Research of Terahertz Spectra of Terbutaline Sulfate Based on Wavelet De-Noising

2011 ◽  
Vol 63-64 ◽  
pp. 327-332
Author(s):  
Xiai Chen ◽  
Ping Jie Huang ◽  
Di Bo Hou ◽  
Xu Sheng Kang ◽  
Guang Xin Zhang ◽  
...  
Keyword(s):  
Time Domain ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Discrete Wavelet ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Terbutaline Sulfate ◽  
Soft Threshold

Terahertz spectra of terbutaline sulfate in the range of 0.2 to 2.2 THz was obtained by THz time-domain spectroscopy. The discrete wavelet transform was applied to de-noising terahertz waveforms. The signal was decomposed into five layers by wavelet decomposition, and then the high-frequency noise signal was eliminated by wavelet reconstruction. Another try was through calculating the standard deviation of the noise signal by the 1-th level signals which got from wavelet decomposition, and then the soft threshold and hard threshold de-noising method was employed respectively. The robustness of these wavelet de-noising methods was testified in this paper, and the absorption and refraction spectra of terbutaline sulfate were got at last. The result of experiment indicts that wavelet can enhance the signal to noise ratio of system and this paper provides a new way for the detection of terbutaline sulfate.

scholarly journals Audio Hiding based on Wavelet Transform and Linear Predictive Coding

2016 ◽  
Vol 42 (1) ◽  
pp. 30-37
Author(s):  
Jamal Hasoon ◽  
Saad Al-Saad
Keyword(s):  
Wavelet Transform ◽  
Efficient Method ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Signal To Noise ◽  
Log Likelihood ◽  
Objective Tests

In this work an efficient method for hiding a speech in audio is proposed. The features of secretspeech is extracted with LPC (Linear Predictive Coding), and these parameters embedded in audio inchaotic order. Discrete Wavelet Transform (DWT) is applied on audio frames to split the signal in high andlow frequencies. The embedding parameters are embedded in high frequency. The stego audio isperceptually indistinguishable from the equivalent cover audio. The proposed method allows hiding a sameduration of speech (secret) and audio (cover). The stego audio is subjected to objective tests such signal to noiseratio (SNR), signal to noise ratio segmental (SNRseg), Segmental Spectral SNR, Log Likelihood Ratio (LLR)and Correlation (Rxy) to determine the similarity with original audio.

scholarly journals Agro Image De-Noising (Aid) for Enhanced Agricultural Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2474-2478
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Photographic Images ◽  
Spline Wavelets ◽  
Soft Threshold ◽  
Noise Ratio ◽  
Processing Techniques

Several Noises may be present in acquired images. This is an undesired feature for image processing techniques that analyze these images. Image de-noising helps improve efficiency of image processing. Many image de-noising methods have been proposed and exist in literature. Image de-noising methods for agricultural images have been proposed to a lesser extent when compared to the bright medical or photographic images. This paper proposes Agricultural Image De-noising (AID) which uses a discrete wavelet transform (DWT) to eliminate noise in agricultural images. This study uses specific kind of wavelet family spline wavelet transforms with appropriate decomposition level and the wavelet coefficients are analysed with hard and soft threshold methods. The denoised image using various spline wavelets is compared of hard threshold and soft threshold are assessed. The performance of AID is calculated using the peak signal to noise ratio (PSNR) and signal to noise ratio (SNR).

Evaluation of the Time Varying Gravity Fields from GRACE Follow-On LRI Instrument

2020 ◽  
Author(s):  
Nadege Pie ◽  
Mark Tamisiea ◽  
Ben Krichman ◽  
Peter Nagel ◽  
Steve Poole ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Gravity Models ◽  
Laser Ranging ◽  
Frequency Noise ◽  
Time Varying ◽  
Signal To Noise ◽  
Science Data ◽  
Design Concepts ◽  
Gravity Fields

<p>The Laser Ranging Interferometer (LRI) instrument on-board of the GRACE Follow-On (GRACE-FO) satellites has been collecting science data since a June 2018, a few weeks after launch. Though the LRI instrument, based on design concepts developed for a future LISA mission, was intended mostly as a demonstration instrument, it has far-exceeded its mission requirements and has provided intersatellite ranging observations with improved signal-to-noise ratio compared to the K-Band Ranging (KBR) instrument. The exploitation of the LRI observations has led to a set of monthly gravity field solutions comparable in many ways to the ones obtained from KBR. Though the ranging observations from the LRI have a much lower high-frequency noise content, this has not so far led to an improvement of the time-varying gravity estimates in the spatial domain, while stark differences are visible in the spectral domain between the KBR and LRI fields. We present the series of LRI gravity models in comparison to its KBR counterpart, as well as regional intercomparisons of the gravity solutions against hydrology models.</p>

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

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.

Multichannel absorption compensation with a data-driven structural regularization

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. V71-V80 ◽  
Author(s):  
Xiong Ma ◽  
Guofa Li ◽  
Hao Li ◽  
Wuyang Yang
Keyword(s):  
Cost Function ◽  
Structural Characteristic ◽  
Seismic Data ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Frequency Noise ◽  
Characteristic Operator ◽  
High Frequency Noise ◽  
The Cost

Seismic absorption compensation is an important processing approach to mitigate the attenuation effects caused by the intrinsic inelasticity of subsurface media and to enhance seismic resolution. However, conventional absorption compensation approaches ignore the spatial connection along seismic traces, which makes the compensation result vulnerable to high-frequency noise amplification, thus reducing the signal-to-noise ratio (S/N) of the result. To alleviate this issue, we have developed a structurally constrained multichannel absorption compensation (SC-MAC) algorithm. In the cost function of this algorithm, we exploit an [Formula: see text] norm to constrain the reflectivity series and an [Formula: see text] norm to regularize the reflection structural characteristic of the compensation data. The reflection structural characteristic operator, extracted from the observed stacked seismic data, is the core of the structural regularization term. We then solve the cost function of SC-MAC by the alternating direction method of multipliers. Benefiting from the introduction of reflection structure constraint, SC-MAC improves the stability of the compensation result and inhibits the amplification of high-frequency noise. Synthetic and field data examples demonstrate that our proposed method is more robust to random noise and can not only improve the resolution of seismic data, but also maintain the S/N of the compensation seismic data.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

Convolutive Blind Source Separation Applied to the Communication Signals

2012 ◽  
Vol 263-266 ◽  
pp. 188-191
Author(s):  
Xiu Ying Sun ◽  
Peng Fei Xu
Keyword(s):  
Independent Component Analysis ◽  
Time Domain ◽  
Wavelet Decomposition ◽  
Threshold Model ◽  
Automatic Method ◽  
Blind Separation ◽  
Communication Signals ◽  
Communication Signal ◽  
Soft Threshold ◽  
Convolutive Blind Source Separation

In this paper, a method for convolutive blind separation for communication sources is introduced. The method works in time-domain, and it is based on the recently very successful algorithm EFICA for Independent Component Analysis, which is an enhanced version of more famous FastICA. In addition, an automatic method of wavelet de-noising processing is proposed, using the 'mini-maxi' soft-threshold model, wavelet decomposition is performed at level 5 for the noisy separated communication signal, it can improve the performance of BSS system, and this is confirmed in the experiment for communication signals with same carrier frequencies and modulation.

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