Fuzzy C-Means Clustering Based Anomalies Detection in Quadratic Frequency Modulated Thermal Wave Imaging
A. Vijaya Lakshmi1, V. S. Ghali2, Muzammil Parvez M3, G. V. P. Chandra Sekhar Yadav4, V. Gopi Tilak5
1A. Vijaya Lakshmi, Research Scholar, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
2V. S. Ghali*, Professor, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
3Muzammil Parvez M, Assistant Professor, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
4G. V. P. Chandra Sekhar Yadav, Research Scholar, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
5V. Gopi Tilak, Research Scholar, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India.
Manuscript received on 05 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 4047-4051 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5390098319/2019©BEIESP | DOI: 10.35940/ijrte.C5390.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Defect characterization from its non-defective counterpart from the raw thermal response plays a vital role in Quadratic frequency modulated thermal wave imaging (QFMTWI). The strength of the bone reduces due to the skeletal disorder as the age of the person grows, Early diagnosis corresponding to disease is necessary to provide good bone strength. By detecting bone density variations the disease can be managed effectively. A non-stationary thermal wave imaging method, Quadratic frequency modulated thermal wave imaging (QFMTWI) is used to characterize strictness of the human bone, as well as experimentation also carried on Carbon fiber reinforced polymers (CFRP) sample and are extended to unsupervised machine learning algorithms like k-means clustering and fuzzy c-means clustering algorithms. In case of an observer with less expertise, a perfect unsupervised clustering approach is necessary to fulfill this requirement. In present article, we applied k-means and fuzzy c-means based unsupervised clustering techniques for subsurface defect detection in QFMTWI. The applicability of these algorithms is tested on a numerical simulated biomedical bone sample having various density variations and an experimental Carbon fiber reinforced polymers (CFRP) sample with flat bottom holes of different depths with same size. Signal to noise ratio (SNR) is taken as performance merit and on comparison, we conclude Fuzzy c-means provides better detection and characterization of defects compared to K-means clustering for QFMTWI.
Keywords: Infrared Thermography, Clustering and Quadratic Frequency Modulated Thermal wave Imaging.
Scope of the Article: Fuzzy Logics