140 research outputs found
LEDCNet: A Lightweight and Efficient Semantic Segmentation Algorithm Using Dual Context Module for Extracting Ground Objects from UAV Aerial Remote Sensing Images
Semantic segmentation for extracting ground objects, such as road and house,
from UAV remote sensing images by deep learning becomes a more efficient and
convenient method than traditional manual segmentation in surveying and mapping
field. In recent years, with the deepening of layers and boosting of
complexity, the number of parameters in convolution-based semantic segmentation
neural networks considerably increases, which is obviously not conducive to the
wide application especially in the industry. In order to make the model
lightweight and improve the model accuracy, a new lightweight and efficient
network for the extraction of ground objects from UAV remote sensing images,
named LEDCNet, is proposed. The proposed network adopts an encoder-decoder
architecture in which a powerful lightweight backbone network called LDCNet is
developed as the encoder. We would extend the LDCNet become a new generation
backbone network of lightweight semantic segmentation algorithms. In the
decoder part, the dual multi-scale context modules which consist of the ASPP
module and the OCR module are designed to capture more context information from
feature maps of UAV remote sensing images. Between ASPP and OCR, a FPN module
is used to and fuse multi-scale features extracting from ASPP. A private
dataset of remote sensing images taken by UAV which contains 2431 training
sets, 945 validation sets, and 475 test sets is constructed. The proposed model
performs well on this dataset, with only 1.4M parameters and 5.48G FLOPs,
achieving an mIoU of 71.12%. The more extensive experiments on the public
LoveDA dataset and CITY-OSM dataset to further verify the effectiveness of the
proposed model with excellent results on mIoU of 65.27% and 74.39%,
respectively. All the experimental results show the proposed model can not only
lighten the network with few parameters but also improve the segmentation
performance.Comment: 11 page
Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion
Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the
important means to assess cardiac functions and vascular abnormalities.
However, due to cardiac beat, blood flow, or the patient's involuntary movement
during the long acquisition, the reconstructed images are prone to motion
artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac
cine MRI acquisition to achieve high-quality images is necessary for clinical
practice. Approach: A novel end-to-end deep learning network is developed to
improve cardiac cine MRI reconstruction under free breathing conditions. First,
a U-Net is adopted to obtain the initial reconstructed images in k-space.
Further to remove the motion artifacts, the Motion-Guided Deformable Alignment
(MGDA) method with second-order bidirectional propagation is introduced to
align the adjacent cine MRI frames by maximizing spatial-temporal information
to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF)
module is designed to correct the blur and artifacts generated from alignment
operation and obtain the last high-quality reconstructed cardiac images. Main
results: At an 8 acceleration rate, the numerical measurements on the
ACDC dataset are SSIM of 78.40%4.57%, PSNR of 30.461.22 dB, and NMSE
of 0.04680.0075. On the ACMRI dataset, the results are SSIM of
87.65%4.20%, PSNR of 30.041.18 dB, and NMSE of 0.04730.0072.
Significance: The proposed method exhibits high-quality results with richer
details and fewer artifacts for cardiac cine MRI reconstruction on different
accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure
Anomalous thermal transport across the superionic transition in ice
Superionic ices with highly mobile protons within the stable oxygen
sub-lattice occupy an important proportion of the phase diagram of ice and
widely exist in the interior of icy giants and throughout the universe.
Understanding the thermal transport in superionic ice is vital for the thermal
evolution of icy planets. However, it is highly challenging due to the extreme
thermodynamic conditions and dynamical nature of protons, beyond the capability
of the traditional lattice dynamics and empirical potential molecular dynamics
approaches. In this work, by utilizing the deep potential molecular dynamics
approach, we investigate the thermal conductivity of ice-VII and superionic
ice-VII" along the isobar of . A non-monotonic trend of
thermal conductivity with elevated temperature is observed. Through heat flux
decomposition and trajectory-based spectra analysis, we show that the
thermally-activated proton diffusion in ice-VII and superionic ice-VII"
contribute significantly to heat convection, while the broadening in
vibrational energy peaks and significant softening of transverse acoustic
branches lead to a reduction in heat conduction. The competition between proton
diffusion and phonon scattering results in anomalous thermal transport across
the superionic transition in ice. This work unravels the important role of
proton diffusion in the thermal transport of high-pressure ice. Our approach
provides new insights into modeling the thermal transport and atomistic
dynamics in superionic materials.Comment: 5 figure
Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine
Cultivated land in Middle-lower Yangtze Plain has been greatly reduced over the last few decades due to rapid urban expansion and massive urban construction. Accurate and timely monitoring of cultivated land changes has significant for regional food security and the impact of national land policy on cultivated land dynamics. However, generating high-resolution spatial-temporal records of cultivated land dynamics in complex areas remains difficult due to the limitations of computing resources and the diversity of land cover over a complex region. In our study, the annual dynamics of cultivated land in Middle-lower Yangtze Plain were first produced at 30 m resolution with a one-year interval in 1990–2010.Changes of vegetation and cultivated land are examined with the breakpoints inter-annual Normalized Difference Vegetation Index (NDVI) trajectories and synthetic NDVI derived by the enhanced spatial and temporal adaptive reflectance fusion model (ESTRAFM), respectively. Last, cultivated land dynamics is extracted with a one-year interval by detecting phenological characteristic. The results indicate that the rate of reduction in cultivated land area has accelerated over the past two decades, and has intensified since 1997.The dynamics of cultivated land mainly occurred in the mountains, hills, lakes and around towns, and the change frequency of these area was mainly one or two times. In particular, the changes in cultivated land in Nanjing have been most intense, perhaps attributed to urban greening and infrastructure construction
Applications of Electromagnetic Forming Technology at the Wuhan National High Magnetic Field Center
The research of the electromagnetic forming (EMF) technology at the Wuhan National High Magnetic Field Center (WHMFC) has focused on designing electromagnetic system for generating a more flex-ible and strong Lorentz forces acting on workpieces, and then expanding the applications of EMF technology to solve current problems in forming large-scale and complex components. In this paper, we will sum up the latest progress of EMF technology at the WHMFC in detail according to recently reported works
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a
novel self-supervised method for recognizing standard views in pediatric
echocardiography. EDMAE introduces a new proxy task based on the
encoder-decoder structure. The EDMAE encoder is composed of a teacher and a
student encoder. The teacher encoder extracts the potential representation of
the masked image blocks, while the student encoder extracts the potential
representation of the visible image blocks. The loss is calculated between the
feature maps output by the two encoders to ensure consistency in the latent
representations they extract. EDMAE uses pure convolution operations instead of
the ViT structure in the MAE encoder. This improves training efficiency and
convergence speed. EDMAE is pre-trained on a large-scale private dataset of
pediatric echocardiography using self-supervised learning, and then fine-tuned
for standard view recognition. The proposed method achieves high classification
accuracy in 27 standard views of pediatric echocardiography. To further verify
the effectiveness of the proposed method, the authors perform another
downstream task of cardiac ultrasound segmentation on the public dataset CAMUS.
The experimental results demonstrate that the proposed method outperforms some
popular supervised and recent self-supervised methods, and is more competitive
on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal
Processing and Contro
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