Signal-to-noise ratio of nondestructive photocurrent-detection readout in near-field photochromic memory: theoretical study

2002 ◽  
Vol 19 (2) ◽  
pp. 297 ◽  
Author(s):  
Tsuyoshi Tsujioka ◽  
Masahiro Irie
Keyword(s):  
Theoretical Study ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

A high signal-to-noise ratio passive near-field microscope equipped with a helium-free cryostat

2017 ◽  
Vol 88 (1) ◽  
pp. 013706 ◽  
Author(s):  
Kuan-Ting Lin ◽  
Susumu Komiyama ◽  
Sunmi Kim ◽  
Ken-ichi Kawamura ◽  
Yusuke Kajihara
Keyword(s):  
Signal To Noise Ratio ◽  
Near Field ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio

Near-Field Plasmonic Probe with Super Resolution and High Throughput and Signal-to-Noise Ratio

Nano Letters ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 881-885 ◽  
Author(s):  
Ruei-Han Jiang ◽  
Chi Chen ◽  
Ding-Zheng Lin ◽  
He-Chun Chou ◽  
Jen-You Chu ◽  
...  
Keyword(s):  
High Throughput ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Super Resolution ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Near-field nanoprobing using Si tip-Au nanoparticle photoinduced force microscopy with 120:1 signal-to-noise ratio, sub-6-nm resolution

2018 ◽  
Vol 26 (20) ◽  
pp. 26365 ◽  
Author(s):  
Mohsen Rajaei ◽  
Mohammad Ali Almajhadi ◽  
Jinwei Zeng ◽  
H. Kumar Wickramasinghe
Keyword(s):  
Signal To Noise Ratio ◽  
Near Field ◽  
Au Nanoparticle ◽  
Signal To Noise ◽  
Force Microscopy ◽  
Noise Ratio
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