Fiber Bragg Gratings Strain Sensors for Railway Applications

Fiber optical sensors have played an important role in applications for monitoring the health of civil infrastructures, such as bridges, oil rigs, and railroads. Due to the reduction in cost of fiber-optic components and systems, fiber optical sensors have been studied extensively for their higher sensitivity, precision and immunity to electrical interference compared to their electrical counterparts. A fiber Bragg grating (FBG) strain sensor has been employed for this study to detect and distinguish normal and lateral loads on rail tracks. A theoretical analysis of the relationship between strain and displacement under vertical and horizontal strains on an aluminum beam has been performed, and the results are in excellent agreement with the measured strain data. Then a single FBG sensor system with erbium-doped fiber amplifier broadband source has been carried out. Force and temperature applied on the system have resulted in changes of 0.05 nm per 50 με and 0.094 nm per 10 oC at the center wavelength of the FBG. Furthermore, a low cost fiber-optic sensor system with a distributed feedback (DFB) laser as the light source has been implemented. We show that it has superior noise and sensitivity performances compared to strain gauge sensors. The design has been extended to accommodate multiple sensors with negligible cross talk. When two cascaded sensors on a rail track section are tested, strain readings of the sensor 20 inches away from the position of applied force decay to one seventh of the data of the sensor at the applied force location. The two FBG sensor systems can detect 1 ton of vertical load with a square wave pattern and 0.1 ton of lateral loads (3 tons and 0.5 ton, respectively, for strain gauges). Moreover, a single FBG sensor has been found capable of detecting and distinguishing lateral and normal strains applied at different frequencies. FBG sensors are promising alternatives to electrical sensors for their high sensitivity,ease of installation, and immunity to electromagnetic interferences.

Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.