Material characterisation by enhanced resolution in non-stationary thermal wave imaging

In the recent past, quadratic frequency-modulated thermal wave imaging (QFMTWI) has been advanced with a chirp z-transform (CZT)-based processing approach to facilitate enhanced subsurface anomaly detection, depth quantification and material property estimation with enhanced depth resolution. In the present study, the applicability of CZT-based phase analysis for foreign object defect detection in a structural steel sample using QFMTWI is validated through finite element-based numerical modelling rather than experimental verification due to limited available resources. Furthermore, the enhanced defect detection capability of the CZT phase approach is qualitatively compared with the frequency- and time-domain phase approaches using the defect signal-to-noise ratio (SNR) as a quality metric. Also, an empirical relationship between the observed phases and the thermal reflection coefficient is obtained, which recommends the CZT phase as a prominent approach for foreign material defect detection.

Thermal Wave Imaging for Detection of Osteoporosis

Osteoporosis is a clinical sickness wherein the bones end up brittle and volatile because of tissue loss, which is usually caused by hormonal changes or a calcium or vitamin D deficiency. Osteoporosis has neither clinical signs nor symptoms, until some fracture occur. The aim of our project is to predict bone brittleness in order to detect osteoporosis using Image processing techniques. The objective measurement of bone mineral density (BMD), is presently accepted as the best indicator of osteoporosis fractures. For measuring and assessing biomaterials, thermal wave imaging is a potential , non-invasive, non-contact and safe imaging method.. Thermal wave imaging has the unique ability to measure physiological changes. The thermal images of bone are taken and removal of noise is carried out and undergone stationary wavelets transform process to improve the resolution of edges. The result shows that Artificial Neural Network is capable of predicting the brittleness of the bone using Regression in Machine Learning.