Thermo-hydrolytic resistance of polyepoxide–glass fibres interfaces by the microbond test

2008 ◽  
Vol 68 (9) ◽  
pp. 2028-2033 ◽  
Author(s):  
P ZINCK ◽  
J GERARD
Keyword(s):  
Glass Fibres ◽  
Microbond Test ◽  
Hydrolytic Resistance

Optical properties of sodium-borosilicate glass fibres

1974 ◽  
Vol 10 (22) ◽  
pp. 450 ◽  
Author(s):  
C.R. Day ◽  
J.E. Midwinter ◽  
G.R. Newns ◽  
R.W.J. Uffen ◽  
R. Worthington
Keyword(s):  
Optical Properties ◽  
Borosilicate Glass ◽  
Sodium Borosilicate Glass ◽  
Glass Fibres

High-power CO laser transmission through As-S glass fibres

1984 ◽  
Vol 20 (20) ◽  
pp. 811 ◽  
Author(s):  
T. Hattori ◽  
S. Sato ◽  
T. Fujioka ◽  
S. Takahashi ◽  
T. Kanamori
Keyword(s):  
High Power ◽  
Co Laser ◽  
Glass Fibres

Observation of energy-distribution-dependent homogeneous upconversion in erbium-doped silica glass fibres

1999 ◽  
Vol 35 (14) ◽  
pp. 1189 ◽  
Author(s):  
D. Bremberg ◽  
S. Helmfrid ◽  
B. Jaskorzynska ◽  
M. Swillo ◽  
J.L. Philipsen ◽  
...  
Keyword(s):  
Energy Distribution ◽  
Silica Glass ◽  
Glass Fibres ◽  
Erbium Doped

scholarly journals Phosphate glass fibres with therapeutic ions release capability – a review

2019 ◽  
Vol 119 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Agata Lapa ◽  
Mark Cresswell ◽  
Phil Jackson ◽  
Aldo R. Boccaccini
Keyword(s):  
Phosphate Glass ◽  
Glass Fibres

Quasi-static and dynamic crush behaviour of 3D printed thin-walled profiles reinforced with continuous carbon and glass fibres

2021 ◽  
Vol 217 ◽  
pp. 108865
Author(s):  
U. Morales ◽  
A. Esnaola ◽  
M. Iragi ◽  
L. Aretxabaleta ◽  
J. Aurrekoetxea
Keyword(s):  
Glass Fibres ◽  
3D Printed ◽  
Thin Walled

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

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