Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

2020 ◽  
Vol 26 (3) ◽  
pp. 204-212
Author(s):  
Anastasia Sarycheva ◽  
Alexey Adamov ◽  
Sergey S Poteshin ◽  
Sergey S Lagunov ◽  
Alexey A Sysoev
Keyword(s):  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Utilization Efficiency ◽  
Hadamard Transform ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Pseudorandom Sequences ◽  
Noise Ratio

In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

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

Analytical model for the signal-to-noise-ratio of drift tube ion mobility spectrometers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ansgar T. Kirk ◽  
Alexander Bohnhorst ◽  
Stefan Zimmermann
Keyword(s):  
Ion Mobility ◽  
Drift Tube ◽  
Signal To Noise Ratio ◽  
Resolving Power ◽  
Operating Point ◽  
Optimum Operating ◽  
Signal To Noise ◽  
Experimental Parameters ◽  
Noise Ratio ◽  
Optimum Operating Point

Abstract While the resolving power of drift tube ion mobility spectrometers has been studied and modelled in detail over the past decades, no comparable model exists for the signal-to-noise-ratio. In this work, we develop an analytical model for the signal-to-noise-ratio of a drift tube ion mobility spectrometer based on the same experimental parameters used for modelling the resolving power. The resulting holistic model agrees well with experimental results and allows simultaneously optimizing both resolving power and signal-to-noise-ratio. Especially, it reveals several unexpected relationships between experimental parameters. First, even though reduced initial ion packet widths result in fewer injected ions and reduced amplifier widths result in more noise, the resulting shift of the optimum operating point when reducing both simultaneously leads to a constant signal-to-noise-ratio. Second, there is no dependence of the signal-to-noise-ratio at the optimum operating point on the drift length, as again the resulting shift of the optimum operating point causes all effects to compensate each other.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

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

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.

Signal-to-Noise Ratio Enhancement of Spectra using Recursive Integration

1989 ◽  
Vol 43 (8) ◽  
pp. 1409-1413 ◽  
Author(s):  
Ron Williams
Keyword(s):  
Signal To Noise Ratio ◽  
Recursive Algorithm ◽  
Synthetic Data ◽  
Spectral Elements ◽  
Fourier Transform Spectrometer ◽  
Peak Shape ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Recursive Integration

A recursive algorithm independent of any functional peak shape is presented for determining optimal integration limits of spectral data from multiwavelength spectrometers. The resulting areas have significantly higher signal-to-noise ratios than the peak maxima. Signal-to-noise ratios are computed for synthetic data with both shot and white noise limitations. The algorithm is also applied to data from a Fourier transform spectrometer. For these data, integration of 25 adjacent spectral elements improves the signal-to-noise ratio as well as the signal averaging peak maxima from 25 successive spectra.

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