Search

Your search keyword '"Naidu V"' showing total 784 results
Start Over Author "Naidu V"
784 results on '"Naidu V"'

1. Higher IL-9 Level Is Associated with Psoriasis Vulgaris Complicated by Metabolic Syndrome [Letter]

Author

Yalcinkaya A, Ansari N, and Naidu V

Subjects
Dermatology, RL1-803
Abstract

Ali Yalcinkaya,* Nabil Ansari,* Vishvan Naidu* Faculty of Life Sciences and Medicine, King’s College London, London, UK*These authors contributed equally to this workCorrespondence: Ali Yalcinkaya, Faculty of Life Sciences and Medicine, King’s College London, Great Maze Pond, London, SE1 1UL, United Kingdom, Tel +44 7477285220, Email ali.yalcinkaya@kcl.ac.uk

Published
2023

2. The Mediating Effect of Self-Efficacy on the Relationship Between Medication Literacy and Medication Adherence Among Patients with Type 2 Diabetes [Letter]

Author

Naidu V, Ganeshamoorthy A, and Allwin C

Subjects
n/a, Medicine (General), R5-920
Abstract

Vishvan Naidu,* Akalya Ganeshamoorthy,* Caitlin Allwin* Faculty of Life Sciences and Medicine, King’s College London, London, UK*These authors contributed equally to this workCorrespondence: Vishvan Naidu, Faculty of Life Sciences and Medicine, King’s College London, London, UK, Tel +447306343834, Email Vishvan.naidu@kcl.ac.uk

Published
2023

3. Effect of Academic Self-Efficacy on Test Anxiety of Higher Vocational College Students: The Chain Mediating Effect [Letter]

Author

Allwin C, Ganeshamoorthy A, and Naidu V

Subjects
letter to the editor, higher vocational students, academic self-efficacy, sense of life meaning, test anxiety, Psychology, BF1-990, Industrial psychology, HF5548.7-5548.85
Abstract

Caitlin Allwin,* Akalya Ganeshamoorthy,* Vishvan Naidu* Faculty of Life Sciences and Medicine, King’s College London, London, UK*These authors contributed equally to this workCorrespondence: Akalya Ganeshamoorthy, Great Maze Pond, London, SE1 1UL, UK, Tel +447494488559, Email Akalya.ganeshamoorthy@kcl.ac.uk

Published
2023

6. Finite Element Method for One-Dimensional Darcy–Brinkman–Forchheimer Fluid Flow Model

Author

Mallikarjunaiah, S. M., Kesavulu Naidu, V., Madhusudhan, R., Anand, N., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Menon, N. Vinod Chandra, editor, Kolathayar, Sreevalsa, editor, Rodrigues, Hugo, editor, and Sreekeshava, K. S., editor

Published
2024
Full Text
View/download PDF

11. Mechanical, Microstructural Characterization and Tribological Response of Al 5050/B4C/SiC Stir Cast Hybrid Metal Matrix Composite

Author

Sri Sukesh, P., Vasu, Y., Venkateswara Rao, M., Damodara Naidu, V., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Sivaram, N. M., editor, Sankaranarayanasamy, K., editor, and Davim, J. Paulo, editor

Published
2023
Full Text
View/download PDF

12. Study and Comparison of Various Metamaterial-Inspired Antennas

Author

Sellapillai, Sathyamoorthy, Rengasamy, Rajkumar, Praveen Naidu, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Subhashini, N., editor, Ezra, Morris. A. G., editor, and Liaw, Shien-Kuei, editor

Published
2023
Full Text
View/download PDF

18. A Hybrid Framework for Efficient Detection of Fake Currency Notes

Author

Santhi, M. V. B. T., Hrushikesava Raju, S., Adinarayna, S., Lokanadham Naidu, V., Waris, Saiyed Faiayaz, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Singh, R. K., editor, Tariq Beg, Mirza, editor, Mulaveesala, Ravibabu, editor, and Mahmood, Md Rashid, editor

Published
2022
Full Text
View/download PDF

19. PSO Optimum Design-PID Controller for Frequency Management of Single Area Multi-Source Power Generating System

Author

Kumarakrishnan, V., Vijayakumar, G., Jagatheesan, K., Boopathi, D., Anand, B., Kanendra Naidu, V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarma, Hiren Kumar Deva, editor, Balas, Valentina Emilia, editor, Bhuyan, Bhaskar, editor, and Dutta, Nitul, editor

Published
2022
Full Text
View/download PDF

23. Gearbox Health Condition Monitoring Using DWT Features

Author

Suresh, Setti, Naidu, V. P. S., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Rao, J. S., editor, Arun Kumar, V., editor, and Jana, Soumendu, editor

Published
2021
Full Text
View/download PDF

24. Gas Turbine Engine Fan Blade Flutter Detection Using Casing Vibration Signals by Application of Recurrence Plots and Recurrence Quantification Analysis

Author

Viswanatha Rao, A. N., Naidu, V. P. S., Jana, Soumendu, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Rao, J. S., editor, Arun Kumar, V., editor, and Jana, Soumendu, editor

Published
2021
Full Text
View/download PDF

25. Challenges in Engine Health Monitoring Instrumentation During Developmental Testing of Gas Turbine Engines

Author

Vishwanatha Rao, A. N., Satish, T. N., Nambiar, Anagha S., Jana, Soumemndu, Naidu, V. P. S., Uma, G., Umapathy, M., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Mistry, Chetan S., editor, Kumar, S. Kishore, editor, Raghunandan, B. N., editor, and Sivaramakrishna, Gullapalli, editor

Published
2021
Full Text
View/download PDF

28. Estimation of Equivalency Units of Vehicle Types for Road Geometry

Author

Sai Kumar, N., Naidu, V. M., Prasad, C. S. R. K., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Arkatkar, Shriniwas S., editor, Velmurugan, S., editor, and Verma, Ashish, editor

Published
2020
Full Text
View/download PDF

32. Metamaterialbased Wide Band MIMO Antenna with Enhanced Isolation for C‐ and X‐band Applications.

Author

Undrakonda, Jyothsna, Upadhyayula, Ratna K., Kumar, Ch. Manohar, and Naidu, V. M.

Subjects
ANTENNAS (Electronics), MIMO systems, RESONATORS, HIGH technology, METAMATERIALS, METAMATERIAL antennas
Abstract

A new decoupling element technology is used to create a tiny circular patch mutiple input multiple output (MIMO) antenna measuring 20 × 25 × 1.6 mm3. A simple split‐ring resonator (SRR) metamaterial design with adjustable inner ring inductance and capacitance allows MIMO antennas to operate across several frequency bands. Tunable‐split ring resonators (T‐SRRs) are small and improve antenna element isolation. The proposed MIMO system provides isolation larger than 20 dB from 5.93 to 12.63 GHz. It is useful for radar applications since it includes the C‐band upper link (5.925–6.425 GHz) and the X‐band (8–12 GHz). With good tuning and network structure decoupling, antennas can operate in dual bands with isolation levels below −15 dB. Over 85% radiation efficiency and 72% miniaturization were achieved in the proposed MIMO antenna. The antenna has good radiation and diversity performance, including an envelope correlation coefficient < 0.1 and a diversity gain of 9.98 dB. These qualities make it suitable for advanced technology. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

33. Machine Learning-based Bearing Fault Classification Using Higher Order Spectral Analysis.

Author

Sharma, Anju, Patra, G. K., and Naidu, V. P. S.

Subjects
MACHINE learning, CONVOLUTIONAL neural networks, IMAGE recognition (Computer vision), SUPPORT vector machines, K-nearest neighbor classification
Abstract

In the defense sector, where mission success often hinges on the reliability of complex mechanical systems, the health of bearings within aircraft, naval vessels, ground vehicles, missile systems, drones, and robotic platforms is paramount. Different signal processing techniques along with Higher Order Spectral Analysis (HOSA) have been used in literature for the fault diagnosis of bearings. Bispectral analysis offers a valuable means of finding higher-order statistical associations within signals, thus proving to detect the nonlinearities among Gaussian and non-Gaussian data. Their resilience to noise and capacity to unveil concealed information render them advantageous across a range of applications. Therefore, this research proposesa novel approach of utilizing the features extracted directly from the Bispectrum for classifying the bearing faults, departing from the common practice in other literature where the Bispectrum is treated as an image for fault classification. In this work vibration signalsare used to detect the bearing faults. The features from the non-redundant region and diagonal slice of the Bispectrum are used to capture the statistical and higher-order spectral characteristics of the vibration signal. A set of sixteen machine learning models, viz., Decision Trees, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine, is employed to classify the bearing faults. The evaluation process involves a robust 10-fold cross-validation technique. The results reveal that the Decision Tree algorithm outperformed all others, achieving a remarkable accuracy rate of 100 %. The naive Bayes algorithm also demonstrated the least performance, with an accuracy score of 99.68 %. The results obtained from these algorithms have been compared with those achieved using Convolutional Neural Network (CNN), revealing that the training time of these algorithms is significantly shorter in comparison to CNN. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

39. A Simple and Efficient Higher Order Finite Element Scheme for Helmholtz Waveguides

Author

Panda, Tattwa Darshi, Nagaraja, K. V., Kesavulu Naidu, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kalam, Akhtar, editor, Das, Swagatam, editor, and Sharma, Kalpana, editor

Published
2018
Full Text
View/download PDF

50. Machine learning augmented multi-sensor data fusion to detect aero engine fan rotor blade flutter.

Author

Rao, A. N. Viswanatha, Satish, T. N., Naidu, V. P. S., and Jana, Soumendu

Abstract

Flutter-induced fatigue failure investigation of the fan blades of aero-engines necessitates extensive testing. During engine ground testing, strain gauges on rotor fan blades and casing vibration sensors were employed to investigate structural dynamic aspects. The correlation between strain sensor signals and fan casing vibration signals allowed the diagnosis of fluttering fan blades. For automated flutter detection during engine development testing, a machine learning-augmented information fusion methodology was developed. The method analyses casing vibration signals by extracting time-domain statistical features, intrinsic mode function characteristics through empirical mode decomposition, and recurrence quantification features. Feature vectors obtained from a relatively large set of engine tests were subjected to dimension reduction by applying machine learning techniques to rank them. Reduced feature vector space was labelled as "flutter" or "normal" based on the correlation of rotor strain gauge signals. In addition, the labelled feature vectors were employed to train classifier models using supervised learning-based algorithms such as Support Vector Machines, Linear Discriminant Analysis, K-means Clustering, and Artificial Neural Networks. Using only vibration signals from the casing, the trained and validated classifiers were able to detect flutter in fan baldes with a 99% probability during subsequent testing. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results