Earticle

Novel porous silicon chip exhibiting dual optical properties, both Frbry-Perot fringe (optical reflectivity) and photoluminescence had been developed and used as chemical sensors. Porous silicon samples were prepared by an electrochemical etch of p-type sillicon wafer (boron-doped, <100> orientation, resistivity 1 - 10 Ω). The ething solution was prepared by adding an equal volume of pure ethanol to an aqueous solution of HF (48% by weight). The porous silicon was illuminated with a 300 W tungsten lamp for the duration of etch. Ething was carried out as a two-electrode Kithley 2420 preocedure at an anodic current. The surface of porous silicon was characterized by FT-IR instrument. The porosity of samples was about 80%. Three different types of porous silicon, fresh porous silicon (Si-H termianated), oxidized porous silicon (Si-OH terminated), and surface-derivatized porous silicon (Si-R terminated), were prepared by the thermal oxidation and hydrosilylation. Then the samples were exposed to the wapor of various organics vapors. such as chloroform, hexane, methanol, benzene, isopropanol, and toluene. Both reflectivity and photoluminescence were simultaneously measured under the exposure of organic wapors.

Interesting information and Meta-information about software systems can be extracted by analyzing their evolution histories. This information has been proved useful for understanding software evolution, predicting future changes, and performing an efficient change impact analysis. A rich source code repository is a prerequisite for a high quality evolution analysis. Nonetheless, the evolutionary information contained in current versioning systems for Aspect Oriented (AO) software is incomplete and of low quality, hence limiting the scope of AO software evolution analysis. In spite of AO Programming (AOP) characteristics, none of current versioning tools match the need of controlling and storing the AO software evolution, they do not perform well with obliviousness and quantification found in AO code. In this paper, we suggest a rule-based repository for AO software evolution, and specifically for AspectJ programming language. This repository is dedicated to handle the proper characteristics of AO paradigm. In our proposal changes are formulated as rewriting rules and recorded in the repository when they are applied. Then, this last is analyzed to detect change patterns in AspectJ software evolution. We give here, the details of our rule-based repository, as well as the proposed approach for change pattern detection. We present a tool validation and some experimentation to prove the feasibility and the efficiency of our proposals.

This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.