Ultrasonic Transmission Imaging Using Continuous Gaussian Noise Source

1975 ◽  
pp. 551-558
Author(s):  
F. J. Fry ◽  
C. P. Jethwa
Keyword(s):  
Gaussian Noise ◽  
Noise Source ◽  
Transmission Imaging

scholarly journals The foraging brain: evidence of Lévy dynamics in brain networks

2016 ◽  
Author(s):  
Tommaso Costa ◽  
Giuseppe Boccignone ◽  
Franco Cauda ◽  
Mario Ferraro
Keyword(s):  
Gaussian Noise ◽  
Resting State ◽  
Noise Source ◽  
State Condition ◽  
Human Cognition ◽  
Lévy Noise ◽  
Characteristic Index ◽  
Lévy Motion ◽  
Levy Noise ◽  
Stable Noise

AbstractIn this research we have analyzed functional magnetic resonance imaging (fMRI) signals of different networks in the brain under resting state condition.To such end, the dynamics of signal variation, have been conceived as a stochastic motion, namely it has been modelled through a generalized Langevin stochastic differential equation, which combines a deterministic drift component with a stochastic component where the Gaussian noise source has been replaced with α-stable noise.The parameters of the deterministic and stochastic parts of the model have been fitted from fluctuating data. Results show that the deterministic part is characterized by a simple, linear decreasing trend, and, most important, the α-stable noise, at varying characteristic index α, is the source of a spectrum of activity modes across the networks, from those originated by classic Gaussian noise (α = 2), to longer tailed behaviors generated by the more general Lévy noise (1 ≤ α < 2).Lévy motion is a specific instance of scale-free behavior, it is a source of anomalous diffusion and it has been related to many aspects of human cognition, such as information foraging through memory retrieval or visual exploration.Finally, some conclusions have been drawn on the functional significance of the dynamics corresponding to different α values.Author SummaryIt has been argued, in the literature, that to gain intuition of brain fluctuations one can conceive brain activity as the motion of a random walker or, in the continuous limit, of a diffusing macroscopic particle.In this work we have substantiated such metaphor by modelling the dynamics of the fMRI signal of different brain regions, gathered under resting state condition, via a Langevin-like stochastic equation of motion where we have replaced the white Gaussian noise source with the more general α-stable noise.This way we have been able to show the existence of a spectrum of modes of activity in brain areas. Such modes can be related to the kind of “noise” driving the Langevin equation in a specific region. Further, such modes can be parsimoniously distinguished through the stable characteristic index α, from Gaussian noise (α = 2) to a range of sharply peaked, long tailed behaviors generated by Lévy noise (1 ≤ α < 2).Interestingly enough, random walkers undergoing Lévy motion have been widely used to model the foraging behaviour of a range of animal species and, remarkably, Lévy motion patterns have been related to many aspects of human cognition.

Mixed Noise Image De-Noising Based on EM Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4734-4741 ◽  
Author(s):  
Gui Cun Shi ◽  
Fei Xing Wang
Keyword(s):  
Applied Sciences ◽  
Wavelet Analysis ◽  
Em Algorithm ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Noise Source ◽  
High Quality ◽  
Noise Image ◽  
Mixed Noise ◽  
Non Gaussian

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

scholarly journals DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning

2021 ◽  
Vol 35 (11) ◽  
pp. 1439-1440
Author(s):  
Peter Gerstoft ◽  
Christoph Mecklenbrauker ◽  
Santosh Nannuru ◽  
Geert Leus
Keyword(s):  
Maximum Likelihood ◽  
Gaussian Noise ◽  
Bayesian Learning ◽  
Noise Source ◽  
Doa Estimation ◽  
Noise Model ◽  
Sparse Bayesian Learning ◽  
Noisy Environment ◽  
Independent Zero

We consider direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e., source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs are estimated from multi-snapshot array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots.

An automatic characterization of a Gaussian noise source for undergraduate electronics laboratory

1995 ◽  
Vol 38 (2) ◽  
pp. 126-130 ◽  
Author(s):  
R. Quere ◽  
M. Lalande ◽  
J.N. Boutin ◽  
C. Valente
Keyword(s):  
Gaussian Noise ◽  
Noise Source

Low Frequency White Gaussian Noise Source

1971 ◽  
Vol 17 (3) ◽  
pp. 98-102
Author(s):  
S. K. Mullick ◽  
Ranjit Singh ◽  
A. K. Sanghi
Keyword(s):  
Gaussian Noise ◽  
Noise Source ◽  
Low Frequency ◽  
White Gaussian Noise

Identification of Asbestos Fibres in a Water Sample by X-Ray Microanalysis

1975 ◽  
Vol 33 ◽  
pp. 274-275
Author(s):  
K. A. Brookes ◽  
D. Finbow ◽  
Madeleine Samuel
Keyword(s):  
Water Sample ◽  
Background Radiation ◽  
X Ray ◽  
Left Hand ◽  
Transmission Imaging ◽  
Different Types ◽  
Normal Specimen ◽  
Liquid Nitrogen Cooling ◽  
Normal Transmission ◽  
Additional Port

Investigation of the particulate matter contained in the water sample, revealed the presence of a number of different types and certain of these were selected for analysis.An A.E.I. Corinth electron microscope was modified to accept a Kevex Si (Li) detector. To allow for existing instruments to be readily modified, this was kept to a minimum. An additional port is machined in the specimen region to accept the detector, with the liquid nitrogen cooling dewar conveniently housed in the left hand cupboard adjacent to the microscope column. Since background radiation leads to loss in the sensitivity of the instrument, great care has been taken to reduce this effect by screening and manufacturing components that are near the specimen from material of low atomic number. To change from normal transmission imaging to X-ray analysis, the special 4-position specimen rod is inserted through the normal specimen airlock.

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