scholarly journals Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63801-63808 ◽  
Author(s):  
He Ming Yao ◽  
Min Li ◽  
Lijun Jiang
Keyword(s):  
Deep Learning ◽  
Near Field ◽  
Learning Approach ◽  
Far Field ◽  
Subwavelength Imaging ◽  
Microwave Frequencies

scholarly journals Far-field subwavelength imaging with near-field resonant metalens scanning at microwave frequencies

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Ren Wang ◽  
Bing-Zhong Wang ◽  
Zhi-Shuang Gong ◽  
Xiao Ding
Keyword(s):  
Near Field ◽  
Far Field ◽  
Subwavelength Imaging ◽  
Microwave Frequencies

Magnetic Tag Antenna for UHF Near-field and Far-Field RFID Applications

2015 ◽  
Vol 5 (2) ◽  
pp. 119
Author(s):  
Mondher Dhaouadi ◽  
M. Mabrouk ◽  
T. Vuong ◽  
A. Ghazel
Keyword(s):  
Near Field ◽  
Far Field ◽  
Rfid Applications

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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