Direct Write Fabrication of Waveguides and Interconnects for Optical Printed Wiring Boards

Current copper based circuit technology is becoming a limiting factor in high speed data transfer applications as processors are improving at a faster rate than are developments to increase on board data transfer. One solution is to utilize optical waveguide technology to overcome these bandwidth and loss restrictions. The use of this technology virtually eliminates the heat and cross-talk loss seen in copper circuitry, while also operating at a higher bandwidth. Transitioning current fabrication techniques from small scale laboratory environments to large scale manufacturing presents significant challenges. Optical-to-electrical connections and out-of-plane coupling are significant hurdles in the advancement of optical interconnects. The main goals of this research are the development of direct write material deposition and patterning tools for the fabrication of waveguide systems on large substrates, and the development of out-of-plane coupler components compatible with standard fiber optic cabling. Combining these elements with standard printed circuit boards allows for the fabrication of fully functional optical-electrical-printed-wiring-boards (OEPWBs). A direct dispense tool was designed, assembled, and characterized for the repeatable dispensing of blanket waveguide layers over a range of thicknesses (25-225 µm), eliminating waste material and affording the ability to utilize large substrates. This tool was used to directly dispense multimode waveguide cores which required no UV definition or development. These cores had circular cross sections and were comparable in optical performance to lithographically fabricated square waveguides. Laser direct writing is a non-contact process that allows for the dynamic UV patterning of waveguide material on large substrates, eliminating the need for high resolution masks. A laser direct write tool was designed, assembled, and characterized for direct write patterning waveguides that were comparable in quality to those produced using standard lithographic practices (0.047 dB/cm loss for laser written waveguides compared to 0.043 dB/cm for lithographic waveguides). Straight waveguides, and waveguide turns were patterned at multimode and single mode sizes, and the process was characterized and documented. Support structures such as angled reflectors and vertical posts were produced, showing the versatility of the laser direct write tool. Commercially available components were implanted into the optical layer for out-of-plane routing of the optical signals. These devices featured spherical lenses on the input and output sides of a total internal reflection (TIR) mirror, as well as alignment pins compatible with standard MT design. Fully functional OEPWBs were fabricated featuring input and output out-of-plane optical signal routing with total optical losses not exceeding 10 dB. These prototypes survived thermal cycling (-40°C to 85°C) and humidity exposure (95±4% humidity), showing minimal degradation in optical performance. Operational failure occurred after environmental aging life testing at 110°C for 216 hours.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.