Results 31 to 40 of about 2,973,613 (156)
Noise Spectral of GML Noise and GSR Behaviors for FGLE with Random Mass and Random Frequency
Fractal and Fractional, 2023 Due to the interest of anomalous diffusion phenomena and their application, our work has widely studied a fractional-order generalized Langevin Equation (FGLE) with a generalized Mittag–Leffler (GML) noise.
Lini Qiu +4 more
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Custom transistor layout design techniques for random telegraph signal noise reduction in CMOS image sensors [PDF]
, 2010 Interface and near oxide traps in small gate area MOS transistors (gate area ,1 mm2) lead to RTS noise which implies the emergence of noisy pixels in CMOS image sensors.
P. Magnan +5 more
core +1 more source
The random component of mixer-based nonlinear vector network analyzer measurement uncertainty [PDF]
, 2007 The uncertainty, due to random noise, of the measurements made with a mixer-based nonlinear vector network analyzer are analyzed. An approximate covariance matrix corresponding to the measurements is derived that can be used for fitting models and ...
Scott, Jonathan B. +3 more
core +1 more source
Известия высших учебных заведений России: Радиоэлектроника, 2023 Introduction. In the gyrocompassing mode, the initial heading angle of a platformless inertial navigation system (PINS) is determined based on the data obtained from accelerometers and gyroscopes that measure the projections of the gravitational ...
Trong Yen Nguyen +2 more
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Spatial Smoothing for Diffusion Tensor Imaging with low Signal to Noise Ratios [PDF]
, 2003 Though low signal to noise ratio (SNR) experiments in DTI give key information about tracking and anisotropy, e.g. by measurements with very small voxel sizes, due to the complicated impact of thermal noise such experiments are up to now seldom analysed.
Prigarin, Sergej M. +4 more
core +1 more source
IEEE AccessSuppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the
Jian Gao +4 more
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A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation
Remote Sensing, 2022 Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been
Haixia Zhao, Tingting Bai, Zhiqiang Wang
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CT Lilun yu yingyong yanjiu, 2023 This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data.
Yi ZHANG +4 more
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Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
Applied Sciences, 2023 The neural network denoising technique has achieved impressive results by being able to automatically learn the effective signal from the data without any assumptions.
Tianwei Lan +3 more
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Unsupervised Seismic Random Noise Suppression Based on Local Similarity and Replacement Strategy
IEEE Access, 2023 Improving the signal-to-noise ratio and suppressing random noise in seismic data is critical for high-precision processing. Although deep learning-based algorithms have gained popularity as denoising methods, they suffer from poor generalization ability,
Jian Gao +4 more
doaj +1 more source