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- Description:
- Architected cellular materials, such as lattice structures, offer potential for tunable mechanical properties for dynamic applications of energy absorption and impact mitigation. In this work, the static and dynamic behavior of polymeric lattice structures was investigated through experiments on octet-truss, Kelvin, and cubic topologies with relative densities around 8%. Dynamic testing was conducted via direct impact experiments (25–70 m/s) with high-speed imaging coupled with digital image correlation and a polycarbonate Hopkinson pressure bar. Mechanical properties such as elastic wave speed, deformation modes, failure properties, particle velocities, and stress histories were extracted from experimental results. At low impact velocities, a transient dynamic response was observed which was composed of a compaction front initiating at the impact surface and additional deformation bands whose characteristics matched low strain-rate behavior. For higher impact velocities, shock analysis was carried out using compaction wave velocity and Eulerian Rankine–Hugoniot jump conditions with parameters determined from full-field measurements.
- Keyword:
- Failure, Digital image correlation, Transient dynamic, Shock, Lattice structure, and Compaction
- Subject:
- Applied Science and Engineering
- Creator:
- Weeks, J. S. and Ravichandran, Guruswami
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 21-03-2023
- Date Modified:
- 21-03-2023
- Date Created:
- 01-12-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s40870-022-00359-2
-
- 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
-
- Description:
- Early diagnosis of brain tumors is crucial for treatment planning and increasing the survival rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and features, as well as treatment choices. One of the essential roles of neurologists and radiologists is the diagnosis of brain tumors in their early stages. However, manual brain tumor diagnosis is difficult, time-consuming, and prone to error. Based on the problem highlighted, an automated brain tumor detection system is mandatory to identify the tumor in its initial stages. This research presents an efficient deep learningbased system for the classification of brain tumors from brain MRI using the deep convolutional network and salp swarm algorithm. All experiments are performed using the publicly available brain tumor Kaggle dataset. To enhance the classification rate, preprocessing and data augmentation such as skewed data ideas are devised. In addition, AlexNet and VGG19 are leveraged to perform specific functionality. Finally, all features merged into a single feature vector for brain tumor classification. Some of the extracted features found insignificant towards effective classification. Hence, we employed an efficient feature selection technique named slap swarm to find the most discriminative features to attain best tumor classification rate. Finally, several SVM kernels are merged for the final classification and 99.1% accuracy is achieved by selecting 4111 optimal features from 8192.
- Keyword:
- Brain tumor, Public health, Health risks, Deep learning, Transfer learning, and MRI
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Almutairi, Fahad , Roy, Sudipta , Alkhurim, Alhassan , Saba, Tanzila , Fayyaz, Abdul Muiz , Rehman, Amjad , and Alyami, Jaber
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-01-2023
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s12559-022-10096-2
-
- Description:
- Identification and recognition of number plate is very difficult from low resolution images due to poor boundary and contrast. Our goal is to identify the digits from a low-quality number plate image correctly, but correct detection was exceedingly difficult in some cases due to the low-resolution image. Another goal of this paper was to upscale the image from a very low resolution to high resolution to recover helpful information to improve the accuracy of number plate detection and recognition. We have used Enhanced- Super-Resolution with Generative Adversarial Network (SRGAN). We modified native Dense Blocks of the Generative Adversarial Network with a Residual in Residual Dense Block model. In addition to Convolutional Neural Networks for thresholding. We also used a Rectified Linear Unit (ReLU) activation layer. The plate image is then used for segmentation using the OCR model for detection and recognizing the characters in the number plates. The Optical character recognition (OCR) model reaches an average accuracy of 84% for high resolution, whereas the accuracy is 4% - 7% for low resolution. The model’s accuracy increases with the resolution enhancement of the plate images. ESRGAN provides better enhancement of low-resolution images than SRGAN and Pro-SRGAN, which the OCR model validates. The accuracy significantly increased digit/alphabet detection in the number plate than the original low-resolution image when converted to a high-resolution image using ESRGAN.
- Keyword:
- Structural similarity of images, Number plate detection, Residual dense block, Super-resolution, Deep learning, and Optical character recognition
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Roy, Sudipta, Ganguly, Debayan , Pal, Debojyoti , Chatterjee, Kingshuk , and Kabiraj, Anwesh
- Contributor:
- Jio Institute, CVMIComputer Vision in Medical Imaging Project
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-09-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s11042-022-14018-0
-
- Description:
- Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
- Keyword:
- Layered Networking, Parallel Architectures, Device Level Clustering, Internet of Things, Topology Binding, Performance Optimization, and Radio-frequency Identification
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Singh, Ninni , Roy, Sudipta , Saad, Ismail , Rahebi, Javad , Farzamnia, Ali , Gunjan, Vinit Kumar , Davanam, Ganesh , and Kallam, Suresh
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- MDPI
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.3390/en15228738
-
- Description:
- Fetal brain segmentation and gestational age prediction have been under active research in the field of medical image processing for a long time. However, both these tasks are challenging due to factors like difficulty in acquiring a proper fetal brain image owing to the fetal movement during the scan. With the recent advancements in deep learning, many models have been proposed for performing both the tasks, individually, with good accuracy. In this paper, we present Multi-Tasking Single Encoder U-Net, MTSE U-Net, a deep learning architecture for performing three tasks on fetal brain images. The first task is the segmentation of the fetal brain into its seven components: intracranial space and extra-axial cerebrospinal fluid spaces, gray matter, white matter, ventricles, cerebellum, deep gray matter, and brainstem, and spinal cord. The second task is the prediction of the type of the fetal brain (pathological or neurotypical). The third task is the prediction of the gestational age of the fetus from its brain. All of this will be performed by a single model. The fetal brain images can be obtained by segmenting it from the fetal magnetic resonance images using any of the previous works on fetal brain segmentation, thus showing our work as an extension of the already existing segmentation works. The Jaccard similarity and Dice score for the segmentation task by this model are 77 and 82%, respectively, accuracy for the type of prediction task is 89% and the mean absolute error for the gestational age task is 0.83 weeks. The salient region identification by the model is also tested and these results show that a single model can perform multiple, but related, tasks simultaneously with good accuracy, thus eliminating the need to use separate models for each task.
- Keyword:
- Medical image processing, Fetal brain segmentation, Deep learning, Fetal gestational age prediction, and Convolutional neural networks
- Subject:
- Data Science and Artificial Intelligence
- Creator:
- Ganguly, Debayan , Chatterjee, Kingshuk , Gangopadhyay, Tuhinangshu , Sarkar, Surjadeep , Halder, Shinjini , Dasgupta, Paramik , and Roy, Sudipta
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s13721-022-00394-y
-
- Description:
- 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.
- Keyword:
- Library, Bibliometric Study, and Knowledge management
- Subject:
- Library and Information Science
- Creator:
- Peter, Manuelraj, Pandiyarajan, Anand, Ali, Mohammed Barkath, and Idhris, Mohamed
- Contributor:
- Jio Institute Digital Library
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- University of Nebraska -Lincoln
- Location:
- United States
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-10-2021
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
-
- Description:
- 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%.
- Keyword:
- Binary pattern, Security, Legal identity for all, and Local Descriptors
- Subject:
- Artificial Intelligence and Data Science
- Creator:
- Rehman, Amjad, Saba, Tanzila, Roy, Sudipta, Harouni, Majid, Karchegani, Negar Haghani Solati, and Bahaj, Saeed Ali
- Contributor:
- Artificial Intelligence and Data Analytics Research Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Wiley
- Location:
- United States
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-04-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1002/jemt.23989
-
- Description:
- Architected cellular materials, such as lattice structures, offer potential for tunable mechanical properties for dynamic applications of energy absorption and impact mitigation. In this work, the static and dynamic behavior of polymeric lattice structures was investigated through experiments on octet-truss, Kelvin, and cubic topologies with relative densities around 8%. Dynamic testing was conducted via direct impact experiments (25–70 m/s) with high-speed imaging coupled with digital image correlation and a polycarbonate Hopkinson pressure bar. Mechanical properties such as elastic wave speed, deformation modes, failure properties, particle velocities, and stress histories were extracted from experimental results. At low impact velocities, a transient dynamic response was observed which was composed of a compaction front initiating at the impact surface and additional deformation bands whose characteristics matched low strain-rate behavior. For higher impact velocities, shock analysis was carried out using compaction wave velocity and Eulerian Rankine–Hugoniot jump conditions with parameters determined from full-field measurements.
- Keyword:
- Transient dynamic, Failure, Shock, Compaction, Lattice structure, and Digital image correlation
- Subject:
- Material Science, Chemistry, and Chemical Engineering
- Creator:
- Weeks, J. S. and Ravichandran, G.
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Switzerland
- Language:
- English
- Date Uploaded:
- 11-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-12-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s40870-022-00359-2
-
- Description:
- Purpose For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.
- Keyword:
- Optimization, Lung Cancer, Recurrent Neural Network, and CT images
- Subject:
- Data Science and Artificial Intelligence
- Creator:
- Roy, Sudipta, Gunjan, Vinit Kumar , Shaik, Fahimudin, and Singh, Ninni
- Owner:
- n.sakthivel@jioinstitute.edu.in
- Publisher:
- Springer Nature
- Location:
- Cham and Switzerland
- Language:
- English
- Date Uploaded:
- 10-02-2023
- Date Modified:
- 16-02-2023
- Date Created:
- 01-11-2022
- Rights Statement Tesim:
- In Copyright
- License Tesim:
- All rights reserved
- Resource Type:
- Article
- Identifier:
- 10.1007/s12553-022-00700-8