strong diffusion
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2022 ◽  
Author(s):  
Mizuki Fukizawa ◽  
Takeshi Sakanoi ◽  
Yoshizumi Miyoshi ◽  
Yoichi Kazama ◽  
Yuto Katoh ◽  
...  

2021 ◽  
Vol 03 (04) ◽  
pp. 35-46
Author(s):  
Alyaa M. Abdul MAJEED

This paper suggests a novel algorithm for encrypting speech signals in common image formats and retrieve them from these image files. The speech signal is encrypted in three levels. In the first level,the sample positions are permuted based on keys generated using Game of Life matrix and Piecewise linear Chaotic Map (PWLCM) in order to reduce the correlation between adjust samples. In the second level, the resulting samples are then converted to Fast Walsh Hadamard Transform (FWHT) and their transactions are encrypted by using circular transformations in the row and column depending on the generated key. At the third level, the values of encrypted samples are converted to color pixels, which are then arranged in a puzzled manner and put in a 2-D matrix to achieve the secured data transfer across networks, with the image file contains the encrypted speech signal. Several objective measures have been used to evaluate the performance of the suggested method. The experimental results and numerical analyses show that the algorithm gives a high degree of security and robust against brute force attackers, statistical attack, strong diffusion and ambiguity so that the encrypted message has been saved in a different format from the original signal, and finally give the good quality of the reconstructed speech signal from image files.


2021 ◽  
Author(s):  
Marco Pizzolato ◽  
Mariam Andersson ◽  
Erick Jorge Canales-Rodriguez ◽  
Jean-Philippe Thiran ◽  
Tim B Dyrby

In magnetic resonance imaging, the application of a strong diffusion weighting suppresses the signal contributions from the less diffusion-restricted constituents of the brain's white matter, thus enabling the estimation of the transverse relaxation time T2 that arises from the more diffusion-restricted constituents such as the axons. However, the presence of cell nuclei and vacuoles can confound the estimation of the axonal T2, as diffusion within those structures is also restricted, causing the corresponding signal to survive the strong diffusion weighting. We devise an estimator of the axonal T2 based on the directional spherical variance of the strongly diffusion-weighted signal. The spherical variance T2 estimates are insensitive to the presence of isotropic contributions to the signal like those provided by cell nuclei and vacuoles. We show that with a strong diffusion weighting these estimates differ from those obtained using the directional spherical mean of the signal which contains both axonal and isotropically-restricted contributions. Our findings hint at the presence of an MRI-visible isotropically-restricted contribution to the signal in the white matter ex vivo fixed tissue (monkey) at 7T, and do not allow us to discard such a possibility also for in vivo human data collected with a clinical 3T system.


2021 ◽  
Author(s):  
Gabriel Ramos-Llordén ◽  
Rodrigo Lobos ◽  
Tae Hyung Kim ◽  
Qiyuan Tian ◽  
Thomas Witzel ◽  
...  

Diffusion MRI (dMRI) of whole, intact, fixed postmortem human brain at high spatial resolution serves as key bridging technology for 3D mapping of structural connectivity and tissue microstructure at the mesoscopic scale. Ex vivo dMRI offers superior spatial resolution compared to in vivo dMRI but comes with its own technical challenges due to the significantly reduced T2 relaxation times and decreased diffusivity incurred by tissue fixation. The altered physical properties of fixed tissue necessitate the use of alternative acquisition strategies to preserve SNR and achieve sufficient diffusion weighting. Multi-shotor segmented 3D echo planar imaging (EPI) sequences have been used to shorten echo times (TEs) with reduced distortions from field inhomogeneity and eddy currents on small-bore MR scanners and have been adopted for high b-value dMRI of ex vivo whole human brain specimens. The advent of stronger gradients on human MRI scanners has led to improved image quality and a wider range of diffusion-encoding parameters for dMRI but at the cost of more severe eddy currents that result in spatial and temporal variations in the background magnetic field, which cannot be corrected for using standard vendor-provided ghost correction solutions. In this work, we show that conventional ghost correction techniques based on navigators and linear phase correction may be insufficient for EPI sequences using strong diffusion-sensitizing gradients in ex vivo dMRI experiments, resulting in orientationally biased dMRI estimates. This previously unreported problem is a critical roadblock in any effort to leverage scanners with ultra-high gradients for high-precision mapping of human neuroanatomy at the mesoscopic scale. We propose an advanced reconstruction method based on structured low-rank matrix modeling that reduces the ghosting substantially. We show that this method leads to more accurate and reliable dMRI metrics, as exemplified by diffusion tensor imaging and high angular diffusion imaging analyses in distributed neuroanatomical areas of fixed whole human brain specimens. Our findings advocate for the use of advanced reconstruction techniques for recovering unbiased metrics from ex vivo dMRI acquisitions and represent a crucial step toward making full use of strong diffusion-encoding gradients for neuroscientific studies seeking to study brain structure at multiple spatial scales.


2021 ◽  
pp. 1-15
Author(s):  
BASSAM FAYAD ◽  
MARIA SAPRYKINA

Abstract We present examples of nearly integrable analytic Hamiltonian systems with several strong diffusion properties: topological weak mixing and diffusion at all times. These examples are obtained by AbC constructions with several frequencies.


2021 ◽  
Vol 13 (2) ◽  
pp. 601
Author(s):  
Dimitris Georgantzis Garcia ◽  
Eva Kipnis ◽  
Efi Vasileiou ◽  
Adrian Solomon

The Circular Economy (CE) is gaining increasing attention among businesses, policymakers and academia, and across research disciplines. While the concept’s strong diffusion may be considered its main strength, it has also contributed to the emergence of many different understandings and definitions, which may hinder or slow down its success. Specifically, despite growing attention, the role of the consumption side in the CE remains a largely under-researched topic. In the present review, we first search the literature by means of snowball mapping and a systematic key-word strategy, and then critically analyze the identified sources in order to elucidate the fundamental elements that should characterize consumption in a CE. We extract two pillars, directly from definition, that should be at the nucleus of future research on consumption in the CE: (1) the hierarchical nature of circular strategies, with “reduce” being preferred to all other strategies; and (2) the inadequacy of defining the CE only through its loops or strategies without considering its goal of attaining sustainable development. Moreover, the discussion is placed within the extant consumer research streams deemed relevant, in order to bridge these with the context of the CE. We highlight limitations of said research streams regarding their typical focus on the quality (and not the quantity) of consumption, the lack of heterogeneity in the theories and data collection methods employed, and the non-impact-based instruments typically used to measure consumption behaviors. We show how these limitations have contributed to the emergence of the intention–behavior gap, a phenomenon extant studies identify as key to overcome for encouraging sustainable consumption practices. In particular, we focus the analysis on the intention–behavior gap in order to: (1) establish the state-of-the-art; and (2) uncover avenues for future research addressing extant limitations.


2021 ◽  
Author(s):  
Nicoló Dell’Unto ◽  

In the last decade, 3D visualisation has seen a strong diffusion in the cultural heritage sector. The development of more efficient computers, the distribution of friendly user interfaces, and the spread of new sensors for recording and visualising information were pivotal for exploring 3D visualisation technology to support advanced interaction and promote new investigation methods. Since the early 1990s, 3D visualisation was conceived as a dynamic tool for increasing the perception of the archaeological material (Reilly 1991), and 3D models were considered an effective solution for addressing complex questions and revising different hypotheses


2020 ◽  
pp. 2150035
Author(s):  
Marifi Güler

A stochastic differential formulation for the collective dynamics of ion channel clusters in excitable membranes is developed from the so-called “reduced strong diffusion formulation”. In this error bound optimizing reduced formulation, the potassium channel states [Formula: see text] and [Formula: see text], and, the sodium channel states [Formula: see text] and [Formula: see text] are the retained states; consequently, the formulation accommodates only four channel variables and five white noises. The accuracy of the formulation is tested over the standard deviations and autocorrelation times of the channel density fluctuations. The findings are seen to be virtually identical to the corresponding results from the exact microscopic Markov simulations. The formulation arises as the most accurate model with that structural simplicity, thus making it an important model for both analytic analyses and numerical simulations in the study of finite-sized membranes.


Author(s):  
Matteo Boldrini

Among the attributes of political candidates, localness represents an aspect relatively uncovered by the core of the literature on party politics. There are, however, different scholars that pinpointed to the utility of this aspect in understanding political elites’ circulation. In line with this assumption, the article aims to map the degree of localness of single-member constituencies’ candidates in the 2018 Italian general elections. More specifically, the analysis focuses on the candidates’ localness by highlight similarities and differences among party-candidates and their geographical distribution. In doing so, the analysis is based on an original dataset; the localness of candidates has been calculated through a localness index. The article is organized as follows. It starts from a literature review on localness. Drawing on this theoretical introduction, I identify the logic behind the research question. In the next section, the rationale for the selection of the different cases analysed is provided. On this ground, the analysis proceeds with a synthetic overview of the context of reference. In the last part of the analysis, the index of localness is defined and applied to the dataset. The main conclusion is that, although there is a strong diffusion of local candidates, there are significant differences among parties, with a higher localness level of the League and the Democratic Party than Forza Italia and the Five Star Movement, especially in the Northern and Southern’s regions.


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