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1. Mesoscale shock structure in particulate composites

Description:

Multiscale experiments in heterogeneous materials and the knowledge of their physics under shock compression are limited. This study examines the multiscale shock response of particulate composites comprised of soda-lime glass particles in a PMMA matrix using full-field high speed digital image correlation (DIC) for the first time. Normal plate impact experiments, and complementary numerical simulations, are conducted at stresses ranging from to elucidate the mesoscale mechanisms responsible for the distinct shock structure observed in particulate composites. The particle velocity from the macroscopic measurement at continuum scale shows a relatively smooth velocity profile, with shock thickness decreasing with an increase in shock stress, and the composite exhibits strain rate scaling as the second power of the shock stress. In contrast, the mesoscopic response was highly heterogeneous, which led to a rough shock front and the formation of a train of weak shocks traveling at different velocities. Additionally, the normal shock was seen to diffuse the momentum in the transverse direction, affecting the shock rise and the rounding-off observed at the continuum scale measurements. The numerical simulations indicate that the reflections at the interfaces, wave scattering, and interference of these reflected waves are the primary mechanisms for the observed rough shock fronts.

Keyword:

Shock structure, Shock compression, Composite, Plate impact, Digital image correlation, and Meso-scale

Subject:

Engineering and Applied Science

Creator:

Lawlor, Barry , Ravindran, Suraj , Gandhi, Vatsa , and Ravichandran, Guruswami

Contributor:

Jio Institute

Owner:

n.sakthivel@jioinstitute.edu.in

Publisher:

Elsevier

Location:

United States

Language:

English

Date Uploaded:

21-03-2023

Date Modified:

21-03-2023

Date Created:

01-02-2023

Rights Statement Tesim:

In Copyright

License Tesim:

All rights reserved

Resource Type:

Article

Identifier:

10.1016/j.jmps.2023.105239
2. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

Description:

Skin Cancer is one of the most widespread forms of cancer in the world which can be detected using dermatoscopic images. In this paper, a texture based feature extraction algorithm is presented for the classification of dermatoscopic images. A median based Local Ternary Pattern is extracted followed by the computation of local quantized ternary patterns. The feature set extracted is then classified using a modified convolutional neural network. The images used for the detection of multiple types of skin cancer are obtained from two publicly available datasets, HAM10000 and ISICUDA11. For the proposed technique, the average recall value, average precision and average accuracy is found to be 75.20%, 95.44% and 96% respectively. An average increase in accuracy for the proposed algorithm is up-to 50.6%, 24.1% and 4.7% over LTP, DLTerQEP and a DE ANN based algorithm respectively.

Keyword:

Medical imaging, Image classification, Image retrieval, and Texture detection

Subject:

Medical Imaging, Data Science, Radiodiagnosis, Computer Science, Artificial Intelligence, and Radiology

Creator:

Sudipta Roy , Varun Srivastava, and Deepika Kumar

Contributor:

Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, India

Owner:

n.sakthivel@jioinstitute.edu.in

Publisher:

Elsevier

Location:

United States

Language:

English

Date Uploaded:

07-02-2023

Date Modified:

16-02-2023

Date Created:

01-09-2022

Rights Statement Tesim:

In Copyright

License Tesim:

All rights reserved

Resource Type:

Article

Identifier:

10.1016/j.compeleceng.2022.108259

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1. Mesoscale shock structure in particulate composites

2. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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1. Mesoscale shock structure in particulate composites

2. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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