scholarly journals A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3041
Author(s):  
Eduardo Trutié-Carrero ◽  
Diego Seuret-Jimenez ◽  
José M. Nieto-Jalil
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Ratio ◽  
The Fourier Transform ◽  
Non Stationary Signal

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

scholarly journals Signal-to-Noise Ratio and Astronomical Fourier Transform Spectroscopy

1988 ◽  
Vol 132 ◽  
pp. 71-78
Author(s):  
J. P. Maillard
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Resolving Power ◽  
High Resolution Spectroscopy ◽  
Signal To Noise ◽  
Array Detectors ◽  
Noise Ratio ◽  
The Fourier Transform ◽  
Large Telescopes ◽  
Very High

The multiplex properties of the Fourier Transform Spectrometer (FTS) can be considered as disadvantageous with modern detectors and large telescopes, the dominant noise source being no longer in most applications the detector noise. Nevertheless, a FTS offers a gain in information and other instrumental features remain: flexibility in choosing resolving power up to very high values, large throughput, essential in high–resolution spectroscopy with large telescopes, metrologic accuracy, automatic substraction of parasitic background. The signal–to–noise ratio in spectra can also be improved: by limiting the bandwidth with cold filters or even cold dispersers, by matching the instrument to low background foreoptics and high–image quality telescopes. The association with array detectors provides the solution for the FTS to regain its full multiplex advantage.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Signal-to-noise ratio and section thickness in two-dimensional versus three-dimensional Fourier transform MR imaging.

Radiology ◽  
1988 ◽  
Vol 166 (1) ◽  
pp. 266-270 ◽  
Author(s):  
J Carlson ◽  
L Crooks ◽  
D Ortendahl ◽  
D M Kramer ◽  
L Kaufman
Keyword(s):  
Fourier Transform ◽  
Mr Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Section Thickness ◽  
Signal To Noise ◽  
Noise Ratio

Improvement of the signal-to-noise ratio of chromatographic peaks by Fourier transform

1991 ◽  
Vol 248 (2) ◽  
pp. 441-446 ◽  
Author(s):  
Attila Felinger ◽  
Tamás L. Pap ◽  
János Inczédy
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Chromatographic Peaks

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals A New Multichannel Fourier Transform Spectrometer

1999 ◽  
Vol 170 ◽  
pp. 36-40
Author(s):  
Tyler E. Nordgren ◽  
Arsen R. Hajian
Keyword(s):  
Fourier Transform ◽  
Extrasolar Planets ◽  
Signal To Noise Ratio ◽  
Fourier Transform Spectrometer ◽  
Test Bed ◽  
Signal To Noise ◽  
Stellar Spectra ◽  
Band Emission ◽  
Noise Ratio ◽  
Ratio Scales

AbstractStellar spectra have been obtained using a multichannel Fourier Transform Spectrometer (FTS) which incorporates components of the Navy Prototype Optical Interferometer. It is well known that a FTS can provide superior wavelength stability as compared to traditional spectrometers. Unfortunately the FTS traditionally suffers from exceptionally poor sensitivity, which until now has limited its uses to sources with high fluxes and/or those with narrow band emission (e.g. the Sun, nebulae, and laboratory samples). We present stellar observations using a new FTS design which overcomes this sensitivity limitation by using a conventional multichannel spectrometer in conjunction with the FTS system. The signal-to-noise ratio of spectra from our test-bed observations are consistent with the theoretical prediction and show that for N channels the sensitivity scales like N, while the signal-to-noise ratio scales like . With this type of an instrument on a 3-m telescope and 9 000 channels we expect to be able to detect and measure such exciting astrophysical phenomenon as gravitational redshifts from single, main sequence stars and extrasolar planets of terrestrial mass.

Signal to Noise Ratio Enhancement Using Empirical Wavelet Transform

2019 ◽  
Author(s):  
Wei Yi Lee ◽  
Rosita Hamidi ◽  
Deva Ghosh ◽  
Mohd Hafiz Musa
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Empirical Wavelet Transform ◽  
Noise Ratio
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