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Copyright of Journal of Engineering & Sustainable Development is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods.

AbstractThis study addresses the multi-material optimization problem by the effective and robust isogeometric proportional topology optimization (IGA-PTO) approach. Non-uniform rational B-spline basis functions are used for descriptions of geometry, displacement field, and density fields of material phases. The alternating active-phase algorithm is employed to convert the optimization problem with multiple constraints into a series of sub-problems of single constraint. Several examples are exhibited, and intensive comparisons with the gradient-based optimality criteria (OC) algorithm are presented to highlight the superior performance of the proposed PTO algorithm. Finally, the optimized topologies are prototyped using 3D printing technology to evidence the manufacturing feasibility. [ABSTRACT FROM AUTHOR]

Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Neural reflectance models are capable of reproducing the spatially‐varying appearance of many real‐world materials at different scales. Unfortunately, existing techniques such as NeuMIP have difficulties handling materials with strong shadowing effects or detailed specular highlights. In this paper, we introduce a neural appearance model that offers a new level of accuracy. Central to our model is an inception‐based core network structure that captures material appearances at multiple scales using parallel‐operating kernels and ensures multi‐stage features through specialized convolution layers. Furthermore, we encode the inputs into frequency space, introduce a gradient‐based loss, and employ it adaptive to the progress of the learning phase. We demonstrate the effectiveness of our method using a variety of synthetic and real examples. [ABSTRACT FROM AUTHOR]

The Multisensor Multiresolution Technique (MMT) is applied to unmixed thermal images from ASTER (90 m), using 30 m resolution images from Landsat 8-9 reflective channels. The technique allows for the retrieval of thermal radiance values of the features identified in the high-resolution reflective images and the generation of a high-resolution radiance image. Different alternatives of application of MMT are evaluated in order to determine the optimal methodology design: performance of the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means classification algorithms, with different initiation numbers of clusters, and computation of contributions of each cluster using moving windows with different sizes and with and without weight coefficients. Results show the K-means classification algorithm with five clusters, without matrix weighting, and utilizing a 5 × 5 pixel window for synthetic high-resolution image reconstruction. This approach obtained a maximum R2 of 0.846 and an average R2 of 0.815 across all cases, calculated through the validation of the synthetic high-resolution TIR image generated against a real Landsat 8-9 TIR image from the same area, same date, and co-registered. These values imply a 0.89% improvement regarding the second-best methodology design (K-means with five starting clusters with 7 × 7 moving window) and a 410.25% improvement regarding the worst alternative (K-means with nine initial clusters, weighting, and 3 × 3 moving window). [ABSTRACT FROM AUTHOR]

Recognizing underwater objects based on radiated noise information is one of the most crucial issues in underwater acoustics. Underwater acoustic target signals are altered by elements such as the undersea environment and the ship's operational circumstances; hence, generalizing the recognition model is crucial. Most conventional Machine Learning (ML) algorithms often encounter difficulties when dealing with the costly recognition model for massive data analysis. However, Convolutional Neural Networks (CNNs) can automatically extract features for precise categorization. DenseNet is a powerful CNN network, but it has a data duplication problem, so in this paper, an approach using multi-resolution with a dense CNN model for underwater acoustic radar signal detection is proposed to overcome the DensNet problem. At first, the wavelet decomposition with different levels is applied to the input signal to represent the suitable data. The decomposed signals are inputs to the dense CNN. Our detection approach beats other CNN models and achieves an overall accuracy of 99.5% at 0 dB SNR based on experimental findings evaluated on a real-world passive sonar data set.

Image registration matches the features of two images by minimizing the intensity difference, so that useful and complementary information can be extracted from the mapping. However, in real life problems, images may be affected by the imaging environment, such as varying illumination and noise during the process of imaging acquisition. This may lead to the local intensity distortion, which makes it meaningless to minimize the intensity difference in the traditional registration framework. To address this problem, we propose a variational model for joint image registration and intensity correction. Based on this model, a related greedy matching problem is solved by introducing a multiscale approach for joint image registration and intensity correction. An alternating direction method (ADM) is proposed to solve each multiscale step, and the convergence of the ADM method is proved. For the numerical implementation, a coarse-to-fine strategy is further proposed to accelerate the numerical algorithm, and the convergence of the proposed coarse-to-fine strategy is also established. Some numerical tests are performed to validate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

It is very important to detect fault and extract fault features of mechanical systems at an early stage, because the above two steps promise normal operation of mechanical systems. However, they are also very challenging. In this context, this article has put forward improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition of rolling bearing (IPSO-AMDMD). Multiresolution dynamic mode decomposition (MRDMD) is used to decompose signals of rolling bearing at the early stage, multiscale fuzzy entropy (MFE) is employed to divide low-rank components and sparse components. In order to make up for the shortcomings of the above two methods, namely truncated rank of MRDMD and inaccurate selection in threshold of MFE, this paper has proposed a new fitness function, which is called synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the optimal parameters adaptively. With these two steps, signals can be decomposed perfectly. Finally, reconstructed signals, which are obtained through the combination of signals from each layer according to a certain weight, go through DMD again, thus getting the final recovered signal. Through simulation experiment and in-field experiment, it has proved that IPSO-AMDMD is viable and sound in accurately extracting features from fault signals. [ABSTRACT FROM AUTHOR]

The demand for scalable indoor localization systems is increasing more than ever due to the emerging phenomena of the Internet of Things, industry 5.0, where humans and robots work together, and ubiquitous connectivity. As a result, various indoor localization systems based on wireless technologies have been proposed in the literature to provide accurate indoor localization services. While there have been advances in indoor localization systems using wireless technologies, there is still a need to address the demands for multiresolution and scalability capabilities. Thus, this paper provides an in-depth analysis of indoor localization techniques and approaches from a scalability perspective, critically analyzing their capabilities and limitations. Therefore, the main objective of this paper is to highlight the key research challenges of implementing multiresolution and scalable wireless indoor localization systems for large-scale environments with the prime concern of identifying key challenges and future research directions. [ABSTRACT FROM AUTHOR]

Abstract Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P

Currently, with the bloom in artificial intelligence (AI) algorithms, various human-centered smart systems can be utilized, especially in cognitive computing, for the detection of various chronic brain diseases such as epileptic seizure. The primary goal of this research article is to propose a novel human-centered cognitive computing (HCCC) method for segment-wise seizure classification by employing multiresolution extracted data with directed transfer function (DTF) features, termed as the multiresolution directed transfer function (MDTF) approach. Initially, the multiresolution information of the epileptic seizure signal is extracted using a multiresolution adaptive filtering (MRAF) method. These seizure details are passed to the DTF where the information flow of high frequency bands is computed. Thereafter, different measures of complexity such as approximate entropy (AEN) and sample entropy (SAEN) are computed from the extracted high frequency bands. Lastly, a k-nearest neighbor (k-NN) and support vector machine (SVM) are used for classifying the EEG signal into non-seizure and seizure data depending on the multiresolution based information flow characteristics. The MDTF approach is tested on a standard dataset and validated using a dataset from a local hospital. The proposed technique has obtained an average sensitivity of 98.31%, specificity of 96.13% and accuracy of 98.89% using SVM classifier. The average detection rate of the MDTF approach is 97.72% which is greater than the existing approaches. The proposed MDTF method will help neuro-specialists to locate seizure information drift which occurs within the consecutive segments and between two channels. The main advantage of the MDTF approach is its capability to locate the seizure activity contained by the EEG signal with accuracy. This will assist the neurologists with the precise localization of the epileptic seizure automatically and hence will reduce the burden of time-consuming epileptic seizure analysis. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Multisensor Multiresolution Technique (MMT) is applied to unmixed thermal images from ASTER (90 m), using 30 m resolution images from Landsat 8-9 reflective channels. The technique allows for the retrieval of thermal radiance values of the features identified in the high-resolution reflective images and the generation of a high-resolution radiance image. Different alternatives of application of MMT are evaluated in order to determine the optimal methodology design: performance of the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means classification algorithms, with different initiation numbers of clusters, and computation of contributions of each cluster using moving windows with different sizes and with and without weight coefficients. Results show the K-means classification algorithm with five clusters, without matrix weighting, and utilizing a 5 × 5 pixel window for synthetic high-resolution image reconstruction. This approach obtained a maximum R2 of 0.846 and an average R2 of 0.815 across all cases, calculated through the validation of the synthetic high-resolution TIR image generated against a real Landsat 8-9 TIR image from the same area, same date, and co-registered. These values imply a 0.89% improvement regarding the second-best methodology design (K-means with five starting clusters with 7 × 7 moving window) and a 410.25% improvement regarding the worst alternative (K-means with nine initial clusters, weighting, and 3 × 3 moving window).

This study proposes a B-spline-based multiresolution and multimaterial topology optimization (TO) design method for fail-safe structures (FSSs), aiming to achieve efficient and lightweight structural design while ensuring safety and facilitating the postprocessing of topological structures. The approach involves constructing a multimaterial interpolation model based on an ordered solid isotropic material with penalization (ordered-SIMP) that incorporates fail-safe considerations. To reduce the computational burden of finite element analysis, we adopt a much coarser analysis mesh and finer density mesh to discretize the design domain, in which the density field is described by the B-spline function. The B-spline can efficiently and accurately convert optimized FSSs into computer-aided design models. The 2D and 3D numerical examples demonstrate the significantly enhanced computational efficiency of the proposed method compared with the traditional SIMP approach, and the multimaterial TO provides a superior structural design scheme for FSSs. Furthermore, the postprocessing procedures are significantly streamlined. [ABSTRACT FROM AUTHOR]

Thin shell structures exhibit complex behaviors critical for modeling and design across wide-ranging applications. Capturing their mechanical response requires finely detailed, high-resolution meshes. Corresponding simulations for predicting equilibria with these meshes are expensive, whereas coarse-mesh simulations can be fast but generate unacceptable artifacts and inaccuracies. The recently proposed progressive simulation framework [Zhang et al. 2022] offers a promising avenue to address these limitations with consistent and progressively improving simulation over a hierarchy of increasingly higher-resolution models. Unfortunately, it is currently severely limited in application to meshes and shapes generated via Loop subdivision. We propose Progressive Shells Quasistatics to extend progressive simulation to the high-fidelity modeling and design of all input shell (and plate) geometries with unstructured (as well as structured) triangle meshes. To do so, we construct a fine-to-coarse hierarchy with a novel nonlinear prolongation operator custom-suited for curved-surface simulation that is rest-shape preserving, supports complex curved boundaries, and enables the reconstruction of detailed geometries from coarse-level meshes. Then, to enable convergent, high-quality solutions with robust contact handling, we propose a new, safe, and efficient shape-preserving upsampling method that ensures non-intersection and strain limits during refinement. With these core contributions, Progressive Shell Quasistatics enables, for the first time, wide generality for progressive simulation, including support for arbitrary curved-shell geometries, progressive collision objects, curved boundaries, and unstructured triangle meshes - all while ensuring that preview and final solutions remain free of intersections. We demonstrate these features across a wide range of stress-tests where progressive simulation captures the wrinkling, folding, twisting, and buckling behaviors of frictionally contacting thin shells with orders-of-magnitude speed-up in examples over direct fine-resolution simulation. [ABSTRACT FROM AUTHOR]

This study presents the incorporation of the effective gradient‐free proportional topology optimization algorithm into the framework of isogeometric analysis. The minimization of the compliance is considered, and the solid isotropic material with penalization method is used. The geometry, displacements, and density are all described by non‐uniform rational B‐spline (NURBS) basis functions. The density at an integration point is determined proportionally to its compliance. Then, the NURBS description of the density is constructed elementwise by deriving a relation between densities assigned to integration points and control points. The global NURBS description of the density for the whole domain is a blend of those from elements. Furthermore, a multiresolution scheme is presented by means of k$$ k $$‐refinement technique to enable the efficient performance for large‐scale problems. The accuracy and efficiency of the proposed approach are assessed through six numerical examples, including two‐ and three‐dimensional structures, with several rigorous tests and comparisons with the gradient‐based optimality criteria algorithm. [ABSTRACT FROM AUTHOR]

Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics |$K$|⁠. Since there is no definitive way to choose |$K$| and since a true value might not exist, we develop a method, which we call topic alignment , to study the relationships across models with different |$K$|⁠. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when |$K$| increases. This strategy gives more insight into the process of generating the data than choosing a single value of |$K$| would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto [ABSTRACT FROM AUTHOR]

We present MR-Net, a general architecture for multiresolution sinusoidal neural networks, and a framework for imaging applications based on this architecture. We extend sinusoidal networks, and we build an infrastructure to train networks to represent signals in multiresolution. Our coordinate-based networks, namely L-Net, M-Net, and S-Net, are continuous both in space and in scale as they are composed of multiple stages that progressively add finer details. Currently, band-limited coordinate networks (BACON) are able to represent signals at multiscale by limiting their Fourier spectra. However, this approach introduces artifacts leading to an image with a ringing effect. We show that MR-Net can represent more faithfully what is expected of sequentially applying low-pass filters in a high-resolution image. Our experiments on the Kodak Dataset show that MR-Net can reach comparable Peak Signal-to-Noise Ratio (PSNR) to other architectures, on image reconstruction, while needing fewer additional parameters for multiresolution. Along with MR-Net, we detail our architecture's mathematical foundations and general ideas, and show examples of applications to texture magnification, minification, and antialiasing. Lastly, we compare our three MR-Net subclasses. [Display omitted] • Framework to train deep networks to represent signals in multiresolution. • Network initialization is used to control the frequencies learned by the model. • MR-Net provides a continuous representation of signals spatially and in scale. • MR-Net represents images with higher or comparable PSNR than other architectures. • Applications in texture magnification and minification, and antialiasing. [ABSTRACT FROM AUTHOR]

This article proposes a novel imaging mode which allows for the acquisition of SAR images with different resolution in a single imaging process, specifically designed for multiple-input–multiple-output synthetic aperture radar (MIMO-SAR). To achieve this, a multimode array system model based on 2-D intrapulse scanning is established, followed by the division of full aperture signals into subaperture signals. Improved azimuth phase coding (APC) technology is then employed to separate multimode echo signals, and spatial filtering technology and range digital beam forming technology are used to remove ambiguity in azimuth and range of the subaperture signals of each mode. Finally, subaperture image coherent fusion algorithm is used to generate high-resolution images corresponding to the full aperture of each mode. Simulation results show that the improved APC can effectively separate multimode echo signals, and the multiresolution characteristics and imaging effect are verified.

Deep convolutional networks are powerful for local feature learning and have shown advantages in image matching and registration. However, the significant differences between cross-modal images increase the challenge of image registration. The deep network should extract modality-invariant features to identify the matching samples and discriminative features to separate the nonmatching samples. The deep network can extract features invariant to the image modality changes by multiple nonlinear mapping layers. However, it does not inevitably lose rich details and affect the discrimination of features, degrading registration performances. This article proposes a novel deep wavelet learning network (DW-Net) for local feature learning. It incorporates spectral information into deep convolutional features for improving cross-modal image matching and registration. Specifically, this article aims to learn the multiresolution wavelet features through multilevel wavelet transform (WT) and the convolutional network. The cross-modal images are divided into low-frequency and high-frequency parts through WT. DW-Net can adaptively extract the shared features from the low-frequency part and useful details from the high-frequency part, which can enhance the modality invariance and discrimination of features. Additionally, the multiresolution wavelet features contain multiscale information and contribute to improving the matching accuracy. Extensive experiments demonstrate the significant advantages in terms of the accuracy and robustness of DW-Net on cross-modal remote sensing image registration. DW-Net can increase the image patch matching accuracy by 3.7% and improve image registration probability by 12.1%. Moreover, DW-Net shows strong generalization performances from low resolution to high resolution and from optical– synthetic aperture radar to other cross-modal image registration.

Land-cover mapping is important for urban planning and management, and current land-cover mapping products are unable to meet the needs of cities due to frequent land surface changes. In this study, based on the low-to-high network (L2HNet) network, we generate a high-resolution land-cover mapping product for Wuhan and its surrounding areas. In this article, we adopt a simplified L2HNet by removing the confident area selection and the L2H loss module to shorten the cycle time of the entire mapping process. The mapping process used ESA LandCover (2021) as low-resolution labels and Google Maps as high-resolution remote sensing images. In the course of the experiment, we also calculate the four indicators mean intersection over union (MIoU), overall accuracy (OA), frequency weighted intersection over union (FWIoU), and Kappa, evaluate the accuracy of our product in predicting fine feature structure using a point-based test method, and compare it with six mainstream land-cover mapping products. The product achieves a 1m-resolution land-cover product in study areas while maintaining an over 75.21% MIoU. OA, FWIoU, and Kappa all maintain values above 85.00%, showing excellent prediction results. In quantitative analysis, compared to ESA LandCover(2021), the L2HNet product has a significant improvement in mapping accuracy for build-up and permanent water, including an exciting 21.08% improvement in permanent water accuracy and an amazing improvement in build-up. The comparison with mainstream products also shows the credibility and practicality of the product. The end result of this research fills a gap in Wuhan and its surrounding areas' 1m-resolution land-cover mapping product. While significantly improving the product's resolution, L2HNet makes time- and labor-saving periodic mapping a reality.

Multisensor data analysis allows exploiting heterogeneous data regularly acquired by the many available remote sensing (RS) systems. Machine- and deep-learning methods use the information of heterogeneous sources to improve the results obtained by using single-source data. However, the state-of-the-art methods analyze either the multiscale information of multisensor multiresolution images or the time component of image time series. We propose a supervised deep-learning classification method that jointly performs a multiscale and multitemporal analysis of RS multitemporal images acquired by different sensors. The proposed method processes very-high-resolution (VHR) images using a residual network with a wide receptive field that handles geometrical details and multitemporal high-resolution (HR) image using a 3-D convolutional neural network that analyzes both the spatial and temporal information. The multiscale and multitemporal features are processed together in a decoder to retrieve a land-cover map. We tested the proposed method on two multisensor and multitemporal datasets. One is composed of VHR orthophotos and Sentinel-2 multitemporal images for pasture classification, and another is composed of VHR orthophotos and Sentinel-1 multitemporal images. Results proved the effectiveness of the proposed classification method.

Digital elevation models (DEMs) are crucial in natural hazard assessments, as they often present the only comprehensive information. While satellites deliver remote sensing information of the land surface of up to 2m resolution, only 25% of the seafloor is mapped with a minimum resolution of 400m. The acquisition of high-resolution bathymetry requires hydroacoustic surveys by research vessels or autonomous vehicles, which is time-consuming and expensive. Predicted bathymetry from satellite altimetry, on the other hand, is widely available but has a significantly lower spatial resolution and high uncertainties in elevation, especially in shallow waters. The research on volcanic islands as a source of both volcanic as well as marine hazards such as tsunamis, is greatly limited by the lack of high-resolution bathymetry. Here we compare 24 geomorphometric parameters of 47 volcanic islands derived from a) the comprehensive bathymetric data of the General Bathymetric Chart of the Ocean (GEBCO) and b) high-resolution (< 250m), ship-based bathymetry. Out of 24 parameters tested, 20 show < ± 2.5% median deviation, and quartiles < ± 10%. Parameters describing the size of a volcanic island are the most robust and slope parameters show the greatest deviations. With this benchmark, we will be able to increase geomorphometric investigations to volcanic islands where little or no high-resolution bathymetry data is available.

Due to the increasingly large amount of data acquired into point clouds, from LiDAR (Light Detection and Ranging) sensors and 2D/3D sensors, massive point clouds processing has become a topic with high interest for several fields. Current client-server applications usually use multiresolution out-of-core proposals; nevertheless, the construction of the data structures required is very time-consuming. Furthermore, these multiresolution approaches present problems regarding point density changes between different levels of detail and artifacts due to the rendering of elements entering and leaving the field of view. We present an autotuning multiresolution out-of-core strategy to avoid these problems. Other objectives are reducing loading times while maintaining low memory requirements, high visualization quality and achieving interactive visualization of massive point clouds. This strategy identifies certain parameters, called performance parameters, and defines a set of premises to obtain the goals mentioned above. The optimal parameter values depend on the number of points per cell in the multiresolution structure. We test our proposal in our web-based visualization software designed to work with the structures and storage format used and display massive point clouds achieving interactive visualization of point clouds with more than 27 billion points. [ABSTRACT FROM AUTHOR]

Topology optimization of multi-material structures under dynamic loads is implemented to minimizing compliance on polygonal finite element meshes with multiple volume constraints. A multiresolution scheme is introduced to obtain high resolution de-signs for structural dynamics problems with less computational burden. This multiresolution scheme employs a coarse finite element mesh to fulfil the dynamic analysis, a refined density variable mesh for optimization and a density variable mesh overlapping with the density variable mesh for design configuration representation. To obtain the dynamic response, the HHT-α method is employed. A ZPR (Zhang-Paulino-Ramos Jr.) update scheme is used to update the design variables in association to multiple volume constraints by a sensitivity separation technique. Several numerical examples are presented to demonstrate the effectiveness of the method to solve the topology optimization problems for mul-ti-material structures under dynamic loads. [ABSTRACT FROM AUTHOR]

A high-accuracy multiresolution method is proposed to solve mechanics problems subject to complex shapes or irregular domains. To realize this method, we design a new wavelet basis function, by which we construct a fifth-order numerical scheme for the approximation of multi-dimensional functions and their multiple integrals defined in complex domains. In the solution of differential equations, various derivatives of the unknown function are denoted as new functions. Then, the integral relations between these functions are applied in terms of wavelet approximation of multiple integrals. Therefore, the original equation with derivatives of various orders can be converted to a system of algebraic equations with discrete nodal values of the highest-order derivative. During the application of the proposed method, boundary conditions can be automatically included in the integration operations, and relevant matrices can be assured to exhibit perfect sparse patterns. As examples, we consider several second-order mathematics problems defined on regular and irregular domains and the fourth-order bending problems of plates with various shapes. By comparing the solutions obtained by the proposed method with the exact solutions, the new multiresolution method is found to have a convergence rate of fifth order. The solution accuracy of this method with only a few hundreds of nodes can be much higher than that of the finite element method (FEM) with tens of thousands of elements. In addition, because the accuracy order for direct approximation of a function using the proposed basis function is also fifth order, we may conclude that the accuracy of the proposed method is almost independent of the equation order and domain complexity. [ABSTRACT FROM AUTHOR]

Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation–calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD‐FP flood hazard model. The multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections. [ABSTRACT FROM AUTHOR]

Community structures are sets of nodes that are densely linked with each other, reflecting the functional modules of real-world systems. Most classical works for community detection (CD) are based on the optimization of an objective function, namely modularity. However, it has been recently demonstrated that there exists a resolution limit in the modularity optimization based CD methods, i.e., the communities cannot be detected if their scales are smaller than a certain threshold. To overcome this resolution limit, in this paper, we propose a decomposition based multiobjective memetic algorithm (called MDMCD) for multiresolution CD (MCD) in complex networks, aiming to detect communities at multiple resolution levels. MDMCD first models the MCD problem as a multiobjective optimization problem (MOP) with two contradictory objectives, namely the intra-link ratio and inter-link ratio. Then, it devises a multiobjective memetic optimization framework that combines a decomposition based multiobjective evolutionary algorithm with a two-level local search to solve the modeled MOP. In this framework, the modeled MOP is first decomposed into a set of single-objective optimization subproblems, each of which corresponds to a CD problem in a certain resolution level. Subsequently, these subproblems are simultaneously optimized by the evolutionary operators and the local search, taking the network-specific knowledge into consideration. Finally, MDMCD returns a population of solutions in a single simulation run, reflecting the community divisions at multiple resolution levels. Experiments on both the simulated and real-world networks show the effectiveness of MDMCD in detecting multiresolution community structures. [ABSTRACT FROM AUTHOR]

This paper discusses challenges for M&S if it is to be increasingly important to decision aiding and policy analysis. It suggests an approach that—from the outset of a policy analysis project—incorporates M&S of a varied resolution with the intent that (1) the results of analysis will be communicated with a relatively simple model and corresponding narrative that scans the system problem in breadth, having been informed by richer modeling, and (2) the broad view is supplemented by the selective detail (zooms) and selected change of the perspective as needed. This is not just a matter of "dumbing down" communication, but a matter of thinking about both forests and trees from the outset and about designing analytic tools accordingly. It will also enable exploratory analysis amidst uncertainty and disagreement, which is central to modern policy analysis and decision-aiding. All of this poses significant challenges for those who design and build M&S. [ABSTRACT FROM AUTHOR]

Images having different focus values are combined using image fusion methodologies. The fused image shows superiority in the quality and highly informative than compared o any other multifocus images. The fused image would show its suitability for visual perception object detection due to its high quality than compared to multifocus images. Among the various quality parameters of the fused image, directional invariance and shift invariance significantly influence the fused image quality. The traditional image fusion methods using wavelets severely suffers with poor invariance and lack of directionality. An efficient image fusion method is developed using DTCWT with qshiftN and MPCA algorithms in the multiresolution (MR) domain. Multifocus input images are decomposed into high and low frequency components using MR algorithm. The decomposed frequency components of the input images are fused using DTCWT with qshiftN algorithm. The proposed fusion algorithm preserves both sift invariance and directional properties of the multifocus source image. Lastly, the fused image is processed using MPCA algorithm to enhance the features. The proposed methodology is assessed using various multifocus images and the evaluated metrics are contrasted with technologically advanced algorithms reported recently. The statistical metrics evaluated for the proposed method for different multifocus images shows superior properties than compared to the recently reported multifocus image fusion algorithms. [ABSTRACT FROM AUTHOR]

The competitive facility location problem arises when businesses plan to enter a new market or expand their presence. We introduce a Bayesian spatial interaction model which provides probabilistic estimates on location-specific revenues and then formulate a mathematical framework to simultaneously identify the location and design of new facilities that maximise revenue. To solve the allocation optimisation problem, we develop a hierarchical search algorithm and associated sampling techniques that explore geographic regions of varying spatial resolution. We demonstrate the approach by producing optimal facility locations and corresponding designs for two large-scale applications in the supermarket and pub sectors of Greater London. [ABSTRACT FROM AUTHOR]

Since the launch of the European Space Agency’s Sentinel-1 radar and Sentinel-2 optical satellites, high-resolution multisource datasets have widely been used for land cover classification and other thematic research. The aim of this study was to segment 4 different indices derived from Sentinel-1 and 2 satellite datasets using multisolution and quadtree methods, classify the land cover of the selected area using an object-based classification method, and make a comparison. As a test site, the northwestern part of Khuvsgul Lake was selected, and integrated images acquired from Sentinel-1 and 2 satellites in June 2022 were analyzed. According to the results, the overall accuracy of the classification based on the quadtree segmentation was 99.71%, while the overall accuracy of the classification based on the multisolution segmentation was 98.80%. This finding indicates that using the quadtree method provides better results in determining the land cover types of the selected area. [ABSTRACT FROM AUTHOR]

The trade-off between speed and fidelity in cloth simulation is a fundamental computational problem in computer graphics and computational design. Coarse cloth models provide the interactive performance required by designers, but they can not be simulated at higher resolutions ("up-resed") without introducing simulation artifacts and/or unpredicted outcomes, such as different folds, wrinkles and drapes. But how can a coarse simulation predict the result of an unconstrained, high-resolution simulation that has not yet been run? We propose Progressive Cloth Simulation (PCS), a new forward simulation method for efficient preview of cloth quasistatics on exceedingly coarse triangle meshes with consistent and progressive improvement over a hierarchy of increasingly higher-resolution models. PCS provides an efficient coarse previewing simulation method that predicts the coarse-scale folds and wrinkles that will be generated by a corresponding converged, high-fidelity C-IPC simulation of the cloth drape's equilibrium. For each preview PCS can generate an increasing-resolution sequence of consistent models that progress towards this converged solution. This successive improvement can then be interrupted at any point, for example, whenever design parameters are updated. PCS then ensures feasibility at all resolutions, so that predicted solutions remain intersection-free and capture the complex folding and buckling behaviors of frictionally contacting cloth. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R2 > 0.89), Landsat 8 (|r| > 0.50 and R2 > 0.72), MODIS (|r| > 0.65 and R2 > 0.88), and AVHRR (|r| > 0.59 and R2 > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation. [ABSTRACT FROM AUTHOR]

We present the derivation, implementation, and analysis of a multiresolution adaptive grid framework for numerical simulations on octree-based three-dimensional block-structured collocated grids with distributed computational architectures. Our approach provides a consistent handling of nonlifted and lifted interpolating wavelets of arbitrary order demonstrated using second-, fourth-, and sixth-order wavelets, combined with standard finite-difference-based discretization op-erators. We first validate that the wavelet family used provides strict and explicit error control when coarsening the grid, and show that lifting wavelets increase the grid compression rate while conserving discrete moments across levels. Further, we demonstrate that high-order PDE discretization schemes combined with sufficiently high-order wavelets retain the expected convergence order even at resolution jumps. We then simulate the advection of a scalar to analyze convergence for the temporal evolution of a PDE. The results shows that our wavelet-based refinement criterion is successful at controlling the overall error while the coarsening criterion is effective at retaining the relevant information on a compressed grid. Our software exploits a block-structured grid data structure for efficient multilevel operations, combined with a parallelization strategy that relies on a one-sided MPI-RMA communication approach with active post-start-complete-wait synchronization. Using performance tests up to 16,384 cores, we demonstrate that this leads to a highly scalable performance. The associated code is available under a BSD-3 license at https://github.com/vanreeslab/murphy. [ABSTRACT FROM AUTHOR]