scholarly journals High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7038
Author(s):  
Hui Xie ◽  
Zhuang Zhao ◽  
Jing Han ◽  
Lianfa Bai ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Hadamard Transform ◽  
High Signal ◽  
Signal To Noise ◽  
Compact Structure ◽  
Reconstructed Signal ◽  
Noise Ratio

Spectral detection provides rich spectral–temporal information with wide applications. In our previous work, we proposed a dual-path sub-Hadamard-s snapshot Hadamard transform spectrometer (Sub-s HTS). In order to reduce the complexity of the system and improve its performance, we present a convolution neural network-based method to recover the light intensity distribution from the overlapped dispersive spectra, rather than adding an extra light path to capture it directly. In this paper, we construct a network-based single-path snapshot Hadamard transform spectrometer (net-based HTS). First, we designed a light intensity recovery neural network (LIRNet) with an unmixing module (UM) and an enhanced module (EM) to recover the light intensity from the dispersive image. Then, we used the reconstructed light intensity as the original light intensity to recover high signal-to-noise ratio spectra successfully. Compared with Sub-s HTS, the net-based HTS has a more compact structure and high sensitivity. A large number of simulations and experimental results have demonstrated that the proposed net-based HTS can obtain a better-reconstructed signal-to-noise ratio spectrum than the Sub-s HTS because of its higher light throughput.

A Carbon-Based DNA Framework Nano–Bio Interface for Biosensing with High Sensitivity and a High Signal-to-Noise Ratio

ACS Sensors ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 3979-3987
Author(s):  
Jing Su ◽  
Wenhan Liu ◽  
Shixing Chen ◽  
Wangping Deng ◽  
Yanzhi Dou ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Carbon Based

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

Narrow-frequency sharp-angular filters using all-dielectric cascaded meta-gratings

Nanophotonics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 3443-3450 ◽  
Author(s):  
Wei-Nan Liu ◽  
Rui Chen ◽  
Wei-Yi Shi ◽  
Ke-Bo Zeng ◽  
Fu-Li Zhao ◽  
...  
Keyword(s):  
Thin Film ◽  
Signal To Noise Ratio ◽  
Incident Angle ◽  
Design Strategy ◽  
Transmission Peak ◽  
High Signal ◽  
Waveguide Array ◽  
Signal To Noise ◽  
Center Wavelength ◽  
Noise Ratio

AbstractSelective transmission or filtering always responds to either frequency or incident angle, so as hardly to maximize signal-to-noise ratio in communication, detection and sensing. Here, we propose compact meta-filters of narrow-frequency sharp-angular transmission peak along with broad omnidirectional reflection sidebands, in all-dielectric cascaded subwavelength meta-gratings. The inherent collective resonance of waveguide-array modes and thin film approximation of meta-grating are employed as the design strategy. A unity transmission peak, locating at the incident angle of 44.4° and the center wavelength of 1550 nm, is demonstrated in a silicon meta-filter consisting of two-layer silicon rectangular meta-grating. These findings provide possibilities in cascaded meta-gratings spectroscopic design and alternative utilities for high signal-to-noise ratio applications in focus-free spatial filtering and anti-noise systems in telecommunications.

scholarly journals Low-dose, phase-contrast mammography with high signal-to-noise ratio

2016 ◽  
Vol 7 (2) ◽  
pp. 381 ◽  
Author(s):  
Lukas B. Gromann ◽  
Dirk Bequé ◽  
Kai Scherer ◽  
Konstantin Willer ◽  
Lorenz Birnbacher ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

High signal-to-noise ratio quantum well bolometer materials

2006 ◽  
Author(s):  
Stanley Wissmar ◽  
Linda Höglund ◽  
Jan Andersson ◽  
Christian Vieider ◽  
Susan Savage ◽  
...  
Keyword(s):  
Quantum Well ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio

The Effect of Pseudorandom Sequence Systematicity on Signal-to-Noise Ratio in Hadamard Transform Ion Mobility Spectrometry

2021 ◽  
Vol 76 (13) ◽  
pp. 1485-1492
Author(s):  
A. P. Sarycheva ◽  
A. Yu. Adamov ◽  
S. S. Lagunov ◽  
G. V. Lapshov ◽  
S. S. Poteshin ◽  
...  
Keyword(s):  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Signal To Noise Ratio ◽  
Hadamard Transform ◽  
Pseudorandom Sequence ◽  
Signal To Noise ◽  
Noise Ratio

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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