Investigations on integration of optoelectronic components with LTCC (low temperature co-fired ceramics) microfluidic module are presented. Design, fabrication and characterization of the ceramic structure for optical absorbance is described as well. The geometry of the microfluidic channels has been designed according to results of the CFD (computational fluid dynamics) analysis. A fabricated LTCC-based microfluidic module consists of an U-shaped microchannel, two optical fibers and integrated light source (light emitting diode) and photodetector (light-to-voltage converter). Properties of the fabricated microfluidic system have been investigated experimentally. Several concentrations of potassium permanganate (KMnO4) in water were used for absorbance/transmittance measurements. The test has shown a linear detection range for various concentrations of heavy metal ions in distilled water. The fabricated microfluidic structure is found to be a very useful system in chemical analysis.

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.