Formal verification of OIL component specifications using mCRL2

International Journal on Software Tools for Technology Transfer - To aid in making software bug-free, several high-tech companies are moving from coding to modelling. In some cases model checking...     

Measurement of Coarse Gravel and Cobble Transport Using Portable Bedload Traps

Portable bedload traps (0.3 by 0.2 m opening) were developed for sampling coarse bedload transport in mountain gravel-bed rivers during wadable high flows. The 0.9 m long trailing net can capture about 20 kg of gravel and cobbles. Traps are positioned on ground plates anchored in the streambed to minimize disturbance of the streambed during sampling. This design permits sampling times of up to 1 h, overcoming short-term temporal variability issues. Bedload traps were tested in two streams and appear to collect representative samples of gravel bedload transport. Bedload rating and flow competence curves are well-defined and steeper than those obtained by a Helley–Smith sampler. Rating curves from both samplers differ most at low flow but approach each other near bankfull flow. Critical flow determined from bedload traps is similar using the largest grain and the small transport rate method, suggesting suitability of bedload trap data for incipient motion studies.     

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1-25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images.     

Pretreatment of glass substrates by Ar/O2 ion beams for the as-sputtered rough Al doped zinc oxide thin films

Textured TCO surfaces are required in silicon thin film solar cells to gain efficient light trapping. Nowadays, magnetron sputtered ZnO:Al films are usually etched in HCl solution to obtain textured surface. This study introduces a method to achieve as-grown rough ZnO:Al films by ion beam pretreatment of the glass substrate. The reference ZnO:Al films deposited on untreated glass are composed of well aligned columnar grains. In contrast, in the as-grown rough films, additional large conical grains are observed. The large grains exhibit faster growth rate than the surrounding columnar grains, and therefore overgrow the columnar grains gradually and finally cover the whole surface. In order to investigate the ZnO:Al film structural properties, transmission electron microscopy and X-ray diffraction are employed. The crystal orientations of these two types of grains are further analyzed by selected area diffraction patterns. The columns in the as-grown rough ZnO:Al are similarly textured as the reference ZnO:Al film on untreated glass. The c-axis is well aligned nearly perpendicular to the substrate surface, but a tilt of 10° was observed with respect to the growth direction. The large conical grains show no strong out of plane texture and random in plane orientation.     

Silicon-based micro-Fourier spectrometer

A novel Fourier spectrometer based on a partly transparent thin-film detector in combination with a tunable silicon micromachined mirror was developed. The operation principle based on the detection of an intensity profile of a standing-wave by introducing a partly transparent detector in the standing-wave. Varying the position of the mirror results in a phase shift of the standing-wave and thus in a change of the optical intensity profile within the detector. The photoelectric active region of the sensor is thinner than the wavelength of the incoming light, so that the modulation of the intensity leads to the modulation of the photocurrent. The spectral information of the incoming light can be determined by the Fourier transform of the sensor signal. Based on the linear arrangement of the sensor and the mirror, the spectrometer facilitates the realization of one- and two-dimensional arrays of spectrometers combining spectral and spatial resolution. The operation principle of the spectrometer will be described and the influence of the detector design on the spectrometer performance will be discussed. A spectral resolution of down to 6 nm was achieved under real-time imaging conditions.     

基于高迁移率微晶硅的薄膜晶体管

近年来,微晶硅(μc-Si:H)被认为是一种制作 TFT 的有前景的材料.采用PECVD法,在低于200℃时制作了微晶硅TFTs,其制作条件类似于非晶态 TFTs.微晶硅 TFTs 器件的迁移率超过了 30 cm2/Vs,而阈值电压是 2.5 V.在长沟道器件(50～200 μm)中观测到了这种高迁移率.但对于短沟道器件(2 μm),迁移率就降低到了7 cm2/Vs.此外,该 TFTs 的阈值电压随着沟道长度的减少而增大.文章采用了一种简单模型解释了迁移率、阈值电压随着沟道长度的缩短而分别减少、增加的原因在于源漏接触电阻的影响.     

Adaptive Matrices and Filters for Color Texture Classification

In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain, and adaptive filter kernels based on Gabor filters. We evaluate the proposed technique on two sets of color texture images and compare results with those other methods achieve. The features and filter kernels learned by GLVQ improve classification accuracy and they are able to generalize much better for data previously unknown to the system.