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- Descrizione:
- Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image correlation (DIC). The experimental setup has a temporal resolution of 100 ns with a spatial resolution varying from 90 to 200 μm/pixel. Experiments were conducted under three different plate impact configurations to measure spatially resolved free surface velocity and validate the experimental technique. First, a normal impact experiment was conducted on polycarbonate to measure the macroscopic full-field normal free surface velocity. Second, an isentropic compression experiment on Y-cut quartz–tungsten carbide assembly is performed to measure the particle velocity for experiments involving ramp compression waves. To explore the capability of the technique in multiaxial loading conditions, a pressure shear plate impact experiment was conducted to measure both the normal and transverse free surface velocities under combined normal and shear loading. The velocities measured in the experiments using digital image correlation are validated against previous data obtained from laser interferometry. Numerical simulations were also performed using established material models to compare and validate the experimental velocity profiles for these different impact configurations. The novel ability of the employed experimental setup to measure full-field free surface velocities with high spatial resolutions in shock compression experiments is demonstrated for the first time in this work.
- Parola chiave:
- Stereo Digital image correlation, Shock Compression, Full-field measurements, and High Speed imaging
- Soggetto:
- Applied Science and Engineering
- Creatore:
- Ravindran, Suraj , Gandhi, Vatsa , Ravichandran, Guruswami , and Joshi, Akshay
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- American Institute of Physics
- luogo:
- United States
- Lingua:
- English
- Data caricata:
- 21-03-2023
- Data modificata:
- 21-03-2023
- data di creazione:
- 01-02-2023
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s40870-022-00359-2
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- Descrizione:
- 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.
- Parola chiave:
- Shock structure, Shock compression, Composite, Plate impact, Digital image correlation, and Meso-scale
- Soggetto:
- Engineering and Applied Science
- Creatore:
- Lawlor, Barry , Ravindran, Suraj , Gandhi, Vatsa , and Ravichandran, Guruswami
- Collaboratore:
- Jio Institute
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- Elsevier
- luogo:
- United States
- Lingua:
- English
- Data caricata:
- 21-03-2023
- Data modificata:
- 21-03-2023
- data di creazione:
- 01-02-2023
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1016/j.jmps.2023.105239
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- Descrizione:
- The evolution of information and knowledge has affected all organizations, including Libraries. Knowledge management is predominant in the fields of business management information systems, Management library, and information science. This study aims to identify and gather literature on the concepts of knowledge management (KM) related to libraries.
- Parola chiave:
- Library, Bibliometric Study, and Knowledge management
- Soggetto:
- Library and Information Science
- Creatore:
- Peter, Manuelraj, Pandiyarajan, Anand, Ali, Mohammed Barkath, and Idhris, Mohamed
- Collaboratore:
- Jio Institute Digital Library
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- University of Nebraska -Lincoln
- luogo:
- United States
- Lingua:
- English
- Data caricata:
- 11-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-10-2021
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
-
- Descrizione:
- Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5, displacement up to 6 pixels, and scale change up to 94%.
- Parola chiave:
- Binary pattern, Security, Legal identity for all, and Local Descriptors
- Soggetto:
- Artificial Intelligence and Data Science
- Creatore:
- Rehman, Amjad, Saba, Tanzila, Roy, Sudipta, Harouni, Majid, Karchegani, Negar Haghani Solati, and Bahaj, Saeed Ali
- Collaboratore:
- Artificial Intelligence and Data Analytics Research Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- Wiley
- luogo:
- United States
- Lingua:
- English
- Data caricata:
- 11-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-04-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1002/jemt.23989
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- Descrizione:
- 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.
- Parola chiave:
- Medical imaging, Image classification, Image retrieval, and Texture detection
- Soggetto:
- Medical Imaging, Data Science, Radiodiagnosis, Computer Science, Artificial Intelligence, and Radiology
- Creatore:
- Sudipta Roy , Varun Srivastava, and Deepika Kumar
- Collaboratore:
- Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, India
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Editore:
- Elsevier
- luogo:
- United States
- Lingua:
- English
- Data caricata:
- 07-02-2023
- Data modificata:
- 16-02-2023
- data di creazione:
- 01-09-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1016/j.compeleceng.2022.108259