5.4-MHz single-crystal silicon wine glass mode disk resonator with quality factor of 2 million

This paper reports the design and electrical characterization of a micromechanical disk resonator fabricated in single crystal silicon using a foundry SOI micromachining process. The microresonator has been selectively excited in the radial extensional and the wine glass modes by reversing the polarity of the DC bias voltage applied on selected drive electrodes around the resonant structure. The quality factor of the resonator vibrating in the radial contour mode was 8000 at a resonant frequency of 6.34 MHz at pressure below 10 mTorr vacuum. The highest measured quality factor of the resonator in the wine glass resonant mode was 1.9 × 106 using a DC bias voltage of 20 V at about the same pressure in vacuum; the resonant frequency was 5.43 MHz and the lowest motional resistance measured was approximately 17 kΩ using a DC bias voltage of 60 V applied across 2.7 μm actuation gaps. This corresponds to a resonant frequency-quality factor (f-Q) product of 1.02 × 1013, among the highest reported for single crystal silicon microresonators, and on par with the best quartz crystal resonators. The quality factor for the wine glass mode in air was approximately 10,000. © 2009 Elsevier B.V. All rights reserved.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Shape Recognition of Linear Construction Entities from Construction Site Images

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

Electron spin resonance signature of the oxygen vacancy in HfO 2

The oxygen vacancy has been inferred to be the critical defect in HfO 2, responsible for charge trapping, gate threshold voltage instability, and Fermi level pinning for high work function gates, but it has never been conclusively identified. Here, the electron spin resonance g tensor parameters of the oxygen vacancy are calculated, using methods that do not over-estimate the delocalization of the defect wave function, to be g xx = 1.918, g yy = 1.926, g zz = 1.944, and are consistent with an observed spectrum. The defect undergoes a symmetry lowering polaron distortion to be localized mainly on a single adjacent Hf ion. © 2012 American Institute of Physics.

DIRECT DETECTION OF PICOSECOND PULSES EMITTED BY AN INJECTION LASER WITH ACTIVE MODE LOCKING.

An Agat-SF linear-scan streak image-converter camera was used to record output pulses of 2. 7 psec duration generated by an injection laser with an external dispersive resonator operated in the active mode-locking regime. The duration of the pulses was determined by the reciprocal of the spectral width and the product of the duration and the spectral width was 0. 30.

Initial investigation into the use of GGBS-MgO in soil stabilisation

Portland cement (PC) is the most widely used binder for ground improvement. However, there are significant environmental impacts associated with its production in terms of high energy consumption and CO2 emissions. Hence, the use of industrial by-products materials or new low-carbon footprint alternative cements has been encouraged. Ground granulated blastfurnace slag (GGBS), a by-product of the steel industry, has been successfully used for such an application, usually activated with an alkali such as lime or PC. In this study the use of MgO as a novel activator for GGBS in ground improvement of soft soils is addressed and its performance was compared to the above two conventional activators as well as PC alone. The GGBS:activator ratio used in this study was 9:1. A range of tests was performed at three curing periods (7, 28 and 90 days), including unconfined compressive strength (UCS), permeability and microstructure analysis. The results show that the MgO performed as the most efficient activator yielding the highest strength and the lowest permeability indicating a very high stabilisation efficiency of the system. © 2012 American Society of Civil Engineers.