scholarly journals Cyanine polyene reactivity: scope and biomedical applications

2015 ◽  
Vol 13 (28) ◽  
pp. 7584-7598 ◽  
Author(s):  
Alexander P. Gorka ◽  
Roger R. Nani ◽  
Martin J. Schnermann
Keyword(s):  
Biomedical Applications ◽  
Optical Sensing ◽  
Super Resolution ◽  
Covalent Modification ◽  
Near Ir ◽  
Resolution Imaging

Reactions involving the covalent modification of the cyanine polyene are enabling emerging approaches in optical sensing, super-resolution imaging, and near-IR uncaging.

scholarly journals Triazole-based osmium(ii) complexes displaying red/near-IR luminescence: antimicrobial activity and super-resolution imaging

2020 ◽  
Vol 11 (33) ◽  
pp. 8928-8935
Author(s):  
Kirsty L. Smitten ◽  
Paul A. Scattergood ◽  
Charlotte Kiker ◽  
Jim A. Thomas ◽  
Paul I. P. Elliott
Keyword(s):  
Antimicrobial Activity ◽  
Cellular Uptake ◽  
Super Resolution ◽  
Methicillin Resistant ◽  
Luminescence Imaging ◽  
Near Ir ◽  
Resolution Imaging

Cellular uptake, luminescence imaging and antimicrobial activity of facial and meridional isomers of Os(ii) triazole-based complexes against methicillin-resistant S. aureus, MRSA.

Photon avalanche in lanthanide doped nanoparticles for biomedical applications: super-resolution imaging

2019 ◽  
Vol 4 (4) ◽  
pp. 881-889 ◽  
Author(s):  
Artur Bednarkiewicz ◽  
Emory M. Chan ◽  
Agata Kotulska ◽  
Lukasz Marciniak ◽  
Katarzyna Prorok
Keyword(s):  
Biomedical Applications ◽  
Super Resolution ◽  
Diffraction Imaging ◽  
Resolution Imaging

Photon avalanche in lanthanide doped nanoparticles shows exceptional properties, potentially suitable for single photoexcitation beam sub-diffraction 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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