author:"Naidu, V.P.S." | Pollux - Fachinformationsdienst Politikwissenschaft

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Aufsatz(elektronisch)#12. September 2011

Image Fusion Technique using Multi-resolution Singular Value Decomposition

In: Defence science journal: DSJ, Band 61, Heft 5, S. 479

Naidu, V.P.S.

ISSN: 0011-748X

A novel image fusion technique based on multi-resolution singular value decomposition (MSVD) has been presented and evaluated. The performance of this algorithm is compared with that of well known image fusion technique using wavelets. It is observed that image fusion by MSVD perform almost similar to that of wavelets. It is computationally very simple and it could be well suited for real time applications. Moreover, MSVD does not have a fixed set of basis vectors like FFT, DCT and wavelet etc. and its basis vectors depend on the data set.Defence Science Journal, 2011, 61(5), pp.479-484, DOI:http://dx.doi.org/10.14429/dsj.61.705

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Aufsatz(gedruckt)#22009

Fusion of Radar and IRST Sensor Measurements for 3-D Target Tracking using Extended Kalman Filter

In: Defence science journal: a journal devotet to science & technology in defence, Band 59, Heft 2, S. 175-182

Naidu, V.P.S.

ISSN: 0011-748X

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Aufsatz(gedruckt)#32008

Pixel-level Image Fusion using Wavelets and Principal Component Analysis

In: Defence science journal: a journal devotet to science & technology in defence, Band 58, Heft 3, S. 338-352

Naidu, V.P.S.; Raol, J.R.

ISSN: 0011-748X

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Aufsatz(elektronisch)#423. Mai 2007

Target Tracking with Multi Acoustic Array Sensors Data

In: Defence science journal: DSJ, Band 57, Heft 3, S. 289-303

Naidu, V.P.S.; Raol, J.R.

ISSN: 0011-748X

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Aufsatz(gedruckt)#52007

Target Tracking with Multi Acoustic Array Sensors Data

In: Defence science journal: a journal devotet to science & technology in defence, Band 57, Heft 3, S. 289-304

Naidu, V.P.S.; Raol, J.R.

ISSN: 0011-748X

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Aufsatz(elektronisch)#624. April 2024

Machine learning Based Bearing Fault Classification Using Higher Order Spectral Analysis

In: Defence science journal: DSJ, Band 74, Heft 4, S. 505-516

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

ISSN: 0011-748X

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.

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Aufsatz(gedruckt)#72007