Photoswitchable aggregation-induced emission of a dithienylethene–tetraphenylethene conjugate for optical memory and super-resolution imaging

RSC Advances ◽  
2013 ◽  
Vol 3 (23) ◽  
pp. 8967 ◽  
Author(s):  
Chong Li ◽  
Wen-Liang Gong ◽  
Zhe Hu ◽  
Matthew P. Aldred ◽  
Guo-Feng Zhang ◽  
...  
Keyword(s):  
Optical Memory ◽  
Super Resolution ◽  
Aggregation Induced Emission ◽  
Resolution Imaging

scholarly journals Facilely prepared aggregation-induced emission (AIE) nanocrystals with deep-red emission for super-resolution imaging

2022 ◽  
Author(s):  
Ruohan Xu ◽  
Dongfeng Dang ◽  
Zhi Wang ◽  
Yu Zhou ◽  
Yanzi Xu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Biological Imaging ◽  
Fast Method ◽  
High Brightness ◽  
Red Emission ◽  
Aggregation Induced Emission ◽  
Organic Nanocrystals ◽  
Resolution Imaging

Organic nanocrystals (NCs) with high brightness are highly desirable for biological imaging. However, the preparation of NCs in a facile and fast method is still challenging. Herein, aggregation-induced emission (AIE)...

Unraveling Dual Aggregation‐Induced Emission Behavior in Steric‐Hindrance Photochromic System for Super Resolution Imaging

2019 ◽  
Vol 132 (22) ◽  
pp. 8638-8648 ◽  
Author(s):  
Hong Yang ◽  
Mengqi Li ◽  
Chong Li ◽  
Qianfu Luo ◽  
Ming‐Qiang Zhu ◽  
...  
Keyword(s):  
Steric Hindrance ◽  
Super Resolution ◽  
Aggregation Induced Emission ◽  
Emission Behavior ◽  
Resolution Imaging

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.

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