# Non-Destructive Infrared Evaluation of Thermo-Physical Parameters in Bamboo Specimens
## 5 Experimental Set-Up
### Thermal Data Acquisition  
The temperature measurements were performed using the experimental platform that is shown in Figure 5. The thermal insulator, 'Isoboard FC' material, has a thermal conductivity of 0.085 W/m\(\cdot\)K. The heat source is a 60 W-electric resistor that we control at 90 \({}^{\circ}\)C using a Proportional-Integral-Derivative (PID) controller and a J type thermocouple. An IR camera FLIR T360 (FLIR Systems, Wilsonville, Oregon, United States) remotely measures the temperature on the convex face of the cylindrical bamboo sample. This camera has an FPA (Focal Plane Array) with size of \(320\times 240\), a field of view of \(25\times 19^{\circ}\), a thermal sensibility of 0.06 \({}^{\circ}\)C, and a spectral range from 7.5 \(\upmu\)m to 13 \(\upmu\)m.  
Figure 6 presents the general scheme of the video acquisition. In the acquisition scheme, the heat source is energized using a variac to reduce the power supplied to the resistance since the controlled response has a high overshoot. Then, we decide to reduce the power from 60 W to 10 W.  
Figure 5: Experimental measurement platform.  
The IR camera records external temperature variations of the sample produced by the heat source. The calibration process transforms image gray levels acquired with the frame-grabber into temperature values that range from 18 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. The transformation function is a fourth degree polynomial that provides an error of 0.2 \({}^{\circ}\)C in the conversion.  
A region of interest (ROI) that contains the bamboo sample is selected from the 720 \(\times\) 480 images provided by the frame grabber. This region is chosen to have its emissivity unaffected by the viewing angle since the camera's optical axis is located to be, to the best of our ability, perpendicular to the selected region. Each experiment lasts one hour, in which the bamboo sample temperature \(T_{cam}\) is taken from one pixel in the ROI and recorded with a sampling frequency of 30 fps. Then, this signal is decimated to take one frame every 5 s and reduce the memory space. This sub-sampled signal (\(T_{cam}\)) along with the resistance temperature (\(T_{res}\)) are the inputs to the inversion algorithm that estimates parameters \(k\) and \(\rho c_{p}\). Figure 7 shows an example of these temperature inputs taken with a emmisivity of 0.96 for the bamboo surface and a reflected temperature of 20 \({}^{\circ}\)C set in the camera.  
Figure 6: Scheme of temperature acquisition.  
The internal temperature of the bamboo sample is measured with a K type thermocouple and a FLUKE 179 multimeter (Fluke Electronics, Everett, Washington, United States). There is a decrease of temperature in the internal side of the bamboo sample, with respect to \(T_{res}\). Therefore, we estimate a bank of eight Infinite Impulse Response (IIR) filters to account for this phenomenon. Matlab's System Identification Toolbox calculates the parameters of each filter \(H(z)\) represented by Equation (9). \(R^{2}\) validates the fitting of each model \(H(z)\), obtaining values that range from 0.96 to 0.99:  
\[H(z)=\frac{b_{0}+b_{1}\cdot z^{-1}}{1+a_{1}\cdot z^{-1}}. \tag{9}\]