Infrared thermography is a nondestructive evaluation technique in which the specimen surface is thermally stimulated to produce a temperature difference between "sound" (free of defects) areas and eventual defective regions. It is well known that the thermographic methods based on the thermal contrast are strongly affected by non-uniform heating at the surface. Hence, thermal contrast-based results considerably depend on the chosen reference point. The differential absolute contrast (DAC) method was developed to eliminate the need of determining a reference point by defining the thermal contrast with respect to an "ideal" sound area. The DAC technique is based on the 1D solution of the Fourier diffusion equation for homogeneous and semi-infinite materials stimulated with a Dirac heat pulse. Although very useful at early times, this assumption considerably decreases DAC accuracy when the heat front approaches the sample rear face. We propose a modified DAC version by explicitly introducing the sample thickness using the thermal quadrupoles theory. We demonstrate that taking into account the sample thickness, the DAC validity range considerably extends for long times after excitation while preserving its performance for short times.
Thermographic Signal Reconstruction (TSR) is a processing technique in Themography for Nondestructive Testing (TNDT). It is based on a least square fit of a low order polynomial to the logarithmic time evolution of experimental data. Even though TSR allows the reduction of data for processing and the filtering of high frequency noise, the resulting TSR polynomial coefficients lack of physical meaning to provide quantitative results and further processing is required in order to characterize internal defects. We propose to use Artificial Neural Networks (ANN) as a tool to map between TSR coefficients and defect depths. This paper presents the application of ANN and TSR coefficients as learning and validation data sets to characterize defects in composite materials.
Disorders associated with repeated trauma account for about 60% of all occupational illnesses, being Carpal Tunnel Syndrome (CTS), the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine, where diseases are detected by temperature variations. In this paper we present the extraction of temperature variations in space and time from hands of healthy and ill subjects with CTS. These features are expected to give support to the analysis of CTS using IT. The paper presents a theoretical framework of CTS, a protocol to acquire infrared images and create a standardized collection. The techniques used for image registration and feature extraction are included. Finally, the results obtained with representative features in images of healthy subjects are presented and compared against features extracted from patientss with CTS.
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