Demonstration of 2.488Gb/s simultaneous all-optical wavelength conversion and tone reading/replacing in an integrated-SOA/DFB laser

A technique is demonstrated that allows for the wavelength conversion of data with both simultaneous monitoring and replacing of a wavelength identifying pilot tone. The technique should be upgradable to data rates of 10Gb/s and higher.

Automated meshing for aero-thermal analysis of complex automotive geometries

Successful product development, especially in motorsport, increasingly depends not just on the ability to simulate aero-thermal behavior of complex geometrical configurations, but also the ability to automate these simulations within a workflow and perform as many simulations as possible within constrained time frames. The core of these aero-thermal simulations - and usually the main bottleneck - is generating the computational mesh. This paper describes recent work aimed at developing a mesh generator which can reliably produce meshes for geometries of essentially arbitrary complexity in an automated manner and fast enough to keep up with the pace of an engineering development program. Our goal is to be able to script the mesh generation within an automated workflow - and forget it. © 2011 SAE International.

Portable electronic warp load meter

A portable type warp load meter has been developed for the use in fishing trawlers. The instrument enables to monitor the warp load in fishing trawlers accurately and easily without disturbing the routine fishing operations. The instrument can be used in several other places like cranes, bollard tests for marine engines, dry docks etc. especially when the operation has to be conducted easily without disturbing the load system. The information displayed in micro ammeter in the range 0 to 1000 kg can be fed to continuous recorders for detailed analysis and permanent records.

In situ temperature and salinity meter

Development of a portable self-contained electronic meter for on the spot determination of temperature and salinity is described. Instant and remote measurements of temperature and salinity of sea and estuarine waters in the range of 25-30°C and 30-35°C for temperature with an accuracy ± 0.05°C and 0-37‰ and 31-37‰ for salinity with an accuracy of ± 0.2‰ and ± 0.05‰ respectively are possible with the instrument. The temperature compensations of the salinity measurements are done manually with the help of temperature charts. The temperature and salinity measurements can be fed to continuous recorders.

Development of a torque meter for fishing vessels

The paper describes the development of an electronic instrument to measure the torque developed on the propeller shaft of fishing vessels at various speeds of the propeller. By measuring the torque, it is possible to determine the actual power transmitted from the engine gear-box unit to the propeller so that propeller efficiency can be evaluated and the optimum size of the propeller for a specific engine and vessel can be determined.

Different protein tyrosine phosphatase superfamilies resulting from different gene reading frames

Protein tyrosine phosphatases (PTPs) are comprised of two superfamilies, the phosphatase I superfamily containing a single low-molecular-weight PTP (lmwPTP) family and the phosphatase II superfamily including both the higher-molecular-weight PTP (hmwPTP) and the dual-specificity phosphatase (DSP) families. The phosphatase I and H superfamilies are often considered to be the result of convergent evolution. The PTP sequence and structure analyses indicate that lmwPTPs, hmwPTPs, and DSPs share similar structures, functions, and a common signature motif, although they have low sequence identities and a different order of active sites in sequence or a circular permutation. The results of this work suggest that lmwPTPs and hmwPTPs/DSPs are remotely related in evolution. The earliest ancestral gene of PTPs could be from a short fragment containing about 90similar to120 nucleotides or 30similar to40 residues; however, a probable full PTP ancestral gene contained one transcript unit with two lmwPTP genes. All three PTP families may have resulted from a common ancestral gene by a series of duplications, fusions, and circular permutations. The circular permutation in PTPs is caused by a reading frame difference, which is similar to that in DNA methyltransferases. Nevertheless, the evolutionary mechanism of circular permutation in PTP genes seems to be more complicated than that in DNA methyltransferase genes. Both mechanisms in PTPs and DNA methyltransferases can be used to explain how some protein families and superfamilies came to be formed by circular permutations during molecular evolution.

Towards robust part tracking in an automated manufacturing process using RFID

Automated Identification and in particular, Radio Frequency Identification (RFID) promises to assist with the automation of mass customised production processes. RFID has long been used to gather a history or trace of part movements, but the use of it as an integral part of the control process is yet to be fully exploited. Such use places stringent demands on the quality of the sensor data and the method used to interpret that data. in particular, this paper focuses on the issue of correctly identifying, tracking and dealing with aggregated objects with the use of RFID. The presented approach is evaluated in the context of a laboratory manufacturing system that produces customised gift boxes. Copyright © 2005 IFAC.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

Automated Computation of the Fundamental Matrix for Vision-Based Construction Site Applications

Estimating the fundamental matrix (F), to determine the epipolar geometry between a pair of images or video frames, is a basic step for a wide variety of vision-based functions used in construction operations, such as camera-pair calibration, automatic progress monitoring, and 3D reconstruction. Currently, robust methods (e.g., SIFT + normalized eight-point algorithm + RANSAC) are widely used in the construction community for this purpose. Although they can provide acceptable accuracy, the significant amount of required computational time impedes their adoption in real-time applications, especially video data analysis with many frames per second. Aiming to overcome this limitation, this paper presents and evaluates the accuracy of a solution to find F by combining the use of two speedy and consistent methods: SURF for the selection of a robust set of point correspondences and the normalized eight-point algorithm. This solution is tested extensively on construction site image pairs including changes in viewpoint, scale, illumination, rotation, and moving objects. The results demonstrate that this method can be used for real-time applications (5 image pairs per second with the resolution of 640 × 480) involving scenes of the built environment.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Automated vision tracking of project related entities

Tracking of project related entities such as construction equipment, materials, and personnel is used to calculate productivity, detect travel path conflicts, enhance the safety on the site, and monitor the project. Radio frequency tracking technologies (Wi-Fi, RFID, UWB) and GPS are commonly used for this purpose. However, on large-scale sites, deploying, maintaining and removing such systems can be costly and time-consuming. In addition, privacy issues with personnel tracking often limits the usability of these technologies on construction sites. This paper presents a vision based tracking framework that holds promise to address these limitations. The framework uses videos from a set of two or more static cameras placed on construction sites. In each camera view, the framework identifies and tracks construction entities providing 2D image coordinates across frames. Combining the 2D coordinates based on the installed camera system (the distance between the cameras and the view angles of them), 3D coordinates are calculated at each frame. The results of each step are presented to illustrate the feasibility of the framework.

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.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.