The Effect of a Single Defective Mask Element on the Multiplex Advantage in Hadamard Transform Spectroscopy

1989 ◽  
Vol 43 (2) ◽  
pp. 278-283 ◽  
Author(s):  
Stephen A. Dyer ◽  
Jin Bae Park
Keyword(s):  
Output Signal ◽  
Signal To Noise Ratio ◽  
Hadamard Transform ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Defective Element ◽  
Output Snr

The effect of a single defective mask element on the output signal-to-noise ratio (SNR) for a stationary-mask Hadamard transform (HT) spectrometer is investigated. The decrease in output-SNR from that of an HT spectrometer having a perfect mask is found to be dependent on the amount of energy impinging on the defective element. A method of compensating for the defective mask element is presented. The method is computationally inexpensive and can be fully automated.

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.

Signal-to-noise ratio enhancement of a Hadamard transform spectrometer using a two-dimensional slit-array

2017 ◽  
Vol 56 (25) ◽  
pp. 7188 ◽  
Author(s):  
Mingbo Chi ◽  
Yihui Wu ◽  
Fang Qian ◽  
Peng Hao ◽  
Wenchao Zhou ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Hadamard Transform ◽  
Two Dimensional ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Output signal-to-noise ratio and speech perception in noise: effects of algorithm

2017 ◽  
Vol 56 (8) ◽  
pp. 568-579 ◽  
Author(s):  
Christi W. Miller ◽  
Ruth A. Bentler ◽  
Yu-Hsiang Wu ◽  
James Lewis ◽  
Kelly Tremblay
Keyword(s):  
Speech Perception ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Speech Perception In Noise ◽  
Noise Effects ◽  
Noise Ratio
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