scholarly journals Single-Core Multiscale Residual Network for the Super Resolution of Liquid Metal Specimen Images

2021 ◽  
Vol 3 (2) ◽  
pp. 453-466
Author(s):  
Keqing Ning ◽  
Zhihao Zhang ◽  
Kai Han ◽  
Siyu Han ◽  
Xiqing Zhang
Keyword(s):  
Liquid Metal ◽  
High Speed ◽  
Liquid Metals ◽  
Super Resolution ◽  
Network Size ◽  
Microgravity Environment ◽  
Undercooled Liquid ◽  
Low Resolution ◽  
Residual Network ◽  
Training Time

In a gravity-free or microgravity environment, liquid metals without crystalline nuclei achieve a deep undercooling state. The resulting melts exhibit unique properties, and the research of this phenomenon is critical for exploring new metastable materials. Owing to the rapid crystallization rates of deeply undercooled liquid metal droplets, as well as cost concerns, experimental systems meant for the study of liquid metal specimens usually use low-resolution, high-framerate, high-speed cameras, which result in low-resolution photographs. To facilitate subsequent studies by material scientists, it is necessary to use super-resolution techniques to increase the resolution of these photographs. However, existing super-resolution algorithms cannot quickly and accurately restore the details contained in images of deeply undercooled liquid metal specimens. To address this problem, we propose the single-core multiscale residual network (SCMSRN) algorithm for photographic images of liquid metal specimens. In this model, multiple cascaded filters are used to obtain feature information, and the multiscale features are then fused by a residual network. Compared to existing state-of-the-art artificial neural network super-resolution algorithms, such as SRCNN, VDSR and MSRN, our model was able to achieve higher PSNR and SSIM scores and reduce network size and training time.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

Liquid metal-based nanocomposite materials: Fabrication technology and applications

Nanoscale ◽  
2021 ◽  
Author(s):  
Hiroki Ota ◽  
Nyamjargal Ochirkhuyag ◽  
Ryosuke Matsuda ◽  
Zihao Song ◽  
Fumika Nakamura ◽  
...  
Keyword(s):  
Liquid Metal ◽  
Flexible Electronics ◽  
Liquid Metals ◽  
Nanocomposite Materials ◽  
Fabrication Technology

Research on liquid metals has been steadily garnering more interest in recent times because the properties of these metals are conducive to flexible electronics applications; further, these metals are in...

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

Kernel-attended residual network for single image super-resolution

2021 ◽  
Vol 213 ◽  
pp. 106663
Author(s):  
Yujie Dun ◽  
Zongyang Da ◽  
Shuai Yang ◽  
Yao Xue ◽  
Xueming Qian
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Cargo sorting zones in the trans-Golgi network visualized by super-resolution confocal live imaging microscopy in plants

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yutaro Shimizu ◽  
Junpei Takagi ◽  
Emi Ito ◽  
Yoko Ito ◽  
Kazuo Ebine ◽  
...  
Keyword(s):  
Membrane Trafficking ◽  
High Speed ◽  
Super Resolution ◽  
Adaptor Protein ◽  
Transport Proteins ◽  
3D Localization ◽  
Golgi Network ◽  
Distinct Distribution ◽  
Vacuolar Trafficking ◽  
Cargo Sorting

AbstractThe trans-Golgi network (TGN) has been known as a key platform to sort and transport proteins to their final destinations in post-Golgi membrane trafficking. However, how the TGN sorts proteins with different destinies still remains elusive. Here, we examined 3D localization and 4D dynamics of TGN-localized proteins of Arabidopsis thaliana that are involved in either secretory or vacuolar trafficking from the TGN, by a multicolor high-speed and high-resolution spinning-disk confocal microscopy approach that we developed. We demonstrate that TGN-localized proteins exhibit spatially and temporally distinct distribution. VAMP721 (R-SNARE), AP (adaptor protein complex)−1, and clathrin which are involved in secretory trafficking compose an exclusive subregion, whereas VAMP727 (R-SNARE) and AP-4 involved in vacuolar trafficking compose another subregion on the same TGN. Based on these findings, we propose that the single TGN has at least two subregions, or “zones”, responsible for distinct cargo sorting: the secretory-trafficking zone and the vacuolar-trafficking zone.

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