Spatial Reconstruction Research Articles

A Star‐Nose‐Inspired Bionic Soft Robot for Nonvisual Spatial Detection and Reconstruction

The star‐nosed mole is recognized as a tactilely sensitive mammal due to its unique nose, which facilitates spatial detection in dark environments through touch using its appendages enveloped in numerous sensory receptors. This article introduces a bionic soft robot inspired by the star‐nosed mole, which combines a pneumatic soft platform with a polydimethylsiloxane–polyethylene terephthalate cylindrical tactile sensor array based on bilayer single‐electrode triboelectric nanogenerators, mimicking the muscle tissue of the mole's nose and the cylindrical appendages surrounded by Eimer's organs. The cylindrical sensor array enables multiangle spatial detection without an external power supply and remains unaffected by external materials. By implementing a constant curvature model for robot motion control, positional information is provided for the contact points between the cylindrical sensor array and the external environment. The robot effectively discriminates the distance and shape of various objects and achieves nonvisual 3D spatial detection and reconstruction in real‐world scenarios. This work presents a novel bionic approach for 3D spatial detection in nonvisual environments.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm

Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements on resource-constrained operational equipment—this paper proposes an improved lightweight ton bag detection algorithm, YOLOv8-TB (YOLOv8-Ton Bag), which is optimized based on YOLOv8. Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. On the logistics ton bag operation dataset provided by shipping port enterprises, the improved algorithm improves by 3.7% and 5% compared with the original algorithm in mAP50 and mAP50-95, respectively, the model size is reduced by 4.42 MB and the amount of model computation is only 8 G, which is capable of accurately detecting logistics ton bags in real time. The superiority of the method is verified by comparing it with other classical target detection algorithms.

A drop in Antarctic sea ice extent at the end of the 1970s

After a period of relative stability, the Antarctic sea ice extent has abruptly decreased in 2016 and has remained low since then. Both atmospheric and oceanic processes likely contributed to this drop but many questions remain regarding the underlying dynamics and it is unknown if this drop is unprecedented. Here we produce a new multi-variate spatial reconstruction covering 1958–2023 and show that a similar drop in sea ice extent occurred at the end of the 1970s, albeit with a smaller magnitude. Both drops show similar spatial patterns, with a higher sea ice loss in the East Antarctic sector than in the West Antarctic sector where the variability is strongly modulated by wind-driven changes. The ocean integrates the atmospheric forcing and provides memory that amplifies the magnitude of both drops.

Robot Grasping Detection Method Based on Keypoints

ABSTRACTThis study introduces a novel keypoint‐based grasp detection network, denoted as GKSCConv‐Net, which operates on n‐channel input images. The network architecture comprises three SCConv2D layers and three SCConvT2D layers. The SCConvT2D layers facilitate upsampling to maintain consistent dimensions between the output and input images. The resultant output consists of maps indicating left grasp points, right grasp points, and grasp center keypoints. The accuracy of predictions is enhanced through the incorporation of the keypoint refinement module and feature fusion module. To validate the model's generalization and applicability, comprehensive training, testing, and evaluation are conducted on diverse data sets, including the Cornell data set, Jacquard data set, and others representing real‐world scenarios. Furthermore, ablation experiments are employed to substantiate the efficacy of the spatial reconstruction unit (SRU) and channel reconstruction unit (CRU) within the SCConv, exploring their impact on grasp keypoint detection outcomes. Real robotic grasping experiments ultimately affirm the model's outstanding performance in practical settings.

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing highdimensional microimage data. We propose a novel finegrained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell FineGrained Classification Dataset (BMCD-FGCD), comprising over 10,000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets.

A deep learning-based spatial gradient reconstruction method for efficient damage identification in composite with high-sparsity Lamb wavefield

The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its time-consuming measurement process limits its application in engineering scenarios. To address this, we introduce a novel damage index, the spatial gradient, which captures the interaction between delamination and the wavefield. We have also developed a neural network capable of reconstructing the spatial gradient directly from high-sparsity Lamb wavefield data obtained at an extremely low spatial sampling rate, thereby significantly reducing measurement time. To enhance the network’s capability to detect wavefield anomalies, we employ the cross-attention technique, allowing for the direct injection of shallow features representing local wavefield distortions caused by damage into the decoder. Additionally, we integrate multiple reconstruction layers to guide the wavefield reconstruction process, ensuring meaningful information is captured at each stage. Our method achieves substantial improvements in reconstruction accuracy, increasing from 70 % to 92 % in single-damage scenario and from 14 % to 72 % in multi-damage scenario compared to the previous state-of-the-art techniques. By using the reconstructed spatial gradient field for damage imaging through spatial covariance analysis, our approach demonstrates its feasibility and generalizability across various damage locations. This suggests its potential as a reliable solution for fast and accurate damage characterization, reducing the measurement burden and enhancing practical applicability.

ADT: Person re-identification based on efficient attention mechanism and single-channel dual-channel fusion with transformer features aggregation

In person re-identification(Re-ID) tasks, using only convolutional neural network(CNNs) makes it challenging to fully exploit the global key information in person images, while using only the Transformer structure fails to adequately capture the multi-scale local detail information in person images. Therefore, to address the more challenging person Re-ID tasks, effectively integrating CNN and Transformer into an algorithmic framework capable of fusing multi-dimensional features has become one of the current focal points of research. Due to the ability of the attention mechanism to suppress the extraction of non-salient features by convolutional neural networks, and the capability of the dual-channel multi-scale mechanism to facilitate the extraction of finer-grained deep information by the Transformer, we propose a network Based on Efficient Attention Mechanism and Single-channel Dual-channel Fusion with Transformer Features Aggregation (ADT). First, we introduce the Convolutional Block Attention Module(CBAM) and Spatial Reconstruction Unit (SRU) into the CNN network to construct a Multi-dimensional Attention Features Re-fusion Network(MCF). By weighting the local features extracted in the convolutional network, attention to person detail features in the model is enhanced. Then, we propose the Single and Dual-channel Integrated Transformer Features Aggregator(SDT) and overlap it in MCF. This integration is designed to more effectively aggregate the multi-scale high-dimensional global features from each layer of the CNN network. Experimental results on the Market1501, DukeMTMC, MSMT17, and CUHK03 datasets demonstrate that our model significantly enriches features representation compared to most methods.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

The influence of categorical stimuli on relational memory binding.

Binding of arbitrary information into distinct memory representations that can be used to guide behavior is a hallmark of relational memory. What is and is not bound into a memory representation and how those things influence the organization of that representation remain topics of interest. While some information is intentionally and effortfully bound-often the information that is consistent with task goals or expectations about what information may be required later-other information appears to be bound automatically. The present set of experiments sought to investigate whether spatial memory would be systematically influenced by the presence and absence of distinct categories of stimuli on a spatial reconstruction task. In this task, participants must learn multiple item-location bindings and place each item back in its studied location after a short delay. Across three experiments, participants made significantly more within-category errors (i.e., misassigning one item to the location of a different item from the same category) than between-category errors (i.e., misassigning one item to the location of an item from a different category) when categories were perceptually or semantically distinct. These data reveal that category information contributed to the organization of the memory representation and influenced spatial reconstruction performance. Together, these results suggest that categorical information can influence memory organization, and not always to the benefit of overall task performance.

Light field images super-resolution method based on hybrid low-dimensional spatial–angular interaction feature and linear complementation epipolar feature

Light-field (LF) cameras record 4D information about the intensity and angle of light, but limited by the resolution of the sensor, LF images require a trade-off between spatial and angular resolution, which results in generally low spatial resolution for LF images. We work on spatial super-resolution reconstruction (SR) of LF images, but due to the high dimensionality of 4D LF, it is difficult to process it directly, so we decompose 4D LF into low-dimensional sub-spaces. Since the 4D features of interest to different low-dimensional sub-spaces are different, we design a hybrid network structure—HSAESR, which processes multiple low-dimensional sub-spaces individually to adequately model the texture information in the 4D LF. HSAESR is designed to reconstruct Macro Pixel Images (MacPI) and Extremely Planar Images (EPI) respectively, and the final reconstruction results are weighted and fused. For MacPI, a CNN-based feature interaction module is designed using a novel asymptotic ’grid’ interaction approach to fully utilize the correlation between the 2D spatial dimension and the 2D angular dimension sub-spaces in 4D LF. For EPI, a Transformer-based epipolar feature linear complementation module is designed learning global and non-global multi-directional epipolar feature correlations, which learns feature linear correlation factor (α) and linear bias weight (β) through the correlation of non-global epipolar features, and corrects and complements the global epipolar correlation features through feature linear complementation. Extensive experiments are conducted on five datasets, the results show that HSAESR further improves the performance of LF images spatial SR, the reconstruction results are better than the comparison methods.

STREAMLINING DIGITAL CORRELATION-INTERFEROMETRIC DIRECTION FINDING WITH SPATIAL ANALYTICAL SIGNAL

This study investigated a search-free digital method for radio direction-finding that utilizes spatial analytical signal reconstruction. The research focused on optimizing the method's accuracy for estimating the direction of a radio source. The analysis identified the separation distance and number of chosen antenna elements for signal reconstruction as the key parameters influencing accuracy. Analytical optimization and simulations were employed to determine the optimal values for these parameters. The research successfully modeled the impact of antenna element selection on direction-finding errors under specific signal-to-noise conditions. This model can be used to guide further development and optimization of this radio direction-finding method.

Four-frame pixel super-resolution method for lensless imaging systems

Lensless imaging systems have gained significant attention recently due to their advantages in terms of reduced size and weight compared to traditional lens-based systems. However, like other imaging methods, lensless imaging encounters challenges in resolving scenes with more details. In this article, we propose a novel four-frame super-resolution method specifically tailored for lensless imaging systems. Our approach shares similarities with previous lensless imaging systems, involving a sensor and a modulation device placed in front of the image sensor. We develop an explicit degradation downsampling model with sub-pixel shifts and provide the solution to corresponding inverse problem, may offering valuable guidance for other super-resolution imaging algorithms based on spatial displacements. By applying random lateral sub-pixel shifts, acquiring four low-resolution (LR) images, and fusing their spatial information, we achieve high-resolution (HR) sensor recordings, enabling super-resolution reconstruction of the imaging scene. Numerical simulations demonstrate approximately an improvement in spatial resolution compared to single-measurement methods. Furthermore, we evaluate the performance of our method across various lensless imaging systems utilizing different masks, validating its versatility and effectiveness in achieving higher resolution outcomes. Experimental results also support our proposed scheme's ability to achieve higher spatial resolution reconstruction in a real system.

SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.

Research on the impact of digital economy on my country's manufacturing upgrading

With the profound changes in the global industrial economic situation and the further adjustment of the industrial competition pattern, the stability and security of my country's industrial chain and supply chain are facing major risks. In this context, promoting the digital transformation of the manufacturing industry has become the only way for the transformation and upgrading of the manufacturing industry. This paper first analyzes the mechanism of my country's digital economy driving the upgrading of the manufacturing industry, and examines it from the perspective of the spatial layout of the industrial chain, aiming to enrich the theoretical basis of the digital economy driving the development of the real economy, and provide useful countermeasures for my country's manufacturing industry to achieve upgrading under the drive of the digital economy. It has important theoretical and practical significance. This paper starts with "digital economy drives the spatial layout reconstruction of the manufacturing industry chain" and takes "realizing the upgrading of the manufacturing industry" as the ultimate goal. The reasonable spatial layout of the manufacturing industry chain and the upgrading of the manufacturing industry are two important aspects of the high-quality development of my country's manufacturing industry. The reasonable spatial layout of the manufacturing industry chain can promote the rational allocation of factor resources, narrow the regional gap, and realize the upgrading of my country's manufacturing industry more efficiently and comprehensively. Therefore, this paper studies the reconstruction of the spatial layout of the manufacturing industry chain driven by the digital economy, which can not only enhance the resilience of my country's manufacturing industry chain and supply chain, but also provide a path for the upgrading of the manufacturing industry. The two complement each other. Through systematic analysis and verification, this study reveals the role of the digital economy in promoting the reconstruction of the spatial layout of the manufacturing industry chain, and thus provides theoretical support and policy recommendations for achieving high-quality development of the manufacturing industry.

Fluorescence super-resolution microscopy via fluctuation-based multi-route synergy.

Fluorescence fluctuation-based super-resolution microscopy (FF-SRM) is an economical and widely applicable technique that significantly enhances the spatial resolution of fluorescence imaging by capitalizing on fluorescence intermittency. However, each variant of FF-SRM imaging has inherent limitations. This study proposes a super-resolution reconstruction strategy (synSRM) by synergizing multiple variants of the FF-SRM approach to address the limitations and achieve high-quality and high-resolution imaging. The simulation and experimental results demonstrate that, compared to images reconstructed using single FF-SRM algorithms, by selecting suitable synSRM routes according to various imaging conditions, further improvements of the spatial resolution and image reconstruction quality can be obtained for super-resolution fluorescence imaging.

MobilenetV2-RC: A lightweight network model for retinopathy classification in retinal OCT images

Abstract Retinopathy is an important ophthalmic disease that causes blindness in the elderly population. With the global old peoples increasing, the ophthalmic healthcare system is more and significant to pre-diagnosis. Optical Coherence Tomography (OCT) is considered the gold standard for ophthalmic treatment and diagnosis. OCT technologies and equipment continue to develop towards the intelligence and convenience for requirements of rapid diagnosis in the remote and poverty-stricken areas. We proposed an improved MobilenetV2 lightweight model for retinopathy classification (MobilenetV2-RC), which incorporates Spatial and Channel Reconstruction Convolution (SCConv) and the improved Convolutional Block Attention Module (CBAM) attention mechanism into the framework. Not only can it effectively limit feature redundancy to reduce model parameters, but also enhance the ability of feature representation to improve classification accuracy. The parameters of the proposed model are only 1.96 M with an overall accuracy of 98.96%, which is higher 3.32% than the original MobilenetV2. Compared with ResNet18, InceptionV3, and VGG16_BN, the overall accuracy is increased by 4.6%, 6.3%, and 3.9%, respectively. The test results of UCSD and Duke open-source datasets are more remarkable. Experimental results show that our proposed algorithm has strong reliability and generalization for the accurate classification of retinopathy, and a greater application prospect in the intelligent diagnosis of ophthalmology and mobile detection terminals.

The Cognitive Mechanisms of Residents under the Background of the Renewal of Suburban Historical and Cultural Villages

This study aims to investigate the cognitive mechanisms of rural residents amidst the renewal of suburban historical and cultural villages by examining the interplay between nostalgia, collective memory, subjective well-being, and place identity in rural tourism destinations. Using Naobao Village as a case study—a suburban historical and cultural village in Hohhot, Inner Mongolia—this research employs a comprehensive approach integrating experimental methods and questionnaire surveys. The findings demonstrate that analyzing the interaction among the four emotions can elucidate the cognitive mechanism of residents. Moreover, it uncovers that positive tourism effects significantly influence residents’ perception of their living environment with positive effects on collective memory, subjective well-being, and place identity. In the theoretical model for generating cognition among residents in suburban historical and cultural villages, subjective well-being and collective memory play crucial mediating roles. This study offers a novel perspective for spatial reconstruction and cultural evolution of tourist landscapes in suburban rural tourist destinations.

Adaptation of the Low Dissipation Low Dispersion Scheme for Reactive Multicomponent Flows on Unstructured Grids Using Density-Based Solvers

Abstract The low dissipation low dispersion (LD2) second-order accurate scheme is effective for scale-resolving simulations in finite volume computational fluid dynamics (CFD) solvers, thanks to its combination of a skew-symmetric split form for convective terms and matrix-valued artificial dissipation fluxes. However, reactive flow simulations face challenges due to steep gradients and varying gas properties. This study replaces the skew-symmetric scheme with the kinetic energy and entropy preserving (KEEP) scheme, utilizing quadratic and cubic split forms for convective terms, enhancing stability. The nonsmooth fluid interfaces in reactive flow simulations necessitate upwind fluxes for reactive scalars to limit total variation (TV), also requiring upwind fluxes for the mixture-dependent internal energy fluxes. Other convective terms use central discretizations from the KEEP scheme, leveraging LD2’s spatial reconstruction to minimize dispersive errors. Numerical assessments show this approach reduces spurious pressure oscillations in single and multicomponent flows. The absolute flux Jacobian for dissipation flux calculation is efficiently computed using an expanded Turkel’s approach for thermally perfect gas mixtures. Partial pressure derivatives are approximated when using the flamelet generated manifolds (FGM) combustion model. The proposed scheme is evaluated through scale-resolving simulations of the Cambridge burner flame SWB1 on an unstructured grid using the density-based solver TRACE, employing both finite rate chemistry (FRC) and FGM combustion models. Comparative analysis with the all-speed SLAU2 scheme shows the superior performance of the proposed scheme in handling turbulent reactive multicomponent flows.

Synergistically flexible-robust effects mediate the dynamic reconfiguration of perylene diimide polymer to enhance piezo-photocatalytic nitrate reduction

Identifying the spatial dynamic reconstruction of π-conjugated polymers for efficient carrier transport and controlling the intermediate process of the reaction are urgent and formidable challenges. In this study, a fresh perspective has emerged proposing to synergistically enhance the adaptability and resilience of dynamic torsion patterns in π-conjugated polymers to mediate charge separation and molecular activation. Excitingly, the highest flexible-robust structure and polarity of naphthalene-linked perylene diimide polymer (N-PDA) demonstrates a highest nitrate reduction rate of 5.36 mmol g−1 h−1 under light irradiation and ultrasonic condition, which is 7.5 times that of H-PDA. Theoretical calculations and experimental observations indicate that the strongest flexible-robust structure of N-PDA enhances the inclination of the rigid plane and atomic bond length, resulting in an accelerated uneven distribution of charges, molecular polarity, and electronic coupling to facilitate charge separation dynamics. Moreover, it exposes active sites that promote the adsorption and activation of NO3- ions while simultaneously regulating intermediate hydrogenation processes. Our study offers innovative prospects for enhancing catalytic efficiency through the implementation of spatial steric configuration of π-conjugated polymers.