Nanoelectromechanical DRAM for ultra-large-scale integration (ULSI)

A Nanoelectromechanical (NEM) device developed for dynamic random access memory (DRAM) is reported. A vertical nanotube structure is employed to form the electromechanical switch and capacitor structure. The mechanical movement of the nanotube defines 'On' and 'OFF' states and the electrical signals which result lead to charge storage in a vertical capacitor structure as in a traditional DRAM. The vertical structure contributes greatly to a decrease in cell dimension. The main concept of the NEM switch and capacitor can be applied to other memory devices as well. © 2005 IEEE.

Observation of large-scale features on graphite by scanning tunnelling microscopy

Superlattice structures and rippling fringes were imaged on two separate pieces of graphite (HOPG) by scanning tunnelling microscopy (STM). We observed the corrugation conservation phenomenon on one of the superlattice structures where an overlayer does not attenuate the corrugation amplitude of the superlattice. Such a phenomenon may illustrate an implication that nanoscale defects a few layers underneath the surface may propagate through many layers without decay and form the superlattice structure on the topmost surface. Some rippling fringes with periodicities of 20 nm and 30 nm and corrugations of 0.1 nm and 0.15nm were observed in the superlattice area and in nearby regions. Such fringes are believed to be due to physical buckling of the surface. The stress required to generate such structures is estimated, and a possible cause is discussed. An equation relating the attenuation factor to the number of overlayers is proposed. © 2005 The Japan Society of Applied Physics.

Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.