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inbuilt function awgn(y, snr) of matlab additive white gaussian noise (awgn)  (MathWorks Inc)


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    MathWorks Inc inbuilt function awgn(y, snr) of matlab additive white gaussian noise (awgn)
    Inbuilt Function Awgn(y, Snr) Of Matlab Additive White Gaussian Noise (Awgn), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/awgn(snr)+function/10__1007_slash_s42979___024___02774___9-246-3-8
    Average 90 stars, based on 1 article reviews
    inbuilt function awgn(y, snr) of matlab additive white gaussian noise (awgn) - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Fast and accurate wavefront reconstruction in two-frame phase-shifting interferometry with unknown phase step
    Article Snippet: A fast and accurate wavefront reconstruction method for two-frame phase-shifting interferometry is proposed.. The unknown phase step between the two interferograms is estimated directly by solving a quartic polynomial equation, and then the phase map is readily reconstructed after obtaining the phase step.. The whole phase reconstruction process is nearly analytical and thus very fast and easy to realize.

    Article Title: Noise Robust Audio Spoof Detection Using Hybrid Feature Extraction and LCNN
    Article Snippet: In recent times, Voice Controlled Devices (VCD) have become an important part of our life.. However, these devices are vulnerable to replay attacks.. Hence, to address the issue, this paper proposes an Automatic Speaker Verification (ASV) system that uses hybrid feature extraction technique at front end and Light Convolutional Neural Network (LCNN) at back end.

    Article Title: Randomized Tensor Wheel Decomposition
    Article Snippet: Tensor wheel (TW) decomposition is an elegant compromise of the popular tensor ring decomposition and fully connected tensor network decomposition, and it has many applications.. In this work, we investigate the computation of this decomposition.. Three randomized algorithms based on random sampling or random projection are proposed.

    Article Title: Experimental Study of Ghost Imaging in Underwater Environment.
    Article Snippet: We use the MATLAB function awgn (x, SNR, signalpower) to add WGN into the input signal, where x, SNR and signalpower are the input signal, noise level, and signal power type, respectively.

    Article Title: A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
    Article Snippet: To test for robustness of the retrieval algorithm we introduced white Gaussian noise in all eight TOA radiance channels using the MATLAB function awgn(X, SNR), where X is the vector signal and SNR is the signal-to-noise-ratio.

    Article Title: Generating Synthetic Sperm Whale Voice Data Using StyleGAN2-ADA
    Article Snippet: Addition of random noise to the signal, with a signal-to-noise ratio (SNR) between 50 and 100, implemented using the MATLAB function awgn(S,snr) .

    Article Title: Tissue response curve-shape analysis of dynamic glucose-enhanced and dynamic contrast-enhanced magnetic resonance imaging in patients with brain tumor.
    Article Snippet: Gaussian noise (Matlab function “awgn”, SNR = 20 dB, signalpower = “measured”) was then added to all curve types to create different versions of the synthetic response curves within each curve type.

    Article Title: Two-frame wavefront reconstruction method with nonlinear optimization for Ronchi lateral shearing interferometry
    Article Snippet: Ronchi lateral shearing interferometry is widely applied in wavefront measurement of advanced lithography tools.. In Ronchi lateral shearing interferometry, higher-order diffraction beam interference is a common challenge, currently mitigated by introducing more phase shifts, resulting in extended measurement times and added errors.. In this study, we propose a nonlinear optimization approach that combines a 2-frame phase-shifting algorithm to suppress the influence of high diffraction orders, reducing the required number of phase shifts by more than 75%.



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