This paper introduces soil-moisture parameter retrieval using high-resolution vertically polarized (VV) Spotlight TerraSAR-X data. The soil-moisture estimation of bare and vegetated areas is considered by using volumetric scattering, which is modeled with a bare-soil component and a component reflecting vegetation. The unknown coefficients of the soil-moisture model are estimated using the Tikhonov regularization scheme. A neural network is used in order to distinguish volumetric scattering from all the other types of scattering. The estimated volumetric soil- moisture parameters are further enhanced by using a supervised feedforward backpropagation neural network. The proposed algorithm based on the Tikhonov regularization scheme, in combination with neural networks, provides good results for estimating volumetric-soil-moisture in an area covered with a small vegetation canopy.
COBISS.SI-ID: 16634134
This paper presents the monitoring of wet zones within a hydro-power plantćs canal using Synthetic Aperture Radar (SAR) and Ground Penetrating radar (GPR) data. SAR based monitoring considers soil moisture estimation and moisture detection using Fourier descriptors, whilst GPR based automatic data interpretation uses Fourier descriptors for detecting potential wet-zones within the canal. The second and fourth order spectral moments, cepstrum, spectral entropy and centroid were used for detecting potential wet-areas using SAR and GPR data. The experimental results showed that SAR data can be used for detecting water leakages when the surface is already wet, meanwhile GPR data can be used for determining water-leakage location inside the canal. GPR measurements were taken on the top of the canal, as well as at its sides. The acquired data were analyzed using Fourier descriptors. The Fourier descriptors showed a good detection performance for potential wet areas withinSAR and GPR data.
COBISS.SI-ID: 16578326
This paper presents an improved monitoring system for the failure detection ofen graving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signalsć peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process.
COBISS.SI-ID: 16888854