scholarly journals Increased gamma and decreased fast ripple connections of epileptic tissue: A high‐frequency directed network approach

Epilepsia ◽  
2019 ◽  
Vol 60 (9) ◽  
pp. 1908-1920 ◽  
Author(s):  
Willemiek J. E. M. Zweiphenning ◽  
Hanneke M. Keijzer ◽  
Eric Diessen ◽  
Maryse A. van ‘t Klooster ◽  
Nicole E. C. Klink ◽  
...  
Keyword(s):  
High Frequency ◽  
Directed Network ◽  
Network Approach

scholarly journals Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

2020 ◽  
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Frequency Representation ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

scholarly journals Unveiling socioeconomic differences in colombia by means of urban mobility complex networks

Memorias ◽  
2018 ◽  
pp. 80-86
Author(s):  
Laura Lotero ◽  
Rafael Hurtado Heredia ◽  
Patricia Jaramillo Álvarez
Keyword(s):  
Complex Networks ◽  
Social Stratification ◽  
Directed Network ◽  
Socioeconomic Differences ◽  
Urban Mobility ◽  
Topological Properties ◽  
Network Approach ◽  
Structural Differences ◽  
Socioeconomic Strata ◽  
Do So

Social stratification lead to marked differences between people in several aspects of their lives, such as income, education, work, welfare and mobility. Here, we aim to analyze urban mobility by socioeconomic differences of travelers. In order to do so, we represent urban mobility by a complex network approach. We show that the topological properties of the networks allow to characterize mobility flows and to recognize differences in the dynamics of socioeconomic strata. We use data from origin destination surveys made for the two most populated cities in Colombia and we represent it in the form of a weighted and directed network. We found that urban mobility networks have structural differences if analyzed by socioeconomic strata of the population and unveil segregation patterns in the highest and lowest income strata.

scholarly journals Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach

2020 ◽  
Vol 10 (17) ◽  
pp. 5764
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

The Microstructure of Steatite (Protoenstatite)

1985 ◽  
Vol 43 ◽  
pp. 236-237
Author(s):  
W. E. Lee ◽  
A. H. Heuer
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Magnesium Silicate ◽  
Beam Current ◽  
Glassy Matrix ◽  
Angle Of Incidence ◽  
X Ray ◽  
Mechanical And Electrical Properties ◽  
Argon Ions ◽  
High Temperature Polymorph

IntroductionTraditional steatite ceramics, made by firing (vitrifying) hydrous magnesium silicate, have long been used as insulators for high frequency applications due to their excellent mechanical and electrical properties. Early x-ray and optical analysis of steatites showed that they were composed largely of protoenstatite (MgSiO3) in a glassy matrix. Recent studies of enstatite-containing glass ceramics have revived interest in the polymorphism of enstatite. Three polymorphs exist, two with orthorhombic and one with monoclinic symmetry (ortho, proto and clino enstatite, respectively). Steatite ceramics are of particular interest a they contain the normally unstable high-temperature polymorph, protoenstatite.Experimental3mm diameter discs cut from steatite rods (∼10” long and 0.5” dia.) were ground, polished, dimpled, and ion-thinned to electron transparency using 6KV Argon ions at a beam current of 1 x 10-3 A and a 12° angle of incidence. The discs were coated with carbon prior to TEM examination to minimize charging effects.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

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