraging multiscale spatial resolution diffraction and imaging techniques, we observe that lattice rotations occur universally in single-crystalline cathodes and play a pivotal role in the structure degradation. These lattice rotations prove unrecoverable and govern the accumulation of adverse lattice distortions over repeated cycles, contributing to structural and mechanical degradation and fast capacity fade. These findings bridge the previous knowledge gap that exists in the mechanistic link between fast performance failure and atomic-scale structure degradation.

Multi-Scale Adaptive Feature Network Drainage Pipe Image Dehazing Method Based on Multiple Attention

Drainage pipes are a critical component of urban infrastructure, and their safety and proper functioning are vital. However, haze problems caused by humid environments and temperature differences seriously affect the quality and detection accuracy of drainage pipe images. Traditional repair methods are difficult to meet the requirements when dealing with complex underground environments. To solve this problem, we researched and proposed a dehazing method for drainage pipe images based on multi-attention multi-scale adaptive feature networks. By designing multiple attention and adaptive modules, the network is able to capture global features with multi-scale resolution in complex underground environments, thereby achieving end-to-end dehazing processing. In addition, we also constructed a large drainage pipe dataset containing tens of thousands of clear/hazy image pairs of drainage pipes for network training and testing. Experimental results show that our network exhibits excellent dehazing performance in various complex underground environments, especially in the real scene of urban underground drainage pipes. The contributions of this paper are mainly reflected in the following aspects: first, a novel multi-scale adaptive feature network based on multiple attention is proposed to effectively solve the problem of dehazing drainage pipe images; second, a large-scale drainage pipe data is constructed. The collection provides valuable resources for related research work; finally, the effectiveness and superiority of the proposed method are verified through experiments, and it provides an efficient solution for dehazing work in scenes such as urban underground drainage pipes.

Multiscale reconfiguration induced highly saturated poling in lead-free piezoceramics for giant energy conversion

The development of high-performance lead-free K0.5Na0.5NbO3-based piezoceramics for replacing commercial lead-containing counterparts is crucial for achieving environmentally sustainable society. Although the proposed new phase boundaries (NPB) can effectively improve the piezoelectricity of KNN-based ceramics, the difficulty of achieving saturated poling and the underlying multiscale structures resolution of their complex microstructures are urgent issues. Here, we employ a medium entropy strategy to design NPB and utilize texture engineering to induce crystal orientation. The developed K0.5Na0.5NbO3-based ceramics enjoys both prominent piezoelectric performance and satisfactory Curie temperature, thus exhibiting an ultrahigh energy harvesting performance as well as excellent transducer performance, which is highly competitive in both lead-free and lead-based piezoceramics. Comprehensive structural analysis have ascertained that the field-induced efficient multiscale polarization configurations irreversible transitions greatly encourages high saturated poling. This study demonstrates a strategy for designing high-performance piezoceramics and establishes a close correlation between the piezoelectricty and the underlying multiscale structures.

Automated ocean front feature mapping using Sentinel-1 with examples from the Gulf Stream

ABSTRACT This study assessed the ability of Sentinel-1 radial velocity (RVL) products to mark the position of ocean current front features, using the Gulf Stream (GS) as a case study. RVL-derived front features were compared to fronts derived from Multi-scale Ultra-high Resolution Sea Surface Temperature Analysis (MURSST) data. A ridge filter was used to find fronts in both the Sea Surface Temperature (SST) and RVL data, and the similarity between each pair of fronts was measured using the discrete Hausdorff Distance (HD) and Mean Hausdorff Distance (MHD). Front features were correctly identified in concurrent SST and RVL data pairs in 65% of cases. The automatic front extraction sometimes failed by misclassifying an eddy or similar ocean feature as the ocean current in either the RVL or SST image, and sometimes failed to extract the entire length of the front visible within the image. SST and RVL fronts were classified manually to determine the success rate of the automatic front extraction, and to exclude front extraction errors from further analysis. The results demonstrated that RVL products were effective at determining the location of ocean fronts where the angle between the front’s normal vector and the sensor’s azimuthal heading is less than ~ 40°. A mean HD of 25.5 km and a mean MHD of 10.8 km was calculated for all front pairs in the study area.

PS5-Net: a medical image segmentation network with multiscale resolution.

In recent years, the continuous advancement of convolutional neural networks (CNNs) has led to the widespread integration of deep neural networks as a mainstream approach in clinical diagnostic support. Particularly, the utilization of CNN-based medical image segmentation has delivered favorable outcomes for aiding clinical diagnosis. Within this realm, network architectures based on the U-shaped structure and incorporating skip connections, along with their diverse derivatives, have gained extensive utilization across various medical image segmentation tasks. Nonetheless, two primary challenges persist. First, certain organs or tissues present considerable complexity, substantial morphological variations, and size discrepancies, posing significant challenges for achieving highly accurate segmentation. Second, the predominant focus of current deep neural networks on single-resolution feature extraction limits the effective extraction of feature information from complex medical images, thereby contributing to information loss via continuous pooling operations and contextual information interaction constraints within the U-shaped structure. We proposed a five-layer pyramid segmentation network (PS5-Net), a multiscale segmentation network with diverse resolutions that is founded on the U-Net architecture. Initially, this network effectively leverages the distinct features of images at varying resolutions across different dimensions, departing from prior single-resolution feature extraction methods to adapt to intricate and variable segmentation scenarios. Subsequently, to comprehensively integrate feature information from diverse resolutions, a kernel selection module is proposed to assign weights to features across different dimensions, enhancing the fusion of feature information from various resolutions. Within the feature extraction network denoted as PS-UNet, we preserve the classical structure of the traditional U-Net while enhancing it through the incorporation of dilated convolutions. PS5-Net attains a Dice score of 0.9613 for liver segmentation on the CHLISC dataset and 0.8587 on the ISIC2018 dataset for skin lesion segmentation. Comparative analysis with diverse medical image segmentation methodologies in recent years reveals that PS5-Net has achieved the highest scores and substantial advancements. PS5-Net effectively harnesses the rich semantic information available at different resolutions, facilitating a comprehensive and nuanced understanding of the input medical images. By capitalizing on global contextual connections, the network adeptly captures the intricate interplay of features and dependencies across the entire image, resulting in more accurate and robust segmentation outcomes. The experimental validation of PS5-Net underscores its superior performance in medical image segmentation tasks, offering promising prospects for enhancing diagnostic and analytical processes within clinical settings. These results highlight the potential of PS5-Net to significantly contribute to the advancement of medical imaging technologies and ultimately improve patient care through more precise and reliable image analysis.

Unifying temperature definition in atomistic and field representations of conservation laws

In this work, a field representation of the conservation law of linear momentum is derived from the atomistic, using the theory of distributions as the mathematical tool, and expressed in terms of temperature field by defining temperature as a derived quantity as that in molecular kinetic theory and atomistic simulations. The formulation leads to a unified atomistic and continuum description of temperature and a new linear momentum equation that, supplemented by an interatomic potential, completely governs thermal and mechanical processes across scales from the atomic to the continuum. The conservation equation can be used to solve atomistic trajectories for systems at finite temperatures, as well as the evolution of field quantities in space and time, with atomic or multiscale resolution. Four sets of numerical examples are presented to demonstrate the efficacy of the formulation in capturing the effect of temperature and thermal fluctuations, including phonon density of states, thermally activated dislocation motion, dislocation formation during epitaxial processes, and attenuation of longitudinal acoustic waves as a result of their interaction with thermal phonons.

Multi-Scale Image- and Feature-Level Alignment for Cross-Resolution Person Re-Identification

Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models cannot completely compensate for the missing information in the LR images, there is still a large gap between the HR image recovered from the LR images and the real HR images. To tackle this challenge, we propose a novel Multi-Scale Image- and Feature-Level Alignment (MSIFLA) framework to align the images on multiple resolution scales at both the image and feature level. Specifically, (i) we design a Cascaded Multi-Scale Resolution Reconstruction (CMSR2) module, which is composed of three cascaded Image Reconstruction (IR) networks, and can continuously reconstruct multiple variables of different resolution scales from low to high for each image, regardless of image resolution. The reconstructed images with specific resolution scales are of similar distribution; therefore, the images are aligned on multiple resolution scales at the image level. (ii) We propose a Multi-Resolution Representation Learning (MR2L) module which consists of three-person re-ID networks to encourage the IR models to preserve the ID-discriminative information during training separately. Each re-ID network focuses on mining discriminative information from a specific scale without the disturbance from various resolutions. By matching the extracted features on three resolution scales, the images with different resolutions are also aligned at the feature-level. We conduct extensive experiments on multiple public cross-resolution person re-ID datasets to demonstrate the superiority of the proposed method. In addition, the generalization of MSIFLA in handling cross-resolution retrieval tasks is verified on the UAV vehicle dataset.

Wind turbine gearbox multi-scale condition monitoring through operational data

Since wind is expected to play a crucial role on the worldwide electricity production scenario, the reliability of the turbines is attracting attention of both industry and academia. New techniques for efficient condition monitoring of key components can be fundamental to optimising the performance and maintenance of a large fleet of turbines. The gearbox and bearings are the most critical mechanical components as they are responsible for a large proportion of the downtime of a wind turbine over its lifetime. However, the monitoring of wind turbine gearboxes is challenging due to the non-stationary nature of the operation and the lack of noise-free vibration measurements. In the present work, a new approach for efficient long to short term monitoring of wind turbine gearboxes has been developed based on real data. An turbine drivetrain failure was used as a test case to develop a new approach based on the use of multi-scale data sources. On the one hand, SCADA (Supervisory Control And Data Acquisition) data were used for general monitoring of the condition of the machine component on long to medium term time scales, while on the other hand, high resolution, triggered event data collected by a CMS (Condition Monitoring System) were used to refine the diagnosis and prognosis of the fault on a shorter time scale. Even though triggered spot events are very difficult to manage, the results show that the use of multi-scale high resolution CMS data can be fast and useful in fault diagnosis to classify a target machine with a healthy reference one. In the present work, the one-class SVM (Support Vector Method) was used for novelty detection. The approach, when applied to all available time scales, can be very precise in detecting the faulty machine and can therefore be proposed as a fast detection approach requiring less data compared to the classical data-driven regression normal behaviour model developed with continuously available SCADA data.

Old school, new rules: floral meristem development revealed by 3D gene expression atlases and high-resolution transcription factor-chromatin dynamics.

The intricate morphology of the flower is primarily established within floral meristems in which floral organs will be defined and from where the developing flower will emerge. Floral meristem development involves multiscale-level regulation, including lineage and positional mechanisms for establishing cell-type identity, and transcriptional regulation mediated by changes in the chromatin environment. However, many key aspects of floral meristem development remain to be determined, such as: 1) the exact role of cellular location in connecting transcriptional inputs to morphological outcomes, and 2) the precise interactions between transcription factors and chromatin regulators underlying the transcriptional networks that regulate the transition from cell proliferation to differentiation during floral meristem development. Here, we highlight recent studies addressing these points through newly developed spatial reconstruction techniques and high-resolution transcription factor-chromatin environment interactions in the model plant Arabidopsis thaliana. Specifically, we feature studies that reconstructed 3D gene expression atlases of the floral meristem. We also discuss how the precise timing of floral meristem specification, floral organ patterning, and floral meristem termination is determined through temporally defined epigenetic dynamics for fine-tuning of gene expression. These studies offer fresh insights into the well-established principles of floral meristem development and outline the potential for further advances in this field in an age of integrated, powerful, multiscale resolution approaches.

Learning to self-adaptive robust dehazing network for video detection in drainage pipeline

With the continuous urbanization and expansion of the built-up area of cities, the requirement for regular maintenance of urban underground drainage pipeline will be further increased. However, the drainage pipeline environment is humid and the water quality conditions are complex, which leads to the existence of uneven concentration of haze in the images captured by CCTV (closed-circuit television). To address these problems, this paper suggests a self-adaptive robust dehazing network for video detection in drainage pipeline. The method extracts feature in the encoder by self-adaptive module and fully multi-scale feature alignment module, and the dehazed image is gradually recovered in the enhanced decoder after the feature recovery module. The results of the experiments indicate that this method significantly improves the robustness and generalization ability of model dehazing by capturing global hazy features at multi-scale resolution, and it is an effective method for applying to drainage pipeline dehazing.

Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes

Geological discontinuity (GD) plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses. Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data. Inspired by recent advances in computer vision, deep learning (DL) models have been widely utilized for image-based fracture identification. The multi-scale characteristics, image resolution and annotation quality of images will cause a scale-space effect (SSE) that makes features indistinguishable from noise, directly affecting the accuracy. However, this effect has not received adequate attention. Herein, we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques. Next, we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Combining these metrics with the scale-space theory, we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition. It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models. In light of these observations, we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation. The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD. It also facilitates the objective understanding, description and analysis of the rock behavior and stability of slopes from the perspective of image data.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast, adaptive multiscale resolution spectral measurement method based on compressed sensing. The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75% compared to a global high-resolution measurement. Mimicking the characteristics of the human retina system, the resolution distribution follows the principle of gradually decreasing. The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user. The system was tested by measuring single and dual spectral peaks, and the results of spectral peaks are consistent with those of global high-resolution measurements.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

Multiscale and multidirection depth map super resolution with semantic inference

AbstractDepth map super resolution has been paid much attention in 3D applications due to the limitation of depth sensors. Few textures in objects with clear contours along them is the most important characteristic of depth map. An efficient image representation should be directional, multiscale and anisotropic. From this we propose a novel multiscale and multidirection depth map super resolution framework with semantic inference to improve the quality of depth maps. In this framework, a multiscale and multidirection depth map contour fusion scheme captures and assembles intrinsic geometrical structures through a multiview non‐subsampled contourlet transform manner. This scheme not only isolates the discontinuities of contours but retains the smoothness along the contours. The semantic inference is also utilized to segment and label the depth map into objects/backgrounds‐level which are coplanar. Furthermore, a semantic‐aware label refinement strategy is introduced to correct the rarely inaccurate labels of the label map for upscaling the target pixel with pixels in the same object or background. Experimental results on benchmark depth map dataset demonstrate that the proposed multiscale and multidirection depth map super resolution framework with semantic inference has a significant improvement than the state‐of‐the‐art algorithms both visually and quantitatively.

Adaptive time multiscale algorithms for fluid–structure interaction with impacts - Application to a row of PWR fuel assemblies under seismic loading

The present article is dedicated to numerical methods for the simulation of the response of PWR fuel assemblies under external mechanical loading such as earthquakes. It proposes new algorithms to manage impact sequences within the time multiscale resolution of a strongly coupled fluid–structure partitioned problem. Preserving the computational efficiency imposes an adaptive strategy to adjust the time scale to force the solution of the costly pressure problem for the fluid only when it is necessary to account for the brutal variations in the structure acceleration resulting from significant impacts. The adaptation criteria must be built upon a thorough monitoring and characterization of all impacts, including the duration of the flight sequence before it occurs and the relative velocity between impact entities. Finally, solutions to mitigate the consequences of missed and unresolved impacts in time are provided to avoid spurious amplification of the variation of the acceleration under impact in the fluid velocity field through the time integration scheme. This leads to two classes of adaptive time multiscale algorithms, extensively tested and qualified through two chosen cases of growing complexity. The paper is completed by a full experimental validation of the proposed fluid–structure framework with contact and impacts, using the results available for a row of six fuel scale assembly immersed in water and submitted to a seismic excitation on a shaking table.