Very high modulation efficiency two-sections tapered laser diode at 1060nm for free space optical communications

High-power (more than 500 mW) and high-speed (more than 1 Gbps) tapered lasers at 1060 nm are required in free-space optical communications and (at lower frequencies of around 100 MHz) display applications for frequency doubling to the green. On a 3 mm long tapered laser, we have obtained an open eye diagram at 1 Gbps, together with a high extinction ratio of 11 dB, an optical modulation amplitude of 530 mW, and a high modulation efficiency of 13 W/A. On a 4 mm-long tapered laser, we have obtained an open eye diagram at 700 Mbps, together with a high extinction ratio of 19 dB, a high optical modulation amplitude of 1.6 W, and a very high modulation efficiency of 19 W/A. On a 6 mm-long tapered laser, we have obtained a very high power of 5W CW and a very high static modulation efficiency of 59.8 W/A. © 2011 SPIE.

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.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.