Entropy-Based Approach to Analyze and Classify Mineral Aggregates

This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

Comparison of the analytical performance of electrophoresis microchannels fabricated in PDMS, glass, and polyester-toner

This paper compares the analytical performance of microchannels fabricated in PDMS, glass, and polyester-toner for electrophoretic separations. Glass and PDMS chips were fabricated using well-established photolithographic and replica-molding procedures, respectively. PDMS channels were sealed against three different types of materials: native PDMS, plasma-oxidized PDMS, and glass. Polyester-toner chips were micromachined by a direct-printing process using an office laser printer. All microchannels were fabricated with similar dimensions according to the limitations of the direct-printing process (width/depth 150 mu m/12 mu m). LIF was employed for detection to rule out any losses in separation efficiency due to the detector configuration. Two fluorescent dyes, coumarin and fluorescein, were used as model analytes. Devices were evaluated for the following parameters related to electrophoretic separations: EOF, heat dissipation, injection reproducibility, separation efficiency, and adsorption to channel wall.