ABSTRACT
A general theory of a photopyroelectric (PPE) configuration, based on an opaque sample and transparent pyroelectric sensor, backing and coupling fluids is developed. A combined back-front detection investigation, based on a frequency scan of the phase of the PPE signals, followed by a self-normalization of the phases' behavior, leads to the possibility of simultaneously measuring both thermal effusivity and diffusivity of a solid sample. A particular case of this configuration, with no coupling fluid at the sample/backing interface and air instead of coupling fluid at the sample/sensor interface (non-contact method) is suitable for simultaneous measurement ofboth thermal diffusivity and effusivity (in fact complete thermal characterization) of porous solids. Compared with the already proposed configurations for investigations of porous materials, this novel configuration makes use of a fitting procedure with only one fitting parameter, in order to guarantee the uniqueness of the solution. The porous solids belong to a class of materials which are by far not easy to be investigated using PPE. To the best of our knowledge, porous materials represent the only type of compounds, belonging to condensed matter, which were not taken into consideration (until recently) as potential samples for PPE calorimetric investigations. Consequently, the method proposed in this paper complete the area of applications of the PPE method. Applications on some porous building materials and cellulose-based samples validate the theory.
ABSTRACT
A new photopyroelectric detection configuration is proposed in order to measure the thermal effusivity of porous solids. Compared with the previously reported detection scheme this configuration makes use of a transparent window in front of the pyroelectric sensor. In such a way, the heat losses by convection at the sensor's irradiated surface are eliminated, and consequently, the conduction remains the only process responsible for the heat propagation in the whole detection cell. In the paper, the mathematical model for this new configuration is developed, with the main conclusion that the sample's thermal effusivity can be finally obtained via a fitting procedure with only two fitting parameters (instead of three as previously reported); in such a way, the possible degeneracy of the results is eliminated. The suitability of the method is demonstrated with application on some porous building materials and cellulose-based pressed powders.
ABSTRACT
Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones.
